A Planner-Independent Collaborative Planning Assistant

Transcription

1 Appeas in the poceedings of the 3 d intenational joint confeence on autonomous agents and multi agent systems A Planne-Independent Collaboative Planning Assistant Hyeok-Soo Kim Jonathan Gatch Institute fo Ceative Technologies Univesity of Southen Califonia Fiji Way, Maina del Rey, CA, 90292, USA kimhs@ict.usc.edu gatch@ict.usc.edu Abstact This aticle intoduces a novel appoach to the poblem of collaboative planning. We pesent a method that takes classical one-shot planning techniues - that take a fixed set of goals, initial state, and a domain theoy - and adapts them to suppot the incemental, hieachical and exploatoy natue of collaboative planning that occus between human plannes, and that multi-agent planning systems attempt to suppot. This appoach is planne-independent - in that it could be applied to any classical planning techniue - and ecasts the poblem of collaboative planning as a seach though a space of possible inputs to a classical planning system. This aticle outlines the techniue and descibes its application to the Mission Reheasal Execise, a multi-agent taining system. 1. Intoduction This pape pesents techniues to enhance the geneality and modulaity of collaboative (e.g., mixed-initiative) planning systems. By allowing human uses and planning systems to jointly develop plans, such systems incease uses tust in the end poduct as well as complement the intuitive planning skills of human expets with the supeio bookkeeping capabilities of automated planning systems. Indeed, collaboative planning systems have been applied to a numbe of significant applications including disaste planning [1], and tutoing systems fo teaching people how to plan and make decisions [14]. These systems diffe in tems of who has authoity o initiative, what knowledge is available to the agent and the high-level communicative goals of the system (e.g., tutoing goals vs. task goals) [7]. But they shae a geat deal, in paticula the need to plan, including the ability to geneate plans, povide feedbacks on the feasibility of diffeent options, monito thei execution, and eplan in esponse to unexpected events o use inteventions. These ae lage and complex systems that tightly integate a numbe of capabilities. Beyond plan geneation, they communicate with the use, often though natual language, model aspects of the use s mental state, ecognize use intentions, execute plans and monito the extenal envionment. Futhe, uses often demand flexible contol ove the planning pocess, at times micomanaging and at othe times expecting the system to handle all the details. Facilitating this close and vaying level of contol ove the planning pocess complicates the poblem of cleanly sepaating the communication module fom the planning pocess. Fo example, athe than simply accepting a goal and initial state, the planne must suppot a wide ange of possible inputs. This lack of modulaity limits the geneality of these methods and contibutes to the obsolescence of thei component technologies. This is most obvious with egad to the planning techniues that undelie these systems, aguably thei most essential component technology. Due to this tight connection between planning and communication components, most planning techniues undelying collaboative planning systems ae antiuated o udimentay. Planning techniues ae, on the othe hand, evolving apidly. A uick eview of the ecent AI Planning system competitions illustates that the top pefoming plannes ae in constant flux. Fo example, in 1998, IPP was the winne of the ADL tack and also showed good pefomance in the STRIPS tack, and HSP solved the most poblems in the STRIPS tack [12]. In 2000, IPP was eplaced by FF (faste but total ode) [4]. In 2002, MIPS solved the highest numbe of poblems in the fully automated tack, and was the only system that poduced solutions in each tack, while FF also out-pefomed its competitos in the numeic and STRIPS domains [11]. To ou knowledge, none of these techniues have been incopoated into state-of-the-at collaboative planning systems. Tying a collaboative planne to a specific planning algoithm can also significantly limit its geneality. The developes of planning systems point to the domain -independence of thei planning techniues as an agument fo thei wide applicability. Thee is a stong eason to doubt this claim. Planning pefomance vaies damatically acoss application domains. Diffeent planning systems excel on diffeent domains and some, supposedly domain-independent, plannes cannot even epesent distinctions essential fo cetain domains. Fo example, at the AI planning system competition in 2002, FF planne exhibited outstanding pefomance against its competitos in most of the Numeic and STRIPS poblems, but it didn't compete in the tempoal domains. On the othe hand, TALPlanne

