Coordination in Competitive Environments

Transcription

1 Proceedngs of the World Congress on Engneerng 0 Vol II, WCE 0, July - 5, 0, London, U.K. Coordnaton n Compettve Envronments I. González, S. Ibarra, J. A. Castán, J. Lara, J. Guzmán Abstract Despte several researches n autonomous agents mportant theoretcal aspects of mult-agent coordnaton has been largely untreatable. Multple cooperatng stuated agents support the promse of mproved performance and ncrease the task allocaton problems n cooperatve envronments. We present a general structure for coordnatng heterogeneous stuated agents that allows both autonomy of each agent as well as explct coordnaton of them. Such stuated agents are emboded for takng nto account ther stuaton to solve any acton. Indeed, organzatonal features have been used as metaphor to acheve hghest levels of nteractons n an agent system. Then, a decson algorthm has been developed to perform a match between the stuated agent knowledge and the requrements of an acton. Fnally, ths paper presents prelmnary results n a smulated robot soccer scenaro. Index Terms Mult-agent coordnaton, e-insttutons, Interactve Norms, Soccer Robotcs. I. INTRODUCTION OORDINATION depends on how autonomous agents C make collectve decsons to work ontly n real cooperatve envronments []. Nowadays, several researchers have proposed that autonomous agent systems are computatonal systems n whch two or more agents work together to perform some set of tasks or satsfy some set of goals. Research n mult-agent systems s then based on the assumpton that multples agents are able to solve problems more effcently than a sngle agent does []. Specal attenton has been gven to MAS developed to operate n dynamc envronments, where uncertanty and unforeseen changes can happen due to presence of other physcal representaton (.e., agents and another envronmental representatons that drectly affects the agents decsons. Such coordnaton allows agents to reach hgh levels of nteracton and ncrease ther successful decsons, mprovng the performance of complex tasks. Agents must therefore work n some way and under a wderange of condtons or constrants. In fact, an agent system wll have to be handled wth a great level of awareness because the falure of a sngle agent may cause a total degradaton of the system performance. For thus, ths paper ams to ntroduce a decson algorthm based on the electronc Insttuton (e-i features [], whch t represents the rules needed to support an agent socety. Specfcally, such algorthm uses knowledge of the agent stuaton regards to three perspectves: nteracton wth socal Manuscrpt receved March, 0. I. González s a student of the Engneerng Program at the Engneerng School at the Autonomous Unversty of Tamaulpas, Mexco (sbarram@uat.edu.mx. S. Ibarra, J. A. Castán, J. Lara and J. Guzmán are professors n the Engneerng School at the Autonomous Unversty of Tamaulpas, Mexco. ISBN: ISSN: (Prnt; ISSN: (Onlne nformaton and other relevant detals to entrust n other agents or humans; awareness representng the knowledge of the physcal body reflectng n the body s sklls; and world ncludng nformaton perceved drectly from the envronment. But each type of agent reacts to ts percepton of the envronment n dfferent ways, modfyng the overall system performance. In partcular, a match functon has been formulated to reach a sutablty rate based on the stuated agents capabltes and the actons requrements. In fact, agents can select those actons for whch they are the best qualfed. The effectveness of ths work s llustrated by developng several examples that analyze cooperatve agents behavor consderng dfferent stuatons n a real cooperatve envronment. II. PROCEDURE FOR PAPER SUBMISSION A group of stuated agents are here presented as cooperatve systems consttuted by a group of autonomous agents who must cooperate among themselves n order to reach specfc goals wthn real cooperatve envronments. When agent nteracton exsts, each element of the agent group must be able to be dfferentated from the others. These agents requre a sense of themselves as dstnct and autonomous ndvduals oblged to nteract wth others wthn cooperatve envronments (.e., they requre an agent dentfcaton [4]. Ths dentfcaton refers to the property of each agent to know who t s and what t does wthn the group. In ths sense, ths work