Decision-making Model to Generate Novel Emergency Response Plans. for Improving Coordination during Large-scale Emergencies

Transcription

1 Ths s the Pre-Publshed Verson. Tang P., Shen G.Q.P. (2015). Decson-makng Model to Generate Novel Emergency Response Plans for Improvng Coordnaton durng Large-scale Emergences, Knowledge-based Systems, Vol 90, Pages , DOI: /.knosys , December. (SCI, 5-Year mpact factor: 3.433, Ranked 17/130 n Computer Scence, Artfcal Intellgence by JCR n 2015). Decson-makng Model to Generate Novel Emergency Response Plans for Improvng Coordnaton durng Large-scale Emergences Pan Tang a, b,, Geoffrey Qpng Shen b a School of Publc Management/ Emergency Management, Research Center of Emergency Management, Jnan Unversty, Guangzhou, Chna b Department of Buldng and Real Estate, The Hong Kong Polytechnc Unversty, Hung Hom, Kowloon, Hong Kong Abstract Developng ont emergency response plans s an effectve method to coordnate mult-agency response endeavors. Ths study presents a novel emergency response plan structure that consders emergency command operaton requrements, such as explctly expressng the ncdent obectve decomposton structure, formalzng decsons n a context-senstve manner, supportng the synchronzaton of respondng actvtes wth varable nterval, encodng complex temporal constrants, and provdng temporal flexblty. A decson-makng model s developed to generate these doman-specfc acton plans automatcally based on ntegratng herarchcal task network (HTN) plannng and schedulng technologes. Ths model presents several valuable contrbutons to exstng state-based forwardng HTN plannng paradgms. Frst, an enhanced HTN s desgned to record traversed HTN exploraton space for constructng of ncdent obectve decomposton structure and decson-makng contexts. Second, the model generates temporal flexble acton plans that enable the handlng of temporal uncertanty n the emergency response doman. A novel concurrency controllng mechansm to ensure the parallelsm of response actvtes wth varable ntervals s also proposed based on the temporally enhanced plannng state that represents a dynamc emergency stuaton. Fnally, the proposed model explctly represents the startng and endng tme of all tasks n the task network to provde complete temporal flexblty. In partcular, a dedcated temporal management method takng full advantage of the decomposton structure nduced by the HTN plannng process s proposed for propagatng tme constrants on the underlng Smple Temporal Network (STN) ncrementally. An emprcal study on typhoon evacuaton demonstrates that the presented model s sutable for solvng real-world problems. Therefore, the decson-makng model can be appled as a computatonal model for the development process of emergency response plans, and wll be embedded as a reasonng logc n an emergency command decson support system. Keywords Emergency command, Emergency response plan, Herarchcal task network plannng, Tme propagaton 1. Introducton Large-scale emergences constantly result n dsastrous consequences. The emergency response process s beyond specfc organzatonal boundares and nvolves efforts from varous functonal departments, such as the polce and fre departments, medcal corps, mltary, cvl organzatons, and multple ursdctons [1]. Respondng organzatons focus on ndvdual efforts, and may be unaware of the response actvtes taken by others. The allocaton of resources to certan tasks may result n these resources becomng unavalable for other tasks. Therefore, dsastrous stuatons present complex nterdependences and conflcts among the response tasks. Ths case s complcated by varous factors, such as hgh uncertanty, consderable tme pressure and urgency, severe resource shortage, and mult-authorty and massve personnel nvolvement [2]. Effectve coordnaton s an essental element of emergency response management. The crux for coordnaton n emergency response s that Correspondng author. E-mal address: ptanghust2010@gmal.com 2015 Ths manuscrpt verson s made avalable under the CC-BY-NC-ND 4.0 lcense

2 Tang P., Shen G.Q.P. (2015). Decson-makng Model to Generate Novel Emergency Response Plans for Improvng Coordnaton durng Large-scale Emergences, Knowledge-based Systems, Vol 90, Pages , DOI: /.knosys , December. (SCI, 5-Year mpact factor: 3.433, Ranked 17/130 n Computer Scence, Artfcal Intellgence by JCR n 2015). precse actons and responsbltes of respondng organzatons cannot be pre-defned [3]. Exstng lterature confrms that effectve coordnaton durng large-scale emergences requres the development of unfed acton plans, whch handles the rapdly changng dynamcs of an emergency envronment, to enable a coherent response process [4, 5, 6, 7]. Emergency command nvolves plannng, drectng, and controllng operatons to acheve the dentfed ncdent obectves and coordnate the response actvtes, as well as reles heavly on the unfed acton plans, called emergency response plans, to adapt to the dynamc emergency stuaton [8]. These plans descrbe the solutons to a current emergency stuaton to acheve a set of dentfed ncdent obectves usng lmted avalable resources. These plans also defne a course of response tasks and assocated constrants to gude all partcpatng organzatons. Plannng for the response process wll enable arrangement of the heterogeneous tasks mplemented by multple organzatons, reduce repettve work and conflcts between respondng operatons, and mprove the effcent use of emergency resources. The unqueness of a dsastrous stuaton requres that emergency response plans be desgned from scratch [9]. Therefore, the process of developng emergency response plans to coordnate and control the response actvtes s a crtcal and complex decson problem because emergency stuatons are complex, and ncdent obectves nteract wth each other. Decson-makng process nvolves a sequence of decson ponts to select the approprate methods to acheve response tasks under a hghly volatle emergency response envronment [10]. Consequently, developng emergency response plans requres delberaton mechansms. Hgh-level reasonng and task plannng are also essental and often computatonally expensve [11]. Therefore, plannng for an emergency response process s clearly a challenge to the decson-makng capablty of emergency managers. Cogntve-level decson support s requred [12]. Ths dea s the motvaton for conductng the current study that ams to desgn a decson-makng model to generate emergency response plans, thereby reducng the decson load of emergency managers durng large-scale emergences. Methods and computatonal models to provde the aforementoned decson support should be based on an understandng of the cogntve-level process nvolved [13], as well as account for the characterstcs of emergency command operaton. These plans partcularly am to provde gudance for drectng and controllng response actvtes. Plannng for emergency response necesstates a set of requrements for the development process of the plan, and poses a challenge to the decson support tools that assst emergency managers. Frst, the nformaton generated durng the development process should be recorded to track and descrbe the structure of the ncdent obectve decomposton. Second, the dynamc emergency stuatons may nvaldate the executon of the emergency response plans; such stuatons requre that the plan descrbe condtons representng the decson contexts based on whch the actual respondng efforts are montored [14]. Thrd, the precse startng and endng tme of response actvtes cannot be determned when plannng for response operatons because of the uncertanty of the emergency response envronment. Instead, these plans should be temporally flexble and support parallel actons performed by multple geographcally dspersed task forces. The deadlne of the ncdent obectves and other complex temporal constrants between response tasks should also be handled effectvely. Herarchcal task network (HTN) [15, 16] plannng s an artfcal ntellgence plannng technque used to search for a soluton to acheve an ntal task network as obectves n the ntal state. The plannng process proceeds by contnuously decomposng tasks untl all compound tasks n the task net are decomposed. Compared to classcal plannng paradgms, HTN plannng s effectve and scalabe due to that t takes structured doman knowledge to gude search process [17]. HTN s recognzed for provdng a partcularly sutable decson support technque to formulate emergency response plans because t demonstrates several advantages. Frst, the problem-solvng mechansm of HTN plannng s smlar to the underlyng decson logc of plannng for emergency response [17]. Second, HTN plannng enables encodng of doman knowledge at dfferent levels of abstracton n