2 out-pefomed its competitos in the tempoal domains, but didn't paticipate in the numeic domains [11]. We ague that planne-independence is a fa moe cucial design goal than domain-independence in the design of collaboative planning systems, though is not immediately obvious how to achieve this goal. A planne-independent system embodies the design philosophy that the planne should be a modula component that can be easily eplaced depending on the chaacteistics of the application o as impoved techniues become available. Unfotunately, due to close input a use has ove the planning pocess, planning and use-inteface modules ae tightly intetwined in collaboative planning systems. In contast, the typical inteface to conventional planning system is at the level of specifying a domain theoy, initial state and a set of goals, which is eally an inappopiate level to sepaate planning and communicative functions. The uestion we addess in this pape is what is the ight level of abstaction (what is the ight API) to sepaate the planning system fom othe modules in a collaboative planning system, specifically focusing on the connection to the use-inteaction module, which tends to have the tightest inteaction. Ou appoach is to conside the geneic planning sevices necessay to suppot collaboation, define a level of abstaction in tems of these basic sevices, and map between these sevices and the low-level API of planning systems in a planne-independent fashion. Section 2 lays out a high-level desciption of collaboative planning systems and section 3 elaboates ou basic appoach and descibes how we map fom the basic planning sevices necessay fo collaboation to low-level calls to a conventional planning system. We conclude by discussing evaluation and diection fo futue eseach in section 4 and Collaboative Planning Systems Collaboative planning systems allow a human use and an intelligent system to closely inteact duing the pocess of plan geneation, execution, and epai. Such systems have been exploed in the eseach community unde a vaiety of titles including mixed-initiative planning systems, human inteactive planning systems, planning systems with adjustable autonomy, and tutoing systems. They diffe in many espects but they shae two tightly intetwined capabilities: they must communicate with the use about the planning pocesses, and they must be able to develop and eason about plans. Hee we eview this wok in tems of teminology we will use in this aticle. 2.1 Communication The inteaction between a use and a system can be seen as a dialogue, and eseaches in this aea have been heavily influenced by linguistic theoies, egadless of whethe the system actually communicates via natual language o though a moe stylized inteface. In such systems, the inteaction is contolled by a dialogue manage and the content of the inteaction is feuently chaacteized in tems of speech acts [3], a fomulism fo chaacteizing and classifying natual language utteances. Fo example, a euest like whee is the helicopte is epesented as an infomation euest about some attibute of a domain entity. Speech acts have a cetain stuctue and impose cetain communicative obligations on the listene. Fo example, upon eceiving an infomation euest, the system is obligated to espond to the euest with some assetion. Standad speech acts include infom, ode, euest, accept, eject, and counte-popose. Depending on the system s capabilities, these ae feuently augmented with dialogue acts that manage tun-taking and shifts in initiative between the system and the use [17]. Speech acts povide an abstact stuctue, but by analyzing human-to-human collaboative planning dialogues, collaboative planning eseaches have also classified geneal featues of the content of these convesations. Human inteactive planning dialogues evolve aound aspects of planning pocess, including the development and efinement of plans, the evaluation and compaison of altenatives, the claification of featues of the envionment, the identification of plan poblems o theats, and the claification of aspects of the planning dialogues. By combining these aspects with speech acts, these systems can classify a ange of utteances. Fo example, a use might infom the system of a couse of actions, ode the system to adopt it, euest the system to develop an altenative, accept the altenative, ode its execution, etc. 2.2 Planning and Planning Sevice Reuests Speech act theoy facilitates undestanding, but to sevice such speech acts, the system must also suppot a ange of plan easoning capabilities. The classic definition of the planning poblem is simple to geneate a satisfying plan given a set of goals and initial state. Howeve, eseaches in collaboative planning ecognize this definition is fa too estictive. In tems of plan geneation, uses feuently demand tighte and incemental contol ove the planning pocess. As in many eal-wold collaboative planning domains, plans ae typically hieachical and uses inteact with the system to efine thei plans, exploe o get advice about diffeent couses of action, and eceive assistance in initiating and monitoing the execution of the plan. If a use euests a couse of actions, the system must be able to develop one. If the use euests a compaison of altenatives, the system must have the means to povide it. If the system wishes to take initiative this must be motivated by some infeences concening the planning pocess. Conside the following hypothetical intechange fom the Mission Reheasal Execise (MRE), a multi-agent taining system [14]: USER: How can I einfoce the platoon in downtown Celic?