proposes two agent classfcatons: Coach Agents (CA and Stuated Agents (SA A. Adoptng e-features In order to mtate the deology of the e-i (.e., e-i uses a set of rules to manage the acton performance n groups of agents, the paper descrbes how agents that work n temporal groups, are able to acheve collectve behavour. Such behavour s possble by usng communcaton among agents. Let us suppose a scene s as a spatal regon where a set of actons must be performed by a group of stuated agents s. s,s S s s where S {s,s,s,...,s } = S s the set of all possble Scenes. Let us defne a coach agent ca n charge of supervsng the executon of the actons n a partcular s. ca,ca G ca ca G CA CA where CA = {ca,ca,ca,...,ca n} Where CA s the set of all possble Supervsor Agents. When sa has dentfed ts s, t must clam nformaton n order to know whch actons must be acheved n such s. It s possble, then, to defne a sa as senstve to the events that happen n real cooperatve envronments based on the agent paradgm [5]. CA n WCE 0

2 Proceedngs of the World Congress on Engneerng 0 Vol II, WCE 0, July - 5, 0, London, U.K. Let us defne a stuated agent sa as an entty that has a physcal representaton on the envronment and through whch the systems can produce changes n the world. sa,sa G sa sa G SA SA where SA = {sa,sa,sa SA,...,sa SA s the set of all possble Stuated Agents. In ths sense, sa could be represented n many ways, (.e., one autonomous robot wth arms, cameras, handles, etc but for the scope of our proposal; sa s emboded for an entty that s characterzed by the consderaton of three parameters refer to: nteracton, awareness and world. In fact, the paper argues coordnaton at two meta-levels (cogntve level - supervson of the ntentons; physcal level executon of the acton n the world, where the coach agents coordnates among them to allocate of a set of actons for a group of stuated agents. Fg.. Levels of Interacton. Let us defne a norm n that s denoted as a rule that must be respected or must fx the behavour that a sa must keep at tryng to perform an acton n a s. We ndcated the concepton of a norm wthn a scene followng a set of rules such that: f (n do/dont {acton} n,n N(s n n N(s N Where N(s = {n,n,n,...,n a} Let us defne an oblgaton obl as the mposton gven to some sa to perform some acton, whch t s establshed followng a set of rules. In order to denote the noton of oblgaton obl the predcate [] s present as follows: obl(pa, ψ,s Where a sa s oblgated to do ψ n s. Cooperatve Actons Studes about whch actons are nvolved n determne scene are needed to perceve knowledge that make possble the organzaton of any determned scene. Once a coach knows n whch scene t wll develop ts functon, t must dentfy the goals to be accomplshed n such spatal regon, ndcate the tasks that must be performed to acheve these goals, and what roles are necessary for the task achevement. Then, a coach s defned n ts knowledge base KB(ca by the consderaton of a set of goals G, a set of tasks T and a set of roles R. KB(ca = G(s T(s R(s Where KB(sa s the nformaton of all the ssues regardng to a specfc scene s, such that: G(s s the set of goals, T(s s a set of all tasks, and R(s s the set of all roles nvolved n determned scene s. Indeed, t s necessary to propose a prorty ndex p that represents the mportance of every acton. A sa wll know both the order n whch the goals and the tasks must be ISBN: ISSN: (Prnt; ISSN: (Onlne n } performed and the order of the role allocaton process regardng ts supervsed s. Such prorty ndex wll be establshed accordng to system requrements (.e., tmelne n order to acheve the sa ams. Goals then embody the overall system purpose; however a ca could acheve a partcular goal wthout the necessty of performng another goal at the same s. g,g G(s g g G(s G Where G(s g G(s = {g,g p(g P,g G(s,...,g o } 0 p(g Where G s the set of all possble Goals and G(s s g nvolved n s. Let us to defne a set of tasks T whch represent the ssues that must be performed to acheve a specfc g. Goal then could be acheved wthout the mplct necessty of performng all ts nvolved tasks. Therefore, the tasks selected are ndependent, but ther development could affect n a postve or negatve way the development of other tasks. t,t T(g t t T(g T(s T Where T(g = {t,t,t t T(g p(t P T(g 0,...,t p } p(t Where T s the set of all possble Tasks. Let us