3 Tang P., Shen G.Q.P. (2015). Decson-makng Model to Generate Novel Emergency Response Plans for Improvng Coordnaton durng Large-scale Emergences, Knowledge-based Systems, Vol 90, Pages , DOI: /.knosys , December. (SCI, 5-Year mpact factor: 3.433, Ranked 17/130 n Computer Scence, Artfcal Intellgence by JCR n 2015). the emergency response doman. Fnally, HTN plannng asssts n a better nterpretaton and understandng of decson ponts wth dfferent stuatonal contexts and constrants. To date, several HTN plannng paradgms have been desgned and appled to the formulaton of emergency response plans. The state-based forward HTN planner called smple herarchcal ordered planner 2 (SHOP2) [18] and ts predecessor, SHOP [19], are appled to assst mltary commanders n plannng evacuaton actons [20], 21]. SIADEX s another state-based forward HTN planner [22, 23] that acheves effcent and expressve handlng of tme and s used for the asssted desgn of forest fre fghtng plans [24, 25]. Bundo proposed a hybrd plannng paradgm that ntegrates HTN and partal-order causal-lnk (POCL) plannng to provde flexble support to flood crss management [26, 27].The doman-ndependent HTN plannng archtecture called Open Plannng Archtecture 2 (O-Plan2) [28] has been appled n the dsaster relef doman to generate a course of acton [29]. Another doman ndependent HTN planner, called System for Integrated Plannng and Executon (SIPE-2) [30], has been appled to ol splls [31] and ont mltary operatons plannng [32]. Fnally, the HTN plannng paradgm XePlanner provde a hghly expressve representaton of doman knowledge to descrbe response tasks and doman-specfc constrants accurately, and has been used to provde support for emergency managers n developng ncdent acton plans durng flood controllng [33]. In addton, case-based reasonng (CBR) approaches were appled to gve recommendatons to support emergency decson makers based on knowledge from prevous dsaster events. Amalef proposed an ontology-supported case-based reasonng (OS-CBR) approach by ntegratng ontology and case based reasonng to provde solutons to emergency managers [34]. The ontology structure s appled for real-tme nformaton extracton and case representaton, whch supports the case-based reasonng process for generatng solutons. Comparng to HTN plannng paradgm, CBR reles heavly on the past response cases and cannot handle complex dsastrous stuatons that have no experences before. However, the aforementoned exstng HTN plannng paradgms don t take nto account for all the requrements of emergency command operaton. Frst, all avalable HTN planners, except for SIADEX, provde rgd representaton of tme arrangement of actons, and cannot generate temporal acton plans where tme arrangement s unknown. Second, the obectve decomposton structure and executon condtons are not represented explctly. Consequently, these plans cannot provde support for montorng executon process to control the response actvtes n all exstng HTN plannng paradgms. Several other defcences exst, whch hnder the applcaton from supportng the development process of emergency response plans. For example, temporal HTN planners, such as SIDEX, O-Plan and XePlanner, adopt PC-2 or PC-CL-2 to check the temporal consstency of the task network. However, plannng tme ncreases consderably as the number of tasks scale up. The achevement of an effcent method for handlng temporal constrants remans a pendng task for most temporal HTN planners. Ths method prevents them from beng appled to solve real-world problems. These gaps are the mpetus of the present study to desgn and develop a decson-makng model based on HTN plannng and schedulng technologes. Therefore, the ultmate obectve of ths study s to embed ths model as a decson logc n an emergency decson support system. In ths study, a decson-makng model that ams to mprove coordnaton n large-scale emergency response s desgned and developed based on state-based forward HTN plannng and schedulng technologes. The obectve s to generate temporal herarchcal emergency response plans that take nto account requrements of the emergency command operaton. In the followng paper, the challenges nvolved n plannng for an emergency response are analyzed, and a novel structure of emergency response plans satsfyng all the requrements s proposed. Wth the obectve of supportng the emergency command operaton, ths new plan structure has several doman-specfc characterstcs, such as expressng the obectve decomposton structure, recordng all decson nodes and ther contexts to montor the executon process of the plan, handlng nterdependences and synchronzaton of the

4 Tang P., Shen G.Q.P. (2015). Decson-makng Model to Generate Novel Emergency Response Plans for Improvng Coordnaton durng Large-scale Emergences, Knowledge-based Systems, Vol 90, Pages , DOI: /.knosys , December. (SCI, 5-Year mpact factor: 3.433, Ranked 17/130 n Computer Scence, Artfcal Intellgence by JCR n 2015). response actvtes wth varable ntervals, and provdng temporal flexblty to adapt to the uncertanty and dynamc nature of the response process. The proposed decson-makng model presents several valuable extensons n addton to exstng HTN plannng paradgms to provde better decson support to the emergency command operaton and to generate the emergency response plans of ths novel structure. Frst, an enhanced HTN s desgned to record the HTN exploraton space, n whch the obectve decomposton structure and executon montorng condtons are extracted. Second, a new concurrent controllng mechansm based on temporally enhanced plannng state and controllng rules s proposed to ensure parallelsm of the response actvtes wth the varable ntervals. Fnally, ths plan structure allows handlng of extensve temporal knowledge, such as temporal causal dependences, deadlnes, temporal landmarks or synchronzaton schema, and represents the startng and endng tmes of all the tasks explctly n the herarchcal task network to provde temporal flexblty by ntegratng the Smple Temporal Network (STN) [35] wth the task network. STN s used extensvely to encode and reason quanttatve temporal constrants over varables. In partcular, a dedcated STN solver takng full advantage of the task decomposton structure nduced by the HTN plannng process s proposed and embedded n the decson-makng model to propagate generated tme constrants on the underlyng STN ncrementally. Ths paper s organzed as follows. Secton 2 ntroduces a novel structure for an emergency response plan that accounts for the emergency command operaton requrements. Secton 3 ntroduces the knowledge formalsm of the decson-makng model. Secton 4 presents the plannng algorthm. Secton 5 lsts an ncremental temporal management method embedded n the proposed model. Secton 6 presents the method for extractng the emergency response plans. Secton 7 ntroduces an emprcal study n a typhoon evacuaton doman, as well as the relevant expermental results. Secton 8 dscusses the related work on HTN plannng and temporal constrants propagaton. Fnally, secton 9 concludes ths study wth a dscusson of ts contrbutons and future work. 2. Extended emergency response plan The endurng characterstcs of emergency command operaton propose requrements for an acton plan to gude and drect respondng task forces durng emergences. These requrements should be consdered when plannng for emergency response to mprove coordnaton. In tradtonal plannng paradgms, the produced acton plans are a seres of actons that are unsutable to support the emergency command [36]. Ths secton ntroduces the plannng process for emergency response durng an emergency command operaton and proposes a number of requrements of emergency response plans. Consequently, a novel emergency response plan structure that consders all the requrements s ntroduced, as well as gudes n the desgn of the decson-makng model based on the ntegraton of the HTN plannng and schedulng technologes. 2.1 Plannng for emergency response Emergency response management s an example of a complex and uncertan work doman [37]. The scale of an emergency s beyond the preparaton of any responsble organzaton [38]. An mportant element n an effectve rapd-response effort s to develop emergency response plans quckly, coordnate efforts among multple agences, and gude responders by allowng them to mprovse plans formulated on ste based on the local emergency stuaton and the prepared standard operaton procedures. Plannng s the glue that ntegrates emergency servces and resources not only wthn but also across organzatons [39]. An emergency response plan need to be very specfc about who s gong to do what, what resource wll be need, where exactly wll they be workng, and who s the pont of contact for partcular tasks [40]. Determnng the precse actons and responsbltes of the nvolved responders s the crux of the coordnaton problem. Once large-scale emergences break out and are reported to the emergency operaton center (EOC), emergency managers wll collect and mantan nformaton on the current and forecast stuatons, as well as the resources

5 Tang P., Shen G.Q.P. (2015). Decson-makng Model to Generate Novel Emergency Response Plans for Improvng Coordnaton durng Large-scale Emergences, Knowledge-based Systems, Vol 90, Pages , DOI: /.knosys , December. (SCI, 5-Year mpact factor: 3.433, Ranked 17/130 n Computer Scence, Artfcal Intellgence by JCR n 2015). assgned to the ncdent on scene. The complexty and unpredctablty of the scenaros have resulted n emergency managers not beng unable to develop a detaled acton plan ahead of tme. Achevng the dentfed ncdent obectves s also beyond the capabltes of any organzaton. Therefore, emergency managers develop emergency response plans by breakng down ncdent obectves nto more specfc response tasks mplemented by task forces from multple organzatons wth responsbltes. Ths process nvolves a sequence of decson-makng ponts to determne the tactcal drecton and specfc resources, reserves, and support requrements to mplement the selected strateges [41]. Emergency response plans represent the ntenton of emergency managers, whch defne the work of nvolved responders, to cope wth dsastrous stuatons. They also need to ensure that all respondng actvtes undertaken are defned and tme conscous. The nvolved sequence of decsons are dependent on the on-gong ncdents and response efforts throughout the emergency response plan development process. Fnally, emergency managers dssemnate ncdent orders, whch are precsely the planned actvtes n the emergency response plan, and drect respondng task forces to acheve the ncdent obectves collaboratvely. Whle the ndvdual task forces are performng specfc response actvtes based on local stuatons, emergency managers montor on-ste response efforts and emergency stuatons, and compare the planned progress wth the actual process to evaluate the valdty of the current plan. 2.2 Requrements of emergency response plans Respondng to large-scale emergences s typcally characterzed as dynamc, hghly tme-dependent, and subect to consderable uncertanty. These characterstcs determne the context and constrants n developng and deployng emergency response plans. These plans should also consder the endurng characterstcs of the applcaton doman to provde sutable support for the emergency command operaton. Based on the analyss of plannng for emergency response, the followng requrements are necessary. (1) Expressng decomposton structure of ncdent obectves Based on the plannng procedure for emergency response, the dentfed ncdent obectves are decomposed step by step, untl all obtaned tasks can be dssemnated to the respondng organzatons for executon. Tasks generated durng the plannng process are ether performed by one or multple task forces from geographcally dspersed respondng organzatons. Each task represents an essental element to acheve the ncdent obectves wth avalable resources n the current stuaton, possbly va the collaboratve executon of several sets of subtasks by more than one respondng organzatons. Therefore, the herarches are clearly the structural characterstcs necessary to understand and conceptualze the emergency response plan. Related to levels n the emergency response organzatonal structure, emergency response plans should reflect alternatve means to acheve hgh levels of tasks. The herarchcal task structure s sutable for expressng the means to acheve the ont ntentons of emergency managers; these ntentons should be acheved by multple respondng task forces n dfferent ncdent locatons [42]. If the task can be represented, dentfed, and analyzed properly, then developng coordnaton mechansms to manpulate non-local tasks based on the task features s possble [43]. Ths pont of vew on emergency response plans defnes the followng requrement. Requrement 1: Extended emergency response plans should defne the herarchcal task structure to express the ont ntentons and ts decomposton structure to acheve the ncdent obectves. (2) Formalzng decsons n a context-senstve manner Developng emergency response plans nvolves a sequence of decson ponts to select the proper methods to acheve complex tasks. These decsons are also formed based on the ncdent status, respondng efforts, and other constrants. The decson contexts descrbe specfc condtons under whch tasks n the emergency response plans can be acheved. However, the hghly dynamc envronment changes constantly and unpredctably whle plannng for emergency response. The dffculty of developng an accurate and precse model on how the envronment