3 SYSTEM: Thee ae two feasible options. Send two suads fowad o send one suad to secue ou oute and speed ou subseuent movement. USER: What is the disadvantage of sending two suads? SYSTEM: It will factue ou foces and limit ou futue options. USER: Send fist suad to secue the oute. SYSTEM: Si. Fist suad needs to secue the landing zone. I suggest we send foth suad. USER: We don t need to secue the landing zone anymoe. Send fist suad. This example illustates seveal planning capabilities that aen t obviously mapped to taditional planning poblems. The planning pocess is hieachical and incemental. Rathe than geneating a complete plan, the system poposes single step efinements. Rathe than geneating a single satisfying plan, multiple ualitatively diffeent options ae exploed in paallel (e.g., sending one vesus two suads). Rathe than eceiving a simple list of plan steps, the use may ask fo evaluative infomation (e.g., what is the disadvantage), and the system may take the initiative to offe advice (e.g., fist suad is peoccupied). Finally, athe than accepting a fixed goal state, planning euiements may be changed in mid-steam (e.g., as when the use dops the goal that the landing zone must be secue). Collaboative plannes typically also incopoate functions to execute plan steps, monito thei execution, and detect when plan epais ae necessay. We use the tem planning sevice euests to efe to the collection of planning capabilities like the above that ae necessay to addess a use s plan-elated speech acts and to infom the systems infeences about dialogue initiative. Collectively, planning sevice euests define an API that the undelying plan easoning system must suppot. 3. Planne-Independent Collaboative Planning Assistant (PICOPA) Figue 1 illustates ou view of how to impose geate modulaity between the planning and the communicative modules in a collaboative planning system. The key idea is to define a set of abstact planning sevice euests that can seve as a bidge between the dialogue manage and a taditional planning system. Unde this view, the uestion we must addess is how to povide a geneal and planne -independent mapping between these sevice euests and the capabilities of planning systems. Ou appoach is motivated by the in pactice efficiency of ecent planning techniues within the context of collaboative planning domains. Although planning in geneal is had (indeed, undecidable), fo suitably constained applications ecent planning techniues can, in pactice, solve the planning poblem. This is paticulaly tue fo the elatively constained popositional domain theoies used by many collaboative planning systems. Ou admittedly stong assumption in this pape is that planning is decidable and elatively efficient in pactice, though we discuss how to elax this assumption late in the pape. If plans can be geneated in milliseconds, howeve, it opens up new possibilities fo collaboative planning. One could imagine epeatedly calling a planne to solve vaiations of a planning poblem to exploe featues of a domain. Fo example, if the use wanted to conside the diffeences between evacuating injued pesonnel with a helicopte vesus an ambulance, one could simply solve the planning poblem twice - once with the domain theoy only with the helicopte-elated actions and once only with the ambulance-elated actions - and summaize the outcomes to the use. System speech act Find Refine plan plan Replies about the euests PICOPA Use Dialogue manage Check inteaction Sensing & Monitoing API Add/dop goals Envionment Use speech act Planning sevice euests Abstact plan API Domain Initial theoy state Planne API Acting Figue 1: PICOPA achitectue euest state info Goal state We build on this obsevation, showing how a moe flexible epetoie of planning sevice euests can be constucted by solving a set of suitably vaied planning poblems. By systematically vaying a taditional planne s domain theoy, initial state and goal state, the system can, by bute foce, povide planne-independent mappings between the planning sevice euests demanded by collaboative planning systems and the capabilities of taditional plannes. The planning system itself can be teated as a black box and altenative plannes could be incopoated, assuming they all take as input an initial and goal state desciption and a domain theoy consisting of a set of pimitive actions. In the emaining of this section, we descibe how PICOPA maps high-level planning sevice euests (e.g., efine the cuent plan o check inteaction) onto a seuence of taditional planning poblems. To handle such euests, the system needs a mechanism that maps them into appo-