to defne a set of roles R whch represent the actons that a pa must fulfl to perform a t wthn a s. r,r R(t r r R(t R(s R Where R(t r R(t p(r P = {r,r,r,...,r } R(t 0 q p(r Where R s the set of all possble Roles. In order to llustrate how ths process s performed, let us suppose a scene s whch s supervsed by the coach ca performng a decson process to defne whch goal must be attended frstly (Fg.. Fg.. The coach ca defnes whch goal must be performed frst. Embodyng Stuated Agents Suppose that a stuated agent lves n a real envronment, therefore, t has the ablty to consder ts physcal representaton n such world. Although these characterstcs could supposedly take a lot of thngs regardng the envronment our proposal takes three knds of knowledge that seek to reference all the nformaton that characterze the percepton of partcular sa. a Interacton Interacton I refers to the certanty that an agent wants to nteract wth other agents to assume a specfc behavor wth successful and hgh relablty to acheve any acton proposed wthn any determned scene. Such nformaton s WCE 0

3 Proceedngs of the World Congress on Engneerng 0 Vol II, WCE 0, July - 5, 0, London, U.K. useful n the nteracton process of the agents because they can trust n other agents based on the result of ther prevous nteractons. Obvously, f a sa has a postve performance of ts actons, ts nteracton level ncreases; but f the outcome of the acton s negatve, ts nteracton level decreases. Such knowledge s obtaned when a sa has a drect relatonshp wth a ca. sa G SA I r,s (sa I(sa Where I (sa s the nteracton level of a sa to perform r,s r n the s. b Awareness Awareness A refers to the set of physcal self-knowledge that a physcal agent has represented about ts sklls and physcal characterstcs to execute any proposed acton. Such physcal representaton s consdered as the embodment of the physcal features that consttute all the nformaton that physcal agents can nclude n ther decson-makng Physcal agents could be any physcal obect handled by an ntellgent agent (.e., an autonomous robot, a machne or an electrc devce. Such pa has features that consder ther physcal body propertes (.e., ther dynamc, ther physcal structure usually when they commt to perform some task or to assume a specfc behavour wthn a cooperatve envronment. Ths fact represents the skll of the physcal agents to know that actons wll be performed based on the knowledge of the physcal agents bodes, whch s acheved through representaton of them on a capabltes bass. pa PA A (pa A(pa Where A (pa s the Awareness of pa to perform t n the s. a World World W refers to the set of envronmental knowledge that physcal agents have to perform the proposed set of actons. Such doman representaton s consdered as the embodment of the envronment knowledge that represents all the physcal nformaton that has nfluence n the physcal agents reasonng process Let us to defne a set of world condtons that represent nformaton about emprcal knowledge of the envronmental state, such that: pa PA W (pa W(pa Where W (pa s the envronmental condton of pa to perform t n a s ; sa uses the above nformaton to know the physcal stuaton of each pa. All knowledge of a partcular pa KB(pa s then consttuted by the nformaton provded for the three modules, such that: pa KB(pa = [I(pa A(pa W(pa ] In partcular, all knowledge related to a specfc t n s s gven such that: KB(pa = [I (pa A (pa W (pa Communcaton Process The humans have a communcaton process that allows transmt nformaton or deas n a common language to make sure and relable commtments between us. Lkewse, artfcal ntellgence has several approaches showng the same process [5], [7] to explot the advantages of expressng communcaton. To accomplsh an acton, a group of agents must establsh communcaton (to coordnate them. On such coordnaton agents must converse among them to agree who s who wthn the group. Then, a communcaton wth three smple dalogues based on the KQML specfcaton s presented as follows: Re quest(sa,sa, θ,s Where sa asks to sa ts θ n the scene s Re ply(sa,sa, φ Where sa responds to sa ts decson φ based on the nformaton dspatched. Inform(sa,sa,,s δ Where sa nforms sa ts state δ n the scene s. Ths process helps to the sa to communcate among them and wth a pa. Fg.. Conversaton between the sa and sa. Otherwse, some concepts have been explaned throughout