6 Tang P., Shen G.Q.P. (2015). Decson-makng Model to Generate Novel Emergency Response Plans for Improvng Coordnaton durng Large-scale Emergences, Knowledge-based Systems, Vol 90, Pages , DOI: /.knosys , December. (SCI, 5-Year mpact factor: 3.433, Ranked 17/130 n Computer Scence, Artfcal Intellgence by JCR n 2015). evolves over tme may result n the developed emergency response plans not beng ncapable of adaptng to the exstng stuaton. The uncertanty from the executon process of the response actvtes also results n unexpected effects. Consequently, dscrepances between the assumed and actual emergency stuatons occurs frequently durng the emergency response plan development and deployment processes. Therefore, emergency managers should evaluate whether the current executng plan remans vald durng an emergency command operaton. The desgn requrement based on the analyss s lsted as follows. Requrement 2: Decson contexts should be formalzed explctly by defnng attached condtons and constrants n the emergency response plans to montor plan valdty durng emergency response. (3) Supportng synchronzaton of the planned actvtes wth varable ntervals Durng emergences, plannng products should be dssemnated to multple geographcally dspersed respondng task forces on scene based on the chan of command. Responders undertake specfc orders by mplementng complex processes that must satsfy the gven constrants and produce the executon effects based on the operaton procedures and local emergency stuaton. Therefore, the specfc start and end tme of planned actvtes n the emergency response plan cannot be pre-determned durng the emergency command operaton. Instead, the exact executon tme s determned by feld responders n actual local stuatons. Planned actvtes can be conducted smultaneously and ndependently f one actvty does not affect the others because the response task forces are also geographcally dspersed. Consequently, mprovng synchronzaton among the response actvtes n emergency response plans can save consderable response tme, thereby resultng n better performance. By contrast, f the executon of an actvty affects or s affected by another one, then nterdependences exst between these actvtes. In the frst case, one planned actvty generates the executon effects, whch provdes the precondton to execute another one. Therefore, a cause effect relatonshp exsts between these actvtes. In the other case, f more than one planned actvty requres common resources, such that rescung the vctms under debrs n dfferent locatons requres the only avalable search-and-rescue team and equpment, then they should be carred out one after the other dependng on the schedulng rules. Based on the analyss, the requrement s detaled as follows. Requrement 3: The extended emergency response plan should be suffcently elaborate to descrbe the nterdependences and synchronzaton between planned actvtes to defne coordnaton across multple respondng task force unts. (4) Encodng complex temporal constrants and provdng temporal flexblty Temporal constrants should be consdered whle developng emergency response plans. Frst, ncdent obectves wth deadlnes must be acheved before emergences result n dsastrous effects. Second, partal order relatonshps between response tasks wthout detaled nformaton on duratons [44] are represented and handled explctly to synchronze and coordnate the response work of multple respondng organzatons. Complex synchronzaton, such as deadlnes or temporal landmarks of response tasks and other synchronzaton schemas between them, also defnes temporal constrants that should be taken nto account whle plannng for emergency response. By contrast, causal-effect dependences between response tasks carred out by multple respondng organzatons mply temporal constrants between them. Consequently, multple types of temporal constrants underlyng HTN should be encoded and handled explctly. Unantcpated events, such as traffc congeston that delays the arrval of fre rescue personnel, changes n weather condtons, and bad weather that prevents the necessary equpments from arrvng on ste, may affect the executon process to complete the response tasks. Ths stuaton demonstrates that the precse start and end tme of response tasks cannot be determned durng the development process of the plan. Therefore, emergency response plans should provde temporal flexblty to adapt to the uncertan and changng envronment. Based on the

7 Tang P., Shen G.Q.P. (2015). Decson-makng Model to Generate Novel Emergency Response Plans for Improvng Coordnaton durng Large-scale Emergences, Knowledge-based Systems, Vol 90, Pages , DOI: /.knosys , December. (SCI, 5-Year mpact factor: 3.433, Ranked 17/130 n Computer Scence, Artfcal Intellgence by JCR n 2015). precedng analyss, the followng desgnng requrement s provded. Requrement 4: The precse start and end tme of the response task n emergency response plans should satsfy all defned temporal constrants, and cannot be pre-determned before executon. 2.3 Extended emergency response plan structure Tradtonal acton plans n exstng HTN plannng paradgms cannot satsfy the aforementoned desgn requrements. That s the mpetus of the current study to extend the acton plans wth enrched syntax and semantcs to represent the emergency response plan. In ths secton, the herarchcal task network generated durng the plannng process s recorded to express the structure of the ncdent obectve decomposton. The condtons are attached to HTN to encode the decson contexts of multple decson ponts durng the emergency response plans developng process. Fnally, unlke acton plans wth determned start and end tmes, STN s ntegrated nto the acton plans to represent synchronzaton among the planned actvtes and encode all the underlyng tme constrants to provde temporal flexblty. The extended emergency response plan structure s lsted as follows. Defnton 1: The emergency response plan has the followng form emresplan =<TemRefTaskNet, eresprocess >, where the element TemRefTaskNet s a temporal-refnng task network representng the ncdent obectve decomposton structure and underlyng tme constrants and the element eresprocess defnes emergency response busness process to coordnate all respondng task forces. The detaled structure of these two elements n emergency response plan s lsted as follows. (1) Temporal refnng task network The temporal refnng task network s lsted as the followng: Defnton 2: The temporal refnng task network has the form: TemRefTaskNet = reftasknet, exstn, actset, where the frst element s a herarchcal task network descrbng the ncdent obectve decomposton structure; the second one s an extended STN encodng tme constrant underlyng all tasks; and the last one s a set of planned actvtes to acheve prmtve tasks, whch are exactly the leaf nodes of the herarchcal task network. 1) The varable reftasknet = { extasknode task, st, et, ttype, desconlst, partask, chldtaskset } (1 M, where M s the total number of task nodes) conssts of a set of extended task nodes. Each task node represents a task boolean varable task, whch s executed durng the nterval between ts start tme ttype represents the type of task. If st and end tme et. The ttype s true, t s a compounded task. Otherwse, t s a prmtve one. As the task n the HTN plannng paradgm, the prmtve task node s executed through a planned actvty drectve, and the varable chldtaskset s ntalzed by the unfyng planned actvty. Instead, each compounded task node s acheved by performng a set of chld task nodes recorded n the varable chldtaskset. The varable partask s the parent task of task task. Fnally, the varable desconlst { descon } (1 N, where N s the total number of means for achevng task task ) s a set of condtons or constrants that the current stuaton and avalable resources should satsfy when the current task node can be acheved by those n the varable chldtaskset. 2) The varable exstn s an extended STN underlyng the herarchcal task network to encode all tme

8 Tang P., Shen G.Q.P. (2015). Decson-makng Model to Generate Novel Emergency Response Plans for Improvng Coordnaton durng Large-scale Emergences, Knowledge-based Systems, Vol 90, Pages , DOI: /.knosys , December. (SCI, 5-Year mpact factor: 3.433, Ranked 17/130 n Computer Scence, Artfcal Intellgence by JCR n 2015). constrants on the start to the end tme wth the task nodes. Unlke tradtonal acton plans that determne the exact executon tme or defne the order between tasks, the varable provdes temporal flexblty for adaptng to the uncertanty of emergency response processes by encodng all tme constrants underlyng the emergency response plan structure. The detaled structure of the extended STN wll be ntroduced n Secton 5. 3) The varable actset s a set of planned actvtes descrbng the executon processes of assocated prmtve tasks. The detaled paradgm wll be ntroduced n Secton 3.1. Each planned actvty acheves a prmtve task n the herarchcal task network. The role of the abovementoned structure mantans an nstance of HTN exploraton for the current plannng problem and provdes gudance for emergency managers to drect and supervse the response efforts. (2) Emergency response busness process The emergency response busness process conssts of a set of planned actvtes wth order and synchronzaton relatonshps. The planned actvtes achevng the prmtve task nodes n the temporal refnng task network descrbe the response actons carred out by geographcally dspersed respondng task forces, such as frefghtng, searchng for vctms, transportng evacuee, settng up tents, and so on. Accordng to the analyss, the emergency response busness process s defned as the follows. Defnton 3: The emergency response busness process has the form eresprocess = { actplus act, Prev, Succ } (1 L, where L s the total number of actons plus n the process). The acton plus actplus s an extended acton model for recordng ordered relatonshps between them n the busness process. In each acton plus model, the varable act represents a planned actvty, where the varables Pre and Succ record a set of mmedate prevous acton plus and a set of mmedate successor ones of the acton act. For the mmedate successor acton plus set of a gven acton plus actplus, f an acton plus actplus exsts n actplus. Succ, then another acton plus actplus does not exst, such that actplus. act actplus. act and k k k actplus. act actplus. act. In the same way, f an acton plus actplus ' exsts n actplus. Prev, then another k k acton plus actplus k ' does not exst, such that actplusk '. actk ' actplus. act and actplus '. act actplus '. act. k k The planned actvty s also represented by an operator nstance as shown n Secton 3.1, whch descrbes the detaled executon process of a specfc prmtve task node. Its start and end tmes are exactly the same as those of the assocated prmtve task node. Moreover, the assumed executon effects representng response efforts descrbe the way the emergency stuaton wll change durng the executon nterval. The assumed executon effects also set the obectves for task forces performng ths actvty and provdes the crtera for montorng the executon progress for emergency managers. As a result, the task forces wll carry out the respondng actvtes accordng to the local stuaton to produce the defned effects. Fnally, the sequence and parallel relatonshps are also defned