4 piate inputs fo a classical planning system and also povides the dialogue manage intepetable feedback fom the esult of the planning system. Fist we intoduce new epesentational constucts, hieachical action sets and the cuent plan set, to facilitate this mapping. 3.1 Hieachical action set A hieachical action set is a novel domain epesentation that takes advantage of the speed of moden non-hieachical planning algoithms but etains the contol and flexibility of collaboative hieachical planning. It is an AND/OR gaph that consists of both abstact and pimitive actions and it epesents hieachical decomposition ules that efine a high-level action to a set o multiple altenative sets of lowe-level ones. Though supeficially simila to hieachical action stuctues in conventional hieachical planning systems [6], thee ae significant diffeences. An abstact action epesents an unodeed set of pimitive actions that ae potentially useful fo achieving a goal athe than a high level seuence of actions to pefom. Decomposing an abstact action coesponds to building the set of pimitive actions into seveal moe focused subsets, athe than yielding a moe detailed lowe level desciption. Fo example, conside the hieachical action set fo obtaining a shelte shown in Figue 2. Two diffeent decompositions of the oot action, ent an apatment and buy a house, subdivide the entie set of six pimitive actions to two smalle and ualitatively distinct subsets, {seaching classified ads, visiting apatments, placing deposit} and {getting a eal estate agent, getting loan pe-appoval, visiting open houses}, espectively. If one pefes to ent an apatment, the system etains the pimitive actions unde ent an apatment in the cuent domain and excludes the pimitive actions elated to buying a house. At any point in the plan efinement pocess, the cuent set of pimitive actions is passed to a non-hieachical classical planning system to check the existence of a complete plan. This pocess allows a use to hieachically constuct a plan unde his/he contol and pefeence while continually eoganizing the domain theoy to eflect to his/he choices. This hieachical action set also makes it easy to tansfom a euested planning sevice into a set of conventional planning poblems by geneating appopiate inputs, especially domain theoies, fo taditional planning systems. Fo a given domain, thee can be multiple hieachical action sets, each of which has its own goals. These goals ae used fo selecting appopiate hieachical action sets accoding to the given goals. Fo example, let s assume thee ae fou actions sets, A, B, C, and D, and each has a goal, a, b, c, and d espectively. When a use wants to geneate a plan to achieve b and d, the system will automatically select B and D as initial hieachical action sets. To avoid edundancy and ambiguity in selecting initial action sets, a set of goals associated with an action set cannot be a subset of goals of anothe action set, but two action sets can have goals in common. Hieachical action sets satisfy the downwad and the upwad solution popeties, so once an abstact solution is found all othe abstact plans can be puned away and all the descendents of any inconsistent abstact plan can be puned away as well [15]. Seaching classified ads. Rent an apt. Visiting apts. Obtaining a shelte Placing a deposit Getting a eal estate agent Buy a house Getting loan peappoval Visiting open houses Figue 2: An example of a hieachical action set fo obtaining a shelte 3.2 Cuent plan set To keep tack of development of a plan while exploing hieachical action sets and feuently changing the cuent domain theoy, the system uses an aay, the cuent plan set, to epesent the cuent plan developed so fa. Initially, it consists of the oot actions of selected hieachical action sets, such that the union of the effects of the oot actions should contain all of the use s goals. If diffeent combinations of action sets could satisfy these goals, these ae consideed potential choice points fo the use. The system fist checks whethe o not each combination has a consistent and complete solution by sending the planne a new domain theoy that consists of all the pimitive actions in each combination in tun. If the planne finds a solution with a combination, the hieachical action sets in the combination will be consideed as valid candidate hieachical action sets. The dialogue manage then infoms the suvived candidates to the use and lets him/he choose one among them. The oot actions of the selected hieachical action sets become initial membes of the cuent plan set. The cuent plan set is updated wheneve each abstact action is eplaced by one of its efinements afte checking consistency between the newly intoduced actions and the existing ones. This pocess continues until all the actions in the cuent plan set become pimitive. The above two new domain epesentations also make it possible fo uses to select thei own stategies athe than the system imposing a specific efinement stategy, such as least commitment o fewest altenative fist (FAF) [13]. The system just povides the necessay infomation to implement these stategies, i.e., existence of altenative efinements o the consistency of each efinement with the est of the cuent plan.