ths research work, but none of them has clarfed how a sa could decde who s the pa (or group that wll take part n the acton of ts responsble s. sa then consders an Influence Degree ID to all these actons nvolved n a s by the tupla ID(s based on the consderaton of the aforementoned parameters to generate an utlty functon that helps them n ther decson makng structure. ID(s = [d EC (s Where d EC (s, d PK (s and d TV (s are values that establsh the relevance of each parameter related to a s. These values are n the range [0,]. In ths sense, the sa responsble n s uses the KB (pa t, and the ID(s to d PK (s n s perform a match functon by means of (. match(id(s,kb(pa t,s = d TV (s ] ] = ID(s = ( KB(pa ( ID(s t,s ( A sa uses the match to determne whch pa must perform r q n a s, assgnng the hgher pa for the most pror r q n s. ( ( ISBN: ISSN: (Prnt; ISSN: (Onlne WCE 0

4 Proceedngs of the World Congress on Engneerng 0 Vol II, WCE 0, July - 5, 0, London, U.K. III. IMPLEMENTATION In our mplementaton, each physcal agent has a dfferent movement controller whch dfferentates from others. Then, we have segmented the scenaro nto three spatal regons (Fg. 5 to represent each s. Fg. 4. Emprcal example of a match process. B. Decson Algorthm An mportant crteron for the development of collectve actons wthn real cooperatve envronments s the traffc of the nformaton avalable from the percepton of the ntentons to the executon of them. We have therefore determned a partcular decson algorthm of four smple stages. Stage. Refers to the property of a sa to perceve whch s must manage, therefore, a sa then knows ts goals, tasks, roles (the prorty of every tem s also perceved and ID nvolved n ts s. Hence, the knowledge base of each sa could be acheved. Stage. All the sa (of the entre SA must organze them to defne whch wll be the order n that they could begn the recrutment of pa to perform the actons wthn ts s. For thus, the sa must converse among them usng the developed dalog (see II.5 Stage. Based on the order obtaned above, a sa s approved to starts the communcaton wth the entre PA to determne that pa wll be the selected to perform every acton. For thus, a sa must obtan the physcal knowledge of each pa by means of drectly communcaton wth they; the envronment condtons and trust value of each pa are obtaned when the sa uses the modules aforementoned (respectvely for each parameter. Once a sa completes the KB(pa of the entre PA, takes such nformaton to perform the match usng the equaton (, consderng the prorty ndex of all the roles. Then, sa has a lst detaled (form hgher to lowest coeffcent of the entre PA. After, sa knows that pa must perform that role; therefore, t s able to oblgate a determne pa to perform a role whch t represent that acton must be performed. Hence, the best pa (of the entre PA wll chose to perform the most pror role and so successvely untl fnsh wth all the roles n such s. Such process guaranty us a sutable role allocaton because the r q always wll allocated to the best pa. Indeed, a sa knows how many PA needs because t needs the same amount of PA such as R(s. Suppose that the system has enough amount of PA to take all the defned roles. To know, every sa s able to exclude a pa that presents a lowest acton capablty. Stage 4. Show-tme. A pa knows the r q that must perform. Ths nvolves physcal changes n the envronment. Now, the envronment has been modfed. So, a new consensus among the SA could be performed to adust t to the current changes n the envronment. Fg. 5. Geographc segmentaton of the expermental envronment. For sake of smplcty, we only have defned one goal per scene G(s =g ; G(s =g and G(s =g. The consensus to defne the executon order of the scenes s derved as follow (Fg. 6. Fg. 6. Supervsor Agent Consensus. Where cbp s the current ball poston on the envronment. So, the spatal regons are lmted accordng to the smulator dmensons (axs x: [0 0]; axs y:[0 80]. Moreover, specfc tasks are defned n order to accomplsh each g such that: T(g = {t,t } T(g = {t,t4} T(g = {t5,t6} Where t s make-pass, t s shootng, t s player-on, t 4 s kck-ball, t 5 s protect-ball and t 6 s coverng a poston. Followng the rule presented for the goals, the tasks also use the cbp as a reference to determne ts executon order. Then, usng the ranges above, a sa may decde the task to perform at