9 Tang P., Shen G.Q.P. (2015). Decson-makng Model to Generate Novel Emergency Response Plans for Improvng Coordnaton durng Large-scale Emergences, Knowledge-based Systems, Vol 90, Pages , DOI: /.knosys , December. (SCI, 5-Year mpact factor: 3.433, Ranked 17/130 n Computer Scence, Artfcal Intellgence by JCR n 2015). explctly to provde support for emergency managers n EOC to ssue the ncdent orders. The abovementoned emergency response plan also descrbes the ncdent obectve decomposton structure generated by the emergency response plan developng process. Second, the plan structure represents decson contexts explctly. Ths representaton enables emergency managers to assocate montors to dfferent abstracton levels of tasks and detect stuatons that may nvaldate the current plan. Thrd, the start and end tmes of the planned actvtes are not pre-determned untl executon to enhance the temporal flexblty of the entre plan. The followng content ntroduces the desgn of a decson-makng model that wll generate ths plan structure based on ntegratng HTN plannng and schedulng technologes. 3. Knowledge formalsm Ths secton proposes the knowledge formalsm of our decson-makng model based on AI plannng technologes, such as the temporal enhanced operator, temporal plannng state, and enhanced herarchcal task network. They provde bass for the desgn of a plannng algorthm that wll produce the abovementoned emergency response plans. The other knowledge formalsms are smlar to the exstng HTN plannng paradgm XePlanner [33]. 3.1 Temporal enhanced operator An operator descrbes the respondng actvtes performed by task forces, such as searchng for vctms, frefghtng, transportng evacuees, and other tasks. Accordng to the desgn requrements n Secton 2.2, the respondng actvty s executed by a complex process carred out by respondng organzatons. Hence, for adaptng to the characterstcs, a temporal enhanced operator s proposed by extendng operator formalsm n the exstng HTN planner, such as XePlanner [33] and SHOP2 [18]. The extensons are lsted as the followng: (1) The nstantaneous precondton of operators can be satsfed before the respondng actvty starts or at any tme durng executon. The nvarant condtons n operators are also formalzed explctly and descrbe the condtons that should be satsfed n any nterval of the executon process. (2) The duraton of the operator cannot be pre-determned. Instead, the enhanced operator provdes terms to represent the start and end tmes of the response actvtes, whch cannot be estmated exactly because of the uncertanty n the emergency response process. An nstance of a temporal enhanced operator s shown n Fgure 1. Ths knowledge formalsm descrbes an actvty whch drves a vehcle?t from locaton?loc-from to locaton?loc-to. Unlke the operator n PDDL 2.1 [36], the duraton of ths operator s not pre-defned. In addton, the effects occurs at any tme durng the executon nterval. (:operator (!drve?t?loc-from?loc-to) ;;nstantaneous precondton 0)) ;;nvarant condtons () ;;delete lst for nstantaneous effects ((at?t?loc-from)) ;;add lst for nstantaneous effects () ;;delete lst for the delayed effects () ;;add lst for the delayed effects (((at?t?loc-to) + et)) ;;cost 50) Fg. 1. Example of an operator

10 Tang P., Shen G.Q.P. (2015). Decson-makng Model to Generate Novel Emergency Response Plans for Improvng Coordnaton durng Large-scale Emergences, Knowledge-based Systems, Vol 90, Pages , DOI: /.knosys , December. (SCI, 5-Year mpact factor: 3.433, Ranked 17/130 n Computer Scence, Artfcal Intellgence by JCR n 2015). 3.2 Temporal enhanced plannng state In classcal HTN paradgms, the plannng state s represented by a set of predcates that assume that these propostons are true, whle propostons not n the classcal plannng state are false. In ths secton, an enhanced plannng state s proposed for recordng the tme when the predcates are generated or trggered. Therefore, the temporal enhanced predcate s also called the temporal predcate. The postve and negatve predcates are all recorded n the enhanced plannng state. If the enhanced predcate s a postve one, then the predcate p s added to the world state after beng trggered. Otherwse, the predcate p s deleted from the world state. The abovementoned analyss shows that the temporal predcate has the followng form. Defnton 4: A temporal predcate s a trple p, flag, tp. The varable p represents the proposton. The varable flag s a boolean varable expressng whether the predcate s a postve or a negatve predcate. If ths varable s true, then the predcate s postve; otherwse, t s negatve. The varable tp s the tme stamp that records the tme when the predcate s generated. Durng the plannng process, temporal predcates are gven by the ntal plannng state or generated by the executon effects when the operators are appled to acheve the prmtve tasks. Therefore, f a predcate s specfed n the ntal state, then the varable tp s ntalzed by TR representng the reference tme of the emergency response process. Otherwse, the varable tp s specfed by the formula t delta. If the symbol s gven, then varable t represents the start tme of an operator; otherwse, t represents the end tme. The varable delta s the tme nterval relatve to the start or end tme when the executon effects are generated. As a result, the temporal enhanced plannng state conssts of a set of temporal enhanced predcates. Unlke tradtonal plannng paradgms [16], the abovementoned plannng state assumes that the propertes of the world change over tme and records the tme when these propertes change. Instead of a sngle global tme, multple local tmes, whch are the tme stamps of each predcate, are recorded n the enhanced plannng state. They provde bass for dscoverng the cause-effect relatonshps between the planned actvtes and the desgn of concurrent controllng mechansm to ensure parallelsm of multple planned actvtes. The detals wll be ntroduced n secton Enhanced herarchcal task network Durng the HTN plannng process, the plan-refnng steps nclude decomposng a compounded task and applyng a prmtve one. The enhanced herarchcal task network s desgned to record the entre decomposton structure of the ncdent obectves generated durng the entre plannng process. Its formalsm s lsted below. Defnton 5: The enhanced herarchcal task network has the form enhetasknet { tplus } (1 M, where M s the total number of task pluses) and conssts of a set of task pluses for recordng the generated nformaton durng the search process. The task plus s an extended task model that expresses explctly the start tme, end tme, type, and decomposton structure. The defnton of task plus s provded n detal as below. Task plus has the form taskplus task, st, et, ttype, refnpreconlst >, where task s a task atom smlar to that n classcal HTN plannng paradgms, st and et represent the start and end tmes of the task respectvely, and ttype s a boolean varable expressng the type of task. If the varable ttype s true, then task s a compounded task. Otherwse, t s a prmtve one. The varable refnpreconlst { refnprecon }(1 N) records a set of plan-refnng steps to acheve task task. These steps are generated when all precondtons of the methods unfyng the current task are evaluated to determne whether they are satsfed n the current plannng state. The formalsm of the plan-refnng step s lsted as below. Defnton 6: The plan-refnng step has the form refnprecon = ( prelst,var BndArrayMean ). The

11 Tang P., Shen G.Q.P. (2015). Decson-makng Model to Generate Novel Emergency Response Plans for Improvng Coordnaton durng Large-scale Emergences, Knowledge-based Systems, Vol 90, Pages , DOI: /.knosys , December. (SCI, 5-Year mpact factor: 3.433, Ranked 17/130 n Computer Scence, Artfcal Intellgence by JCR n 2015). varable prelst s a lst of precondtons that record the nstantaneous precondtons of methods or operators for refnng the gven task task. If the evaluaton shows these precondtons have been satsfed n the current plannng state, then varable vabndmeanlst { vabndarraymean } (1 k L) records all alternatve k achevng means vabndarraymean k to refne the gven task n ths specfc nstantaneous precondton lst, and L s the total number of alternatve achevng means. Otherwse, t s ntalzed by null. The varable vabndarraymean k can acheve a gven task when the nstantaneous precondton lst current plannng state wth a specfc varable bndng array. Its detaled formalsm s lsted as below. prelst s satsfed n the Defnton 7: The achevng means has the form vabndarraymean ( varbndarray, chldren ), where the k k k varable vabndarray k s an array of varable bndngs specfyng that the gven nstantaneous precondton lst s satsfed n the current plannng state wth t. If the task plus s a compounded one, then the varable m chldren { taskplus }(1 m L) s a set of task pluses called chld task pluses used to acheve the gven task k task wth the varable array k varbndarray k. Otherwse, the varable records the operator to acheve the prmtve task plus wth ths varable array. Accordng to the abovementoned defnton, the enhanced herarchcal task network records the traversed search track by decomposng the ntal ncdent obectves. Obvously, the track can be used to extract the abovementoned emergency response plan. An example of the enhanced herarchcal task network s shown n Fgure 2 to descrbe ts structure. As shown, the cons n ths fgure represent the varables n defnton 6 and 7. Fgure 2. An example of the enhanced herarchcal task network 4. State-based forward plannng process Developng emergency response plans durng large-scale emergences nvolves knowledge-based hgh-level