5 3.3 Solution matix PICOPA handles planning sevice euests by systemically solving vaiants of the cuent plan set and combining the esults in fomulating an answe to the euest. The solution matix epesents this computation. Fo example, in Figue 3, the use wishes to decompose two abstact actions, A 1 and C 2, in paallel (a use can decompose them simultaneously). PICOPA must infe which combinations of efinements ae consistent with each othe. The solution matix in Table 1 illustates how this sevice euest is tanslated into a seies of planning poblems that ae specializations of the cuent plan set. To check the consistency of each poblem, PICOPA constucts a domain theoy fo each possible combination of efinement with pimitive actions and the pimitive descendents of the abstact actions in the combination. Fo example, the fist ow coesponds to specializing A 1 into (A 111, A 112 ) and C 2 into (C 211, C 212 ), emaining B 3 and D 1. Afte examining each domain theoy by passing it to a conventional planning system along with initial and goal state, PICOPA geneates a solution matix that summaizes the esults. If the use chooses one among possible candidates, the cuent plan set is updated to eflect this choice. Table 1 shows the fist thee of fou possible combinations ae found to be consistent. If the use chooses the second one - (A 111, A 112 ) and (C 221, C 222 ) - the cuent plan set is specialized fom {A 1, B 3, C 2, D 1 } to {A 111, A 112, B 3, C 221, C 222, D 1 }. By showing if a pat of the plan has a stong connection with its othe pat (e.g., (A 121, A 122 ) is inconsistent with (C 221, C 222 ), namely, (A 121, A 122 ) should be only with (C 211, C 212 )), o if a decision limits the futue development of the plan (e.g., selecting (A 121, A 122 ) esticts to decomposing C 2 only into (C 211, C 212 )), the solution matix pevent uses fom bad decisions as well as povides useful infomation fo uses to make ight decisions. A 111 A 112 A 1 B 3 C 2 D 1 A 121 A 122 C 211 C 212 C 221 C 222 The cuent plan set { A 1, B 3, C 2, D 1 } Figue 3: Decompositions of A 1 and C 2 consistency A 1 B 3 C 2 D 1 (A 111, A 212 ) (C 211, C 212 ) All the All the Yes (A 111, A 112 ) pimitive (C 221, C 222 ) pimitive Yes (A 121, A 122 ) descendents of B 3 dents of D 1 (C 211, C 212 ) descen- Yes (A 121, A 122 ) (C 221, C 222 ) No Table 1: A solution matix (A 1 and C 2 ) 3.4 Planning sevice euests Thee ae a wide ange of planning sevice euests that ae necessay to suppot collaboative plan-elated inteactions between human uses and the system. Collectively, planning sevice euests define an API that cleanly sepaate the undelying planne fom othe system components. Seveal eseaches in the collaboative planning community have poposed paticula APIs. Fo example, Allen and Feguson fomalized a set of planning sevice euests they call them poblem solving opeations, - necessay fo collaboative planning in tems of a well defined API shown in Table 2 [2]. Hee we descibe how to map these high-level planning sevice euests into classical planning poblems in tems of hieachical action set, cuent plan set, and solution matix. In ode to facilitate the evaluation of ou appoach and easily compae it with othe elated woks, the planning sevice euests contain all the poblem solving opeations that Allen and Feguson mashaled in thei ecent pape, and seveal supplementay ones. Up to now we ve focused on decomposition. Now we ll genealize this discussion to a numbe of planning sevice euests. Intoduce/Refine objective Modify/coect goal o solution Evaluate plan Specify/Extend solution Ceate/Compae/Reject option/solution Undo opeation Cancel plan Table 2: Collaboative poblem solving opeations (Allen and Feguson) Intoduce objective: when the system gets a set of goals fom a use to geneate a plan, PICOPA fist identifies hieachical action sets elevant to the stated use goals. The union of the goals of selected hieachical action sets should contain all of the use s goals, and thei oot actions become the initial cuent plan set. In the example in Figue 4, if the given goals ae to achieve and z, then thee exist two possible hieachical action sets, {A, B} o {A, C}. Note that thee may be multiple consistent combinations of hieachical action sets that ae teated as altenatives fo the use to select amongst. Refine plan (objective): Afte a set of use goals ae intoduced and the elevant high-level tasks ae decided, the use and PICOPA should concetize the tasks until they have a satisfiable plan in an executable level by navigating the hieachical action sets fom pat to pat accoding to thei cuent concen and the degee of exigency of the tasks. When the use euests to expand an abstact action in the