any tme. But, to attempt to acheve such tasks a sa must defne whch roles t must perform and the prorty order of such roles. Therefore, by means of human analyss we have proposed four roles that could be used to perform any task such that: R(t = { r, r, r,r 4} Where r s go to the ball, r s kck the ball, r s cover a zone and r 4 s take a poston to be used n each t. In addton, we have performed a combnaton wth the nformaton nvolved n the envronment-based knowledge. Such combnaton s used by sa to perform the match process consderng the aforementoned parameters. Then, a bnary combnaton lets us generate eght nfluence degrees (Table I. Table I. Influence Degree Consderaton (0: s not consdered; :s consdered ISBN: ISSN: (Prnt; ISSN: (Onlne WCE 0

5 Proceedngs of the World Congress on Engneerng 0 Vol II, WCE 0, July - 5, 0, London, U.K. We present a revew to show how we have mplemented these parameters n the robot soccer testbed. Interacton here called Trust TV represents the socal relatonshp among agents takng nto account the result of past nteractons of a sa wth a pa. Equaton ( shows the trust calculaton f the am s reached. Otherwse, usng ( shows the trust calculaton f the am s not reached. tv tv tv t,s t,s (pa = tvt,s (pa + ΔA(s, σ ( (pa = tv (pa ΔP(s, ω ( Where the tv (pa [0,] and hgher (pa represent the best pa to perform t n s, ΔA(s, σ and ΔP(s, ω are the awards and punshments gven n s respectvely and σ s from to Q(s and ω s from to Q'(s ; that are the number of awards and punshments n s. Awareness here called Physcal Knowledge PK represents the knowledge of the agents about ther physcal capabltes to perform any proposed task. In partcular, the ntrospecton process s performed by usng neural networks takng nto account the knowledge that a pa has related to perform t n s. Consder that a hgh PK (pa [0,] by representng a sutable pa. World here called Envronmental Condtons EC s a value related to the dstance between the current locaton of a pa and the locaton of the ball. Equaton (4 shows the calculaton: ec t,s (pa = ( d(pa, r( / d max(s ec t,s (pa [0,] (4 Where ec (pa s the value of a pa to perform a t n s; d(pa,r(t,s s the dstance between the pa wth r(t,s and dmax(s s the maxmal dstance of all pa n s. Then, equaton (5 shows the dmax(s calculaton where m s the total number of pa n IAS. d max(s = max(d(, s,...,d(m,s d max [0,] In order to show how our approach performs the role allocaton process we present a possble stuaton (Fg. 7 where the ball s wthn the s and we use all the nfluence degrees generated to perform the pa selecton. Then, we only showed the allocaton for one acton (kck the ball. Fg. 7. Possble stuaton for the PA n the envronment. (5 In (Table II we present the values of a pa regardng to the proposed acton. In (Table III we show the match values obtaned by means of the equaton (. Then, s possble to see wll be the pa selected by the sa to perform the proposed acton. Addtonally, the remaned physcal agents follow a fx strategy whch was defned to consder actons to the entre PA. TABLE II. PHYSICAL AGENTS KNOWLEDGE BASES TABLE III. SOME EXAMPLES OF PHYSICAL AGENT SELECTION IV. RESULTS AND CONCLUSIONS We ran two expermental evaluatons to valdate the proposed approach. In partcular, n the experments our IAS uses all the bnary combnaton of the ID to perform the match process. In Exp., our IAS competed aganst a blnd opponent n 0 games. Here, the IAS performance s mproved when all the parameters are consdered. So, IAS(ID 7 shows a better average (mprovement rate: +8% better than IAS(ID 0 (any parameter consdered. Then, n the Exp., a league of 8 games was performed to confront the IAS among them. So, the IAS performance ncreases when uses ontly all the parameters. In fact, the IAS(ID 7 shows a better average (mprovement rate: +9% than IAS(ID 0. As conclusons we argue the need of agent metacoordnaton to explot the advantages of abstract the envronment knowledge (by the supervsor agents and use t to nfluence the reasonng process of the physcal agents. In addton, a combnaton (named Influence Degree descrbes the consderaton among these parameters gvng to the sa the ablty to determne a decson process to perform a match between the scene requrements and the physcal agent capabltes. In fact, the best performance s obtaned when our team agent took nto account