12 Tang P., Shen G.Q.P. (2015). Decson-makng Model to Generate Novel Emergency Response Plans for Improvng Coordnaton durng Large-scale Emergences, Knowledge-based Systems, Vol 90, Pages , DOI: /.knosys , December. (SCI, 5-Year mpact factor: 3.433, Ranked 17/130 n Computer Scence, Artfcal Intellgence by JCR n 2015). cogntve control [45], whch challenges the decson-makng capabltes of emergency managers. The plannng process s a computatonal model for provdng cogntve-level support to the development process of emergency response plan, whch nvolves a sequence of decson ponts as shown n secton 2.1. Ths secton ntroduces the man algorthm and key procedures of the plannng process. 4.1 Man algorthm The nput of the plannng algorthm s the ntal plannng state representng the emergency stuaton at the begnnng of the emergency response and all the dentfed ncdent obectves to be acheved. Smlar to SHOP2, ths plannng process s a state-based forward and proceeds as the enhanced herarchcal task network s expanded or the plannng state s refreshed. The plannng process s also advanced to expand a common search space consstng of search nodes wth a specal structure. The detaled defnton of a search node s lsted as below. Defnton 8: The search node has the form sn ActSet, s, taskplusset, exstn, where the varable ActSet s a set of planned actvtes represented by the generated operator nstances, the varable s s the current plannng state, the varable taskplusset s a set of leaf task pluses n the enhanced herarchcal task network to be refned, and the varable exstn s an extended STN encodng all the tme constrants underlyng the assocated herarchcal task network. In the search node formalsm, the start and end tmes of each operator nstance n ActSet are the start and end tmes of the unfyng prmtve task plus. All parent task pluses of the generated operator nstances and task pluses n taskplusset consttute the herarchcal task decomposton structure assocated wth the current search node. Ths structure can be extracted from the common enhanced herarchcal task network. The extended STN wll be ntroduced n Secton 5. The search space recordng all search nodes s ntalzed by, s0, taskplusset, Stn, and s exploted by advancng the plannng process. A common data structure of the enhanced herarchcal task network s expanded and records all plan-refnng nformaton. The varable enhetasknet s ntalzed by all task pluses recorded n taskplusset, whch represent all the ncdent obectves to be acheved. Smlar to XePlanner, ths algorthm performs a depth-frst search process n the space of all decompostons of the gven ntal task network, returns a set of search nodes sorted by the metrc values, and outputs an optmal one when the emergency managers determne to nterrupt t. In each teraton, a search node s selected from search space openlst. Thereafter, the tasks wth no predecessors n ths search node are selected to be refned. The plan-refnng steps decompose the compounded tasks and apply the prmtve tasks, as ntroduced n Secton 4.2. When all compounded tasks n the current search node are decomposed and all prmtve tasks are appled, or when the plannng tme exceeds the tme lmtaton gven by emergency managers, the plannng process termnates, and the emergency response plans are extracted as shown n Secton 6. The key procedures n the presented model, such as expandng the search space, dscoverng the cause-effect relatonshps between tasks, and concurrent controllng mechansm of multple planned actvtes, are novel and wll be ntroduced n the followng secton, and are dfferent form those n XePlanner [33]. 4.2 Key procedures In ths part, the key procedures nvolved n the man plannng algorthm are presented, and the processes of provdng specfc functons by the presented decson-makng model are ntroduced Expandng the search space Gven a search node, the plan-refnng steps nclude decomposng compounded tasks and applyng prmtve ones. The detals are lsted below. (1) Decomposng compounded tasks. When a compounded task t wth no predecessors s selected to be

13 Tang P., Shen G.Q.P. (2015). Decson-makng Model to Generate Novel Emergency Response Plans for Improvng Coordnaton durng Large-scale Emergences, Knowledge-based Systems, Vol 90, Pages , DOI: /.knosys , December. (SCI, 5-Year mpact factor: 3.433, Ranked 17/130 n Computer Scence, Artfcal Intellgence by JCR n 2015). refned, the unfyng methods are used to decompose t. Frst, the precondtons of each branch are evaluated ndvdually on whether they are satsfed by the current plannng state. If they are satsfed wth a varable bndng array, then the generated cause-effect relatonshps are added to the varable exstn. caueftset, whch records all cause-effect relatonshps between these two tme ponts. All subtasks n ths branch are added to the varable enhetasknet. Ther tme ponts and the underlyng tme constrants are also added to the extended STN exstn. (2) Applyng prmtve tasks. When a prmtve task t wth no predecessors s selected to be refned, t s appled by the unfyng operators. Frst, the precondtons of ths operator are evaluated as decomposng the compounded tasks. If they are satsfed by the current plannng state, then the enhanced STN s updated n the same manner. The operator nstance s also added to the varable enhetasknet and the search node. As a result, the plannng state s updated by the executon effects of ths operator nstance Dscoverng the cause-effect relatonshps between tasks An mportant functon of emergency response plans s to dscover and manage nterdependences between response tasks and actvtes. One of the prncples of ths plan s coordnatng multple respondng task forces based on unfed acton plans durng emergency command operaton. The basc nterdependences between tasks are cause-effect relatonshps [22] that show that the producng task provdes the executon condtons of the consumng task. Durng the search space expandng process, two basc plan-refnng steps dscover and generate cause-effect relatonshps among the tasks, such as decomposng compound tasks and applyng prmtve ones. When a compounded task s decomposed, the nstantaneous precondton lst for each branch of the unfyng methods s evaluated to check the satsfablty. For each lteral n an nstantaneous precondton lst wth tme pont t that matches a postve temporal predcate wth tme pont t n the plannng state, a cause-effect relatonshp s generated to record the causal structure between the tasks and s defned on these tme ponts. The same procedure s appled for a prmtve task. From the abovementoned analyss, the knowledge formalsm of cause-effect relatonshps s lsted as below. Defnton 9: A cause-effect relatonshp has the form p ce t t, where the varable t s the tme l stamp of the last postve temporal predcate generatng p, and varable t s the tme stamp of the nstantaneous precondton satsfed by the former predcate. An nstantaneous precondton nstantpre = ( logcalexp@ t ) n a method or an operator refnng a task mples that the complex logcal expresson logcalexp must be satsfed by the plannng state before tme t. The logcal expresson logcalexp s a logcal atom or a complex expresson, such as conuncton, dsuncton, negaton, assgnment expresson, and call expresson as n SHOP2 [18]. The tme pont t s ether the start tme or any tme pont durng the nterval of a compound task or a prmtve one. From the abovementoned defnton, our decson-makng model supports complex logcal expressons for checkng the satsfacton of precondtons, and the detals of cause-effect relatonshp generaton are lsted as follows. (1) If a logcal expresson logcalexp s a logcal atom and s satsfed by a temporal enhanced predcate

14 Tang P., Shen G.Q.P. (2015). Decson-makng Model to Generate Novel Emergency Response Plans for Improvng Coordnaton durng Large-scale Emergences, Knowledge-based Systems, Vol 90, Pages , DOI: /.knosys , December. (SCI, 5-Year mpact factor: 3.433, Ranked 17/130 n Computer Scence, Artfcal Intellgence by JCR n 2015). p, t n the current plannng state, then a cause-effect relatonshp p ce t t s generated. l (2) If a logcal expresson logcalexp ( and L1 LN) (1 N ) s a conuncton, where N s the total number of logcal atoms n ths expresson. If a logcal atom L s satsfed by a temporal enhanced predcate p, t n the current plannng state, then a set of cause-effect relatonshps { ce t t } are generated. p (3) If a logcal expresson logcalexp ( or L1 LN) (1 N ) s a dsuncton, where N s the total number of logcal atoms n ths expresson. If there exst a logcal atom L satsfed by a temporal enhanced predcate p, t n the current plannng state, then a cause-effect relatonshp { ce t t } s generated. p (4) If logcalexp s a dsuncton, negaton, assgnment expresson, or call expresson, then no cause-effect relatonshp s generated. Obvously, unlke the causal lnks between two operators n POCL [26], the presented cause-effect relatonshps are temporal and are defned on two tme ponts. As a result, the temporal causal-effect relatonshps are more precse because the tme ponts can be any tme pont durng the executon nterval of operators. The relatonshps provde an expressve representaton and explot the greatest level of concurrency between planned actvtes. Moreover, unlke SIADEX [22], whch only defnes causal-effect lnks between planned actvtes at the lowest level, the abovementoned cause-effect relatonshp formalsm represents the causal structure between the tasks n dfferent abstracton levels. In addton, when a cause-effect relatonshp p ce tp tp s generated, a temporal constrant t t 0 s added to the STN of the search node for encodng t Concurrent controllng of multple planned actvtes The abovementoned plannng algorthm s a state-based forward plannng paradgm smlar to SHOP2 and XePlanner, n whch the generaton process of planned actvtes s the same as the executon process. Despte the actons are generated one by one durng the plannng process, they are partally ordered, and ther executon ntervals may overlap. Ths secton ntroduces a concurrent controllng mechansm of multple operators wth varable ntervals. For an nstantaneous precondton nstantpre = ( t ) of a method or an operator, the plannng algorthm chooses the earlest possble postve unfed tmed predcate ptruet,, 1 that supports the logcal expresson logcalexp. In addton, all the followng condtons should also be satsfed. (1) There s a postve tmed predcate ptruet,, 2, whch satsfes logcal expresson logcalexp. (2) There s no negatve tmed predcate p, false, t3 n the plannng state, such that t2 t3 t1.