6 cuent plan set, PICOPA shows the possible decompositions of the action and checks if each decomposition is consistent with emaining pat of the cuent plan. This pocess can be done by geneating planning poblems that diffe only in thei domain theoies (they shae the same initial and goal states) and sending them to the undelying planne. Each domain theoy consists of the pimitive descendents of the selected altenative and ones of the emaining actions in the cuent plan set. The use can then select one among the altenatives that passed the consistency check. Fom the example in Figue 4, let s assume that the cuent plan set is {A 11, A 12 }. If the use would like to expand A 12 fist, then the system checks the consistency of two altenative efinements of A 12 with the emaining pat in the cuent plan set (in this case, only with the step A 11 ). PICOPA constucts pimitive planning poblems fo each of these altenatives by specializing the domain theoies - {A 1111, A 1112, A 1121, A 1122, A 1211, A 1212 } and {A 1111, A 1112, A 1121, A 1122, A 1221, A 1222 }. The solution matix in Table 3 shows the consistency of the two diffeent decompositions of A 12 with the emaining plan steps. This table illustates that (A 1221, A 1222 ) is inconsistent with A 11, meaning that the use can choose only (A 1211, A 1212 ) as a decomposition of A 12. Afte the use chooses one of the consistent options, the cuent plan set is eplaced to the newly selected one, i.e., fom {A 11, A 12 } to {A 11, A 1211, A 1212 }. Initial state: p Goal state:, p t A 1111 A 1112 t A 11 p k A 1121 A 1122 m k A 1211 A 1212 m A A 12 z s p f A 1221 A 1222 A 21 A 2111 A 2112 s f p d B A 2121 A 2122 u z d A 2211 A 2212 u C A 22 f w g z A 2221 A 2222 Figue 4: Hieachical Action Sets. The peconditions appea left of the action, and the effects ight. A 11 A 12 Consistency All the pimitive (a 1211 a 1212 ) Yes descendents of A 11 (a 1221, a 1222 ) No Table 3: Solution matix fo efining A 12 Check inteaction (Compae/Evaluate options): A key difficulties in collaboative planning is detecting and communicating intedependencies between diffeent potions of the plan. Fo example, how a use decides to obtain a shelte may constain his o he options fo obtaining tanspotation due to the limited budget. A novel featue of PICOPA allows uses to systematically exploe inteactions w among altenative plan efinements by evaluating the consistency of combinations of possible efinements. Fo example, the solution matix in Table 1 evaluates the pai-wise inteactions between efinements of A 1 and C 2. The esults indicate inteactions between these tasks (how the use chooses one as a efinement of A 1 will limit his o he options fo efining C 2 ). Although the system cannot detect exactly why the inteaction occued (the planne only infoms no plan with the inputs, not why the failue occued), it povides useful feedback to the use: e.g., if you efine A 1 into A 121 and A 122, you will constain you options fo efining C 2. Depending on the time available, PICOPA can pogessively deepen its inteaction checks, initially exploing pai-wise inteactions, moving on to 3-way inteactions, etc. Modify/Coect solution (Undo opeation): In addition to efinement, the system allows non-chonological backtacking ove efinement decisions. When the system cannot develop a plan anymoe fom the cuent plan set due to some new constaints o cicumstantial changes ceated in the middle of plan geneation o when a use changes his/he pevious decision, the elated actions in the cuent plan set ae eplaced by thei paent action fo econsideation by eveting to the pevious decision point in the planning pocess, as well as any siblings that wee peviously puned away fom the hieachical action sets ae ecalled into the sets. Add o dop goal (Modify/Coect goal): Uses can add o dop goals even in the middle of plan geneation. When uses want to dop goals, it doesn t invalidate the cuent plan set, though it may now contain unnecessay plan steps. These plan steps ae automatically emoved though the pocess of eliminating unnecessay plan steps that will be explained ight afte. In the example in Figue 4, let s assume that the oiginal goals ae [, ] and the cuent plan set is {A 11, A 12 }. Afte dopping a goal, [], A 12 becomes unnecessay and is automatically emoved fom the cuent plan set. On the othe hand, if a use euests to add a goal, PICOPA finds an appopiate hieachical action set, the goals (effects) of which contain the goal desied to be added, and then adds the oot of the hieachical action set into the cuent plan set. In the example in Figue 4, if a use wants to add a goal, [g], PICOPA adds the oot action (C) to the cuent plan set. Howeve, this may cause unnecessay pocessing when the goal can be achieved fom the cuent plan without adding any additional plan steps. Fo example, let s assume the oiginal goal is [] and the cuent plan set consists of {A 11, A 12 }. If PICOPA is euested to add a new goal [], it will eoganize the cuent plan set by adding the oot action (B) that has [] as an effect. Since thee ae duplicated plan steps that achieve [], one of them, A 12 o B, should be emoved fom the cuent plan set. To pevent this poblem, wheneve the system gets a euest to add a goal, it fist checks if the system can achieve the added goal

7 fom the cuent plan set without any change. If not, then PICOPA finds an appopiate hieachical action set that contains the added goal as an effect and adds the oot actions into the cuent plan set. Thee ae two additional sevices that ae applied egadless of use euests. The following two sevices ae pefomed wheneve a cuent plan set is updated and a plan step is executed espectively. Eliminate unnecessay plan steps: In many cases, goals of one pat of the plan can be achieved fotuitously by some othe potion of the plan, in othe wods, thee may exist edundant plan steps to achieve a same goal. It may also happen that a goal can be achieved by an unexpected event in the middle of plan execution. In some othe cases, a hieachical action set may contain unnecessay actions fo a cetain cicumstance since a hieachical action set defines a vey geneal way to achieve a set of goals. Fom the above two cases, it is intuitively ealized that PICOPA is euied to detect and eliminate edundant o unnecessay plan steps so as not to poduce inefficient plans. Let s conside the following two situations in a planning poblem to build a house. Fist, a house geneally needs to be ai-conditioned and winteized. Howeve, if the use wants to build a house in Hawaii he doesn t need to spend exta money fo potection against the cold. Second, if he goes to buy a bath tub, and the stoe also sells lawn and gaden poducts, he doesn t need to go to a lawn and gaden stoe. When only a pat of the selected hieachical action set is euied fo the given poblem o a goal (sub-goal) is fotuitously achieved, it can makes a pat of the cuent plan set supefluous. PICOPA detects the existence of these plan steps, that have no pimitive descendent in the implicitly etuned plan and emove them fom the cuent plan set.. Fo example, in Figue 1, let s assume that the goal state is [] instead of [, ]. Since thee is no hieachical action set that achieves only [] the system inevitably selects {A} as an initial cuent plan set that is to additionally achieve as well as. Then, the use chooses {A 11, A 12 } as the decomposition of {A}. Since A 12 is just to achieve [], the implicitly etuned plan fom the undelying planning system doesn t contain any action that is descent of A 12. So, A 12 is identified to be unnecessay and emoved fom the cuent plan set i.e., the cuent plan set is changed fom {A 11, A 12 } to {A 11 }. Monito execution and eplan: PICOPA monitos the cuent wold to detect extenal events. If the system detects any unexpected change in the cuent wold, it examines the validity of the cuent plan with the change. When the plan is no longe consistent with the changed wold state, the system must thow out the cuent plan and egeneate a new plan fom scatch to achieve the use goals fom the new situation. Though PICOPA cannot detect what caused the plan failue because it doesn t have any knowledge about intenal planning pocess of the base planning system, it can detect if the cuent plan is valid with the changed wold by checking the inteaction between the unexpected event and the emaining pat of the plan (unexecuted plan steps). In addition to the planning sevice euests listed above, the system may meet a cicumstance that needs to efomulate domain theoies athe than econstucting domain theoies with elated actions. Specify solution: When the use wants to specify a esouce fo an action (assigning the fist suad to secue the landing zone) o pefes to execute an action befoe othe action (pacifying the cowd befoe moving the injued boy to hospital) it is necessay to efomulate the definition of the action. Fo specifying a esouce to an action, the taget vaiable in the definition of the action should be eplace by the specific instance athe than a planne instantiates the vaiable with abitay one of the possible esouces. Thee is still the difficulty of mapping a vaiable binding in an abstact level into ones in the pimitive level, but this ambiguity can be claified by additional inteactions with the use to find which pimitive actions should be assigned with the specific esouce. When uses want to post odeing constaints between actions, such a euest could be handled by adding auxiliay constaints to the base planne s domain theoy. Fo example, one way to enfoce abstact action A to occu befoe abstact action B is to add A-complete effects to each of A s set of pimitive actions and A-complete peconditions to each of B s actions. We believe that many additional euests can be coveed by simila epesentational manipulations. 4. Evaluation A pimay empiical uestion fo this wok is whethe the appoach, given its emphasis on in pactice efficiency of planning, will scale to pactical collaboative planning domains. In the MRE team taining system, a esticted plan scipting language sufficed to dive mixed-initiative inteaction [14]. We discoveed that IPP [9], a contempoay planning system, could do full plan geneation on the same domain theoy in less time than it took to find a scipted solution (typically less than 100 ms to geneate a plan fom scatch). FF [8] can also geneate a plan in less time than IPP does, but the totally odeed plan fom FF causes unnecessay odeing constaints that limit the flexibility of its execution. Futhe, ou ealy expeiments with JADE [5], a complex collaboative foce deployment planning domains, shows that IPP and FF can solve poblems in at most a couple of seconds. This is not to say that planning is a solved poblem, but fo many of the domains consideed by collaboative planning, the ecent new planning techniues ae fa moe than adeuate. With this confidence, we have been applying HICOPA to MRE, expecting impovement of planning capability as well as independency of planning component. We will then contast the coveage of API and speed against the cuent existing system. This possibility also encouages us to evaluate the HICOPA by testing it