15 Tang P., Shen G.Q.P. (2015). Decson-makng Model to Generate Novel Emergency Response Plans for Improvng Coordnaton durng Large-scale Emergences, Knowledge-based Systems, Vol 90, Pages , DOI: /.knosys , December. (SCI, 5-Year mpact factor: 3.433, Ranked 17/130 n Computer Scence, Artfcal Intellgence by JCR n 2015). (3) There s no other postve tmed predcate pturet,, 4, such that t4 t2, and satsfng the above two condtons. Durng the plannng process, the operators are appled sequentally n the plannng state. An operator a s applcable n the temporal enhanced plannng state f the followng condtons are satsfed. (1) The nstantaneous and nterval precondtons of ths operator a are satsfed n the current plannng state. (2) The effects of ths operator a do not nterfere wth the current plannng state and any recorded nvarant condtons. 3) A negatve tmed predcate p, t1, whch nterferes wth the nvarant condtons p,( t2, t3) of ths operator a, does not exst n the plannng state. In addton, the nterference s defned as the volaton of any of the followng condtons: (1) If a tmed predcate p, false, t1 exsts n the plannng state that causes p at tme t 1, then the effect of ths operator a that caused p should be added after tme t 1. In the same way, f a tmed predcate ptruet,, 2 exsts n the plannng state that causes p at tme 2 t, then the effect of acton a that caused p should be added after tme t 2. (2) If a effect of ths operator a deletes a predcate p and s protected by an nvarant condton p,( t, t ) n the current plannng state, then ths operator cannot delete the predcate before t (3) If ths operator a defnes an nvarant condton p,( t1, t2) and a negatve tmed predcate p, t exsts n the current plannng state that causes p n tme t 3, then the negatve tmed predcate 3 should occur after ths nvarant condton termnates. That s to say, the tme constrant t2 t3 0 s added to the current STN. (In ths paper, the end tme of the nvarant condton happens before the negatve tmed predcate, that s, tme pont t 3 s ordered to tme pont t 1.) Accordng to the abovementoned concurrent controllng mechansm for ensurng parallelsm of multple planned actvtes, all the cause-effect relatonshps are dscovered and encoded by the STN when prmtve tasks are appled. As a result, the generated actons are arranged properly and do not nterfere wth one another. 5. Incremental temporal management method based on sem-stn The abovementoned decson-makng model that ams to support the development process of emergency response plans provdes excellent expressng capablty to represent qualtatve and quanttatve tme constrants. When the compounded tasks are decomposed or when prmtve tasks are appled to expand the search space, the tme constrants are generated and added to the search nodes to encode nterdependences between the tasks. Obvously, temporal conflcts may occur. In the presented decson-makng model, the start and end tmes of all the tasks are represented explctly. Consequently, the entre STN s larger compared to exstng plannng paradgms,

16 Tang P., Shen G.Q.P. (2015). Decson-makng Model to Generate Novel Emergency Response Plans for Improvng Coordnaton durng Large-scale Emergences, Knowledge-based Systems, Vol 90, Pages , DOI: /.knosys , December. (SCI, 5-Year mpact factor: 3.433, Ranked 17/130 n Computer Scence, Artfcal Intellgence by JCR n 2015). and more plannng tme s requred to check the temporal consstency of the underlyng STN. Ths secton proposes a temporal constrant propagaton algorthm called Prop-PC2-STP, whch takes full advantage of the herarchcal decomposton structure nduced by the HTN plannng process. Ths algorthm s embedded n the plannng process for propagatng tme constrants. 5.1 Extended smple tme net Our decson-makng model provdes an expressve representaton of tme constrants between tasks. Durng the plannng process, the generated tme constrants, whch are ether pre-defned beforehand or nduced by the plan-refnng process, are added ncrementally to the STN for encodng the ndependences between tasks n a search node. On the one hand, when a compounded task s decomposed, four types of tme constrants are generated and should be encoded by the STN. The detals nclude the followng: C-Type1: tme constrants produced by checkng the satsfacton of the precondtons n the method, as shown n 4.2.2, C-Type2: tme constrants encodng the start tme of the parent task s ordered to the start tme of all ts chld tasks, and those representng the end tme of all the chld tasks are before the end tme of the parent task, C-Type3: tme constrants encodng the start tme of all the chld tasks are ordered to ther end tme, C-Type4: qualtatve and quanttatve tme constrants between the chld tasks, whch are defned n the method formalsm. On the other hand, when a prmtve task s appled, two types of tme constrants are generated, and are lsted as follows: P-Type1: tme constrants produced by checkng the satsfacton of the precondtons for the operator unfyng the prmtve task, as shown n 4.2.2, and P-Type2: tme constrants produced by applyng the concurrent controllng rules. When all the tme constrants mentoned above are added to the STN and reman consstent, the current plan-refnng steps can be executed. The STN s a framework wdely used for checkng temporal consstency and dervng the mnmal network [34]. Amng at mprovng the effectveness of tme management, the tradtonal STN s extended to encode all tme constrants and herarchcal decomposton structures nduced by the HTN plannng process. Frst, the tme pont cluster structure s recorded to dvde the underlyng STN nto multple smaller subnets. Second, the cause-effect relatonshps defnng on two tme ponts representng the nterdependences between dfferent tasks are represented explctly. The defnton of the extended STN s stated as below. Defnton 10: The extended STN has the form exstn tpset, tcset, caueftset, clusterstructure. tpset { TR, tp, tp } (1 L, where L s the number of all the tasks) s a set of tme ponts (1) representng the start or end tmes of all tasks n the herarchcal task network and the reference tme pont of the plannng process. (2) tcset tc a tp tp b (1 M, where M s the number of all the tme constrats) 1 2 { } represents the tme constrants n the extended STN. (3) p { 1 2 k k k } caueftset ce tp tp (1 k N, where N s the number of all the cause-effect relatonshps) s a set of cause-effect relatonshps as shown n Defnton 9. (4) clusstruct { C tpset, ngcluslst } s the tme pont cluster structure consstng of a set of tme ponts subcollecton n the entre STN and ther ntersecton set, called separaton. The varable tpset s a set of tme ponts n tme pont cluster C, and the varable ngcluslst { ng sep, C } records a lst of k

17 Tang P., Shen G.Q.P. (2015). Decson-makng Model to Generate Novel Emergency Response Plans for Improvng Coordnaton durng Large-scale Emergences, Knowledge-based Systems, Vol 90, Pages , DOI: /.knosys , December. (SCI, 5-Year mpact factor: 3.433, Ranked 17/130 n Computer Scence, Artfcal Intellgence by JCR n 2015). neghborng tme pont clusters. The varable ng k descrbes that C s the neghbor tme pont cluster of C, and there s a separaton sep between these two tme pont clusters. The separaton sep records the tme ponts, whch are both n C and C smultaneously. Durng the plannng process, when a cause-effect relatonshp p 1 2 k k k ce tp tp s generated, t s added to the cause-effect relatonshp set exstn. caueftset. At the same tme, the nduced tme constrant tp tp 0 s 1 2 k k added to exstn. tcset, and the temporal consstency of the current extended STN s checked. Each tme pont cluster always conssts of TR and the start and end tme ponts of a compounded task and ts chld tasks. Moreover, the frst tme pont cluster n the extended STN s ntalzed by TR and the start and end tme ponts of all the tasks representng the ncdent obectves. Durng the plannng process, the tme pont cluster structure s expanded as the followng: Rule 1: When the tme constrants of C-Type1, P-Type1, and P-Type2 are generated and added to the extended STN, f the pre-tme pont pretp and post-tme pont posttp are n the same tme pont cluster C for each tme constrant a pretp posttp b, then the tme pont cluster structure of the STN remans unchanged. Otherwse, gven that the pre-tme pont pretp belongs to tme pont cluster C and that the post-tme pont posttp belongs to tme pont cluster C, the pre-tme pont pretp s added to tme pont cluster C and the separator between tme pont clusters C and C. Rule 2: When the tme constrants of C-Type2, C-Type3, and C-Type4 are generated, gven that the start and end tme of the compounded task are found n tme pont cluster C, a new tme pont cluster C s created and ntalzed by TR. Moreover, the start and end tme of the compound task to be decomposed and those of all ts subtasks are added to tme pont cluster C. The separator between tme pont clusters C and C s ntalzed by the start and end tmes of ths compounded task and TR. As a result, the tme pont cluster C s called the affected tme pont cluster, and the sub-stn underlyng C s called the ntally affected sub-stn, to whch the generated tme constrants are added. From the abovementoned analyss, the entre STN s dvded nto multple sub-nets underlyng each tme pont cluster. Each sub-net encodes all the tme constrants underlyng the tme ponts n the assocated tme pont cluster. 5.2 Temporal propagaton algorthm The study takes full advantage of the herarchcal decomposton structure to propagate tme constrants on the underlng STN. A new tme propagaton algorthm, called Prop-H-STN, s proposed and trggered once the tme ponts and ther underlng tme constrants are added ncrementally. The Prop-H-STN algorthm s trggered n three cases durng the plannng process. Frst, tme constrants of C-Type1 are generated and added to the extended STN when the precondton n the method s checked. Second, the tme constrants of C-Type2, C-Type3, and C-Type4

18 Tang P., Shen G.Q.P. (2015). Decson-makng Model to Generate Novel Emergency Response Plans for Improvng Coordnaton durng Large-scale Emergences, Knowledge-based Systems, Vol 90, Pages , DOI: /.knosys , December. (SCI, 5-Year mpact factor: 3.433, Ranked 17/130 n Computer Scence, Artfcal Intellgence by JCR n 2015). are generated and added to the extended STN when a compound task s decomposed. Thrd, the tme constrants of P-Type1 and P-Type2 are generated and added to the extended STN when a prmtve task s appled. In the exstng temporal HTN planner, the added tme constrants are propagated ndvdually on the entre underlng STN drectve. All generated tme constrants n Prop-H-STN are nstead added to the ntal affected sub-stn and are propagated smultaneously. The tme pont cluster structure changes accordng to Rules 1 and 2 n Secton 5.1. The detaled procedures of Prop-H-STN algorthm s shown n Fgure 3. 0: functon prop-h-stn( exstn, tcset, C ) 1: propagate tcset n ntal affected sub-stn underlng C ; 2: for each neghbor tme pont cluster C of 3: for each tc. pretp C C and tc. posttp C C 4: tghtentcset { tc tc s tghten after sub-stn underlng C s propagated}; 5: end for each 6: f ( tghtentcset s not null) 7: prop-ncremental-pc2(edstn, tghtentcset, C ); 8: end f 9: end for each 10: end functon Fg 3. Prop-ncre-PC2 algorthm The nputs of Prop-H-STN algorthm are an extended STN, a set of tme constrants, and the affected tme pont cluster. All tme constrants are added to the ntal affected sub-stn underlng of the affected tme pont C cluster C. Frst, the tme constrant propagaton algorthm (Fgure 6) s appled to propagate and check the temporal consstency n the underlng sub-stn of the current affected tme pont cluster C (Lne 1). For separators between tme pont cluster C and each of ts neghbor tme pont clusters C, tme constrant s added to the varable tghtentcset (Lnes 3 5) once t s tghtened after tme propagaton. Ths algorthm contnues to propagate tme constrants recursvely n tghtentcset (Lnes 6 9) f the varable s not null. The algorthm stops once no tme constrant n the sub-stn can be tghtened. Thus, all underlng sub-stns of each tme pont cluster are local mnmal. That s, the entre STN, whch s a partal mnmal network, s consstent [46]. All local mnmal tme constrants are propagated n the sub-stn underlng of the neghbor tme pont cluster f the tme constrants defned on the tme ponts n separators between current tme pont cluster and ts neghbor tme pont clusters are tght. The temporal propagaton process n a sub-stn, as shown n Lne 1 of Prop-H-STN algorthm, tghtens the edges representng tme constrants n the sub-stn underlng a tme pont cluster. The detals of the procedure are lsted n Fgure 4. 0: functon propagate-substn( tcset, sub _ STN ) 1: Q ; 2: for each tme constrant tc tcset 3: for each tme pont tp C. tpset 4: f tp tc. pretp and tp tc. posttp 5: Q {( tc. pretp, tc. posttp, tp)} ; 6: end f 7: end for each 8: end for each 9: whle Q

19 Tang P., Shen G.Q.P. (2015). Decson-makng Model to Generate Novel Emergency Response Plans for Improvng Coordnaton durng Large-scale Emergences, Knowledge-based Systems, Vol 90, Pages , DOI: /.knosys , December. (SCI, 5-Year mpact factor: 3.433, Ranked 17/130 n Computer Scence, Artfcal Intellgence by JCR n 2015). 10: select and delete a trangle from Q ; 11: Revse( ) ; 12: for each tc havng been updated n trangle 13: for each tme pont tc C. tpset 14: Q {( tc. pretp, tp, tc. posttp),( tc. posttp, tp, tc. pretp)} 15: end for each 16: end for each 17: end whle 18: end functon Fg 4. Tme propagaton algorthm n a sub-stn The above algorthm handles multple tme constrants n varable tcset and smultaneously propagates them n the current sub-stn. The assocated trangles of a tme constrant are nspred by the STP algorthm [47], whch provdes a new perspectve on temporal problems as composed by a set of trangles. These trangles consst of vertexes representng two tme ponts of ths tme constrant and each tme ponts n a gven tme pont cluster, except for the two former ones. The varable Q n propagate-substn algorthm records all trangles to be checked (Lne 1) and are ntalzed by the assocated trangles for each nput tme constrant (Lnes 2 8). In each teraton, a trangle s selected and computed (Lne 10). In lne 11, one edge of trangle s tghtened and updated smlar to PC2 algorthm, or all three edges are tghtened at once compared to the STP algorthm. Gven that each tghtened edge represents a tme constrant, ther assocated trangles are added to the varable Q. They can be added to the front, the end, or any poston n the queue. The manner n whch the trangles are nserted n the queue also affects the performance of ths temporal propagaton algorthm. The experments wll be ntroduced n secton 7. Fnally, the algorthm termnates, and a mnmum sub-stn s obtaned once the varable Q s null. 6. How to extract the emergency response plan Ths secton ntroduces the method for extractng the emergency response plans of the new structure from the generated plannng nformaton once the plannng process termnates. The generated plannng nformaton, whch s recorded by the data structure of the enhanced herarchcal task network, as well as the generated search node, s ntroduced n above sectons. The emergency response plan conssts of two elements. The frst element keeps a trace of the selected decompostons durng the HTN exploraton, whch records a sequence of decson ponts whle plannng for emergency response. The second element corresponds to the acton plan, whch conssts of a set of planned actvtes n the lowest level of temporal refnng task network. The extracton of the two elements s ntroduced n ths secton. 6.1 Extractng the temporal refnng task network A method of constructng the temporal refnng task network (Secton 2.3) s proposed accordng to the nformaton elements generated by the plannng process. The detaled process of extractng each element n the temporal refnng task network s lsted as follows: (1) The extended task node n the temporal refnng task network s defned as extasknode task, st, et, ttype, conlst, partask, chldtaskset. The frst three elements are the same as the relevant elements n the formalsm of the unfyng task plus taskplus n an enhanced herarchcal task network. The varable desconlst { descon } representng decson contexts for refnng the tasks s a lst of

20 Tang P., Shen G.Q.P. (2015). Decson-makng Model to Generate Novel Emergency Response Plans for Improvng Coordnaton durng Large-scale Emergences, Knowledge-based Systems, Vol 90, Pages , DOI: /.knosys , December. (SCI, 5-Year mpact factor: 3.433, Ranked 17/130 n Computer Scence, Artfcal Intellgence by JCR n 2015). decson condtons descon. Accordng the enhanced herarchcal task network formalsm, the condton has the form descon tp, pre, varbndarray. Ths form ndcates that the precondton pre should be satsfed by an emergency stuaton wth varable bndng array varbndarray at tme pont tp, when a compound task node extasknode s acheved by all of ts subtasks or a prmtve one s executed by an operator nstance. The varable tp s a tme pont representng the start, end, or any tme pont durng the nterval of task task. Gven that the task plus taskplus n the enhanced herarchcal task network s assocated wth a gven task node tnode, a decson condton descon tp, refnprecon. prelst, refnprecon. varbndarraylst.varbndarray ) s k generated for each refnprecon n taskplus. refnpreconlst. Fnally, the varable partask s the task node unfed to the task plus taskplus of the current task node and s extracted by the same procedure. The varable chldtaskset s a set of task nodes unfyng to the task pluses n taskplus. refn Pr econ. varbndarraylst. varbndarraymean. chldren. k k (2) The varable exstn s ntalzed by the extended STN n the output search node. (3) The planned actvty has the form act =< head, delpre, addpre >, whch descrbes the detaled executon process of a specfc prmtve task. The name of the planned actvty head s ntalzed by the head of the assocated operator nstance. The negatve executon effects, delpre { delevent predcate,, tp }, are a set of events that represent a negatve temporal predcate nstance, and are ntalzed by a deleted lst of nstantaneous and delayed effects n the assocated operaton nstance. The defned predcate predcate s deleted from the current state when an event delevent s trggered at tme tp durng executon. The postve executon effects, addpre { addevent predcate,, tp }, are also ntalzed by the added lst of nstantaneous and delayed effects. The defned predcate predcate s added to the current state when an event addevent s trggered at tme tp. Thus, the emergency stuaton changes durng the executon nterval of the planned actvtes. Therefore, all elements n the temporal refnng task network can be extracted from the generated search node curnode and the enhanced herarchcal task network enhetasknet n the presented decson-makng model. 6.2 Extractng the emergency response busness process The emergency response busness process represents the executon flow of the emergency response plan. In