8 with othe collaboative planning domains, such as ones in JADE o O-Plan2. Although due to the limitation in getting these systems we can only test it with thei example domains, it seems sufficient to veify ou appoach by validating that PICOPA can cove API acoss multiple domains. Finally, we will veify the adaptability and compatibility of the PICOPA with divese classical planning systems and apply it onto vaious types of planning domain. 5. Conclusions This pape pesented a collaboative plan assisting agent (PICOPA) allowing human uses and planning systems to jointly develop plans by diectly using classical AI plannes. We poposed a mapping mechanism fom planning sevice euests necessay fo collaboative inteaction into lowe-level calls to a conventional planning system by geneating appopiate inputs fo the planning system. Theefoe, PICOPA can utilize existing classical AI plannes fo collaboative planning poblems accoding to the chaacteistics of the application o as new techniues become available athe than developing a custom planne fo a specific system o application. This appoach can make classical planning eseaches elevant to the collaboative planning community. The cuent system depends on the stong assumption that the base planne can uickly eithe solve a planning poblem o detect that no solution exists. While this may be easonable fo simple domains, it could be uneasonable in geneal. We can elax this euiement by extending solution matices of PICOPA to a 3-value logic (consistent, inconsistent, unknown) whee "unknown" means that the planne could not teminate within some small pe-specified time limit. We would then have to suitably ualify the esults of planning sevice euests, making esponses, in essence, heuistics athe than definitive esults. Fo example, athe than stating that a efinement was inconsistent, PICOPA could state that it may be inconsistent. The use could incease the uality of this feedback by extending the time limit, o the system could peiodically fill in solution matices as time becomes available. PICOPA is being applied within the context of the Mission Reheasal Execise collaboative planning system, a ich vitual taining envionment that includes human tainees inteacting with gaphically embodied vitual agents that can communicate with though natual language about tactical decisions. PICOPA shows pomise to extend the planning capabilities of the exiting MRE system while at the same time allowing the planning component to be moe modula and easie to update with moe advanced planning techniues as they become available. 6. Acknowledgements This pape was developed with funds of the Depatment of the Amy unde contact numbe DAAD D Any opinions, findings and conclusions o ecommendations in this pape ae those of the authos and do not necessaily eflect the views of the Depatment of the Amy. 7. Refeences [1] J. Allen, D. Byon, M. Dzikovska, G. Feguson, L. Galescu, and A. Stent. Towads convesational human-compute inteaction., AI Magazine, 22(4): , [2] James Allen and Geoge Feguson. Human-machine collaboative planning. Poceedings of the Thid Intenational NASA Wokshop on Planning and Scheduling fo Space, Houston, TX, Octobe 27-29, [3] J. A. Austin. How to do things with wods. Havad Univesity pess, Cambidge, Massachusetts, [4] Fahiem Bacchus. Results of AIPS-2000 planning competition ul: PS-2000.ps. [5] Cox, M. T., and Veloso, M. M.. Contolling fo unexpected goals when planning in a mixed-initiative setting. In E. Costa & A. Cadoso (Eds.), Pogess in Atificial Intelligence: Eighth Potuguese Confeence on Atificial Intelligence: , Belin: Spinge, [6] Kutluhan Eol, James Hendle, and Dana S. Nau. UMCP: A sound and complete pocedue fo hieachical task-netwok planning. In AIPS-94, pages , [7] Mati A. Heast. Tends & Contovesies: Mixed-initiative inteaction. IEEE Intelligent System, 14(5):14-23, [8] Jog Hoffmann and Benhad Nebel. The FF Planning System: Fast Plan Geneation Though Heuistic Seach. Jounal of Atificial Reseach, Volume 14: pages , [9] J. Kohle, B. Nebel, J. Hoffmann, Y. Dimopoulos. Extending Planning Gaphs to an ADL subset. ECP-97: pages , Spinge LNAI 1348, [10] Hyeok-Soo Kim and Jonathan Gatch. A planne independent appoach to human inteactive planning. Eighteenth Intenational Confeence on Atificial Intelligence, Wokshop on Mixed-Initiative Intelligent Systems, Pages , [11] IPC, the esults of 2002 Intenational Planning Competition ul: [12] Dew McDemott. The 1998 Planning system competition, Atificial Intelligence, 21(2):35--55, [13] Reiko Tsuneto, Dana Nau, and James Hendle. Plan-efinement stategies and seach-space size. Poceedings of the Euopean Confeence on Planning, pages , [14] J Riclel, S Masella, J Gatch, R Hill, D Taum, and W Swatout. Towad a new geneation of vitual humans fo inteactive expeiences. IEEE Intelligent Systems 17(4):32--38, [15] Stuat Russell and Pete Novig. Atificial Intelligence A Moden Appoach. Pentice-Hall, Uppe Saddle Rive, New Jesey, 1995.

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