21 Tang P., Shen G.Q.P. (2015). Decson-makng Model to Generate Novel Emergency Response Plans for Improvng Coordnaton durng Large-scale Emergences, Knowledge-based Systems, Vol 90, Pages , DOI: /.knosys , December. (SCI, 5-Year mpact factor: 3.433, Ranked 17/130 n Computer Scence, Artfcal Intellgence by JCR n 2015). the processs, the planned actvtes represent the executon process of all operaton nstances n the generated search node (Secton 2.3). The order relatonshps between the actvtes are encoded by the sem-structure STN. The process of extractng elements of the emergency response busness process s ntroduced n ths secton. The order relatonshps between tasks are defned by the sem-structure STN f ther start and end tme ponts are n the same tme pont cluster. That s, one task s ordered to another task f the end tme pont of the former task s before the start tme of the latter one. The ordered relatonshps between tasks are also defned n dfferent levels. If one task s ordered to another task, all planned actvtes achevng the former task are before those achevng the latter one. Thus, all the order relatonshps between each pars of planned actvtes are generated. In ths paper, these relatonshps are represented by a boolean matrx called adacent matrx. The th planned actvty should be executed before the th one f the element lne and row are true. The th planned actvty s called frst planned actvty f all the elements n the th lne are false. That s, the th planned actvty should be executed frst. The th planned actvty s called the last planned actvty f all the elements n the th row are false. That s, no other planed actvtes should be executed after the th planned actvty. The mmedate prevous acton plus set actplus. Prev and mmedate successor acton plus set actplus. Succ for each planned actvty plus actplus act, Prev, Succ n the emergency response busness process are ntalzed accordng to the above adacent matrx. The reachable matrx of ths adacent matrx, whch represents a planned actvty ordered to all others, s computed. Then, for each successor acton plus set actplus. Succ of each planned actvty, f there exst a planned actvty actplus n actplus. Succ s before a planned actvty actplus n actplus. Succ accordng to the reachable matrx, the plan actvty k actplus k s removed from the varable actplus. Succ. Two vrtual acton plus Start and End, whch represent the source and snk nodes respectvely, are added to the busness process. Ths addton s nspred by the workflow defnton. The varable Start. Prev s ntalzed by an empty set. The varable Start. Succ s ntalzed by all the frst planned actvtes. Moreover, the vrtual acton plus Start s added to the mmedate prevous acton plus set of all the frst acton pluses. The varable End. Succ s also ntalzed by an empty set. The varable End. Prev s ntalzed by all the last planned actvtes. Moreover, the vrtual acton plus End s added to the mmedate successor acton plus set of all last acton pluses. Hence, the executon process emergency response busness process starts executon from the vrtual acton plus Start and termnates at the vrtual acton plus End. 7. Case study and expermental results A practcal case of typhoon evacuaton and the expermental results are presented n ths secton to demonstrate the applcablty of the presented decson-makng model for provdng support to emergency command operatons n EOC. Frst, a typhoon evacuaton doman that reflects the characterstcs of emergency response s ntroduced. An emergency response plan generated by a decson-makng model for copng wth an emergency stuaton case s presented. The decson-makng model s also compared wth exstng plannng paradgms. Fnally, a set of experments are performed to show the performance of Prop-H-STN algorthm based on the sem-structured STNs embedded n the model.

22 Tang P., Shen G.Q.P. (2015). Decson-makng Model to Generate Novel Emergency Response Plans for Improvng Coordnaton durng Large-scale Emergences, Knowledge-based Systems, Vol 90, Pages , DOI: /.knosys , December. (SCI, 5-Year mpact factor: 3.433, Ranked 17/130 n Computer Scence, Artfcal Intellgence by JCR n 2015). 7.1 A typhoon evacuaton doman A typhoon s a typcal large-scale dsaster n Chna s South-East coastal areas. Gven the strong wnds and ranstorms, collaboraton and coordnaton between multple respondng organzatons are requred to acheve dentfed ncdent obectves [48]. Such obectves nclude evacuatng and settlng resdents n low-lyng communtes, controllng floods, and patrollng water conservancy facltes. In ths secton, a typhoon evacuaton doman s desgned to test the decson-makng model by nvestgatng a local ursdcton of Shenzhen n the southeast coastal regon of Chna. Fg. 5. Sketch of an urban regon n Chna's South-East coastal area Ths urban regon (Fg. 5) s n coastal area under the foot of the hlls, wth a rver flowng through t. Seven low-lyng communtes are along the sea and rver. The resdents may be n danger and should be evacuated to assgned shelters once the orders are receved. These communtes, ncludng Com-A, Com-B, Com-C, Com-D, Com-E, Com-F, and Com-G, le n locatons Loc 1, Loc 5, Loc 11, Loc 17, Loc 19, Loc 20, and Loc 21, respectvely. Resdents n Com-A and Com-B should be evacuated to Shelter-A n Loc 9. The assgned shelter of resdents n Com-C s Shelter-B n Loc 11. The shelter of Com-D and Com-E resdents les n Loc 14. The shelter of resdents n Com-F and Com-G s Shelter-D s n Loc 13. When a typhoon or ranstorm comes, emergency managers n EOC n Loc 7 should assess and dentfy the communtes n danger and evacuate resdents n these low-lyng areas to specfc shelters before the dsaster happens. Once ncdent orders are receved, task forces from multple respondng organzatons, such as polce, fre control, medcal, cvl admnstraton, and transport departments, respond and carry out specfc tasks to evacuate and settle resdents. Close collaboraton and cooperaton among the respondng organzatons are essental to acheve a coherent response to typhoon dsasters. 7.2 Expermental results of applcaton case In a smulated emergency stuaton, the typhoon wll arrve n 12 hours, the water level at Staton 1 n locaton Loc 22 s 23 meters, and the water level at Staton 2 n Loc 16 s 28 meters. Emergency managers n EOC locatng n Loc 7 dentfy two ncdent obectves by assessng the emergency stuaton. One obectve wth the prorty 1

23 Tang P., Shen G.Q.P. (2015). Decson-makng Model to Generate Novel Emergency Response Plans for Improvng Coordnaton durng Large-scale Emergences, Knowledge-based Systems, Vol 90, Pages , DOI: /.knosys , December. (SCI, 5-Year mpact factor: 3.433, Ranked 17/130 n Computer Scence, Artfcal Intellgence by JCR n 2015). descrbes that resdents n Com-A should be evacuated to the assgned shelter wthn 18 hours as the deadlne. The other obectve wth prorty 2 descrbes that resdents n Com-F should be evacuated to the assgned shelter wthn 24 hours. The emergency response plan automatcally generated by the presented decson-makng model s shown n fgure 6 and fgure 7, and acheves the dentfed ncdent obectves n the gven emergency stuaton. The temporal refnng task network of the plan s presented n fgure 6, where the obectve decomposton structure s descrbed clearly. The yellow rectangles represent compound tasks, the red ones represent prmtve tasks, and the blue ones represent planned actvtes. All the planned actvtes and order relatonshps are represented by the emergency response busness process as shown n fgure 7. Fg. 6. Temporal refnng task network of the generated emergency response plan n the typhoon evacuaton doman Fg. 7. Emergency response busness process of the emergency response plan n the typhoon evacuaton doman 7.3 Comparson of exstng HTN planners and our decson-makng model Two experments are performed to test the performance of our decson-makng model. In the frst experment, XePlanner s compared wth our decson-makng model n the typhoon evacuaton doman. In the other experment, a doman-ndependent heurstc forward channg planner Sapa [49] and the presented decson-makng model are performed on the same plannng problems n ZenotravelTme doman [50]. (1) Comparson of XePlanner and our decson model wth typhoon evacuaton doman Our decson-makng model and Xeplanner [33] are state-based forward planners appled n generatng acton plans for emergency response and can encode multple types of doman knowledge n emergency management. Comparng wth our decson-makng model, STN only defnes tasks n the lowest level of the herarchcal task network n XePlanner. Therefore, the STN underlng the herarchcal task network has a sgnfcantly smaller scale n ths planner. Gven that XePlanner and our decson-makng model are developed for the same purpose and appled n plannng for emergency response, a set of plannng problems are selected randomly n typhoon evacuaton doman to test ther performance. The plan metrc value of generated acton plans s quadruple make-span of the produced acton plan n XePlanner. The make-span of a generated acton plan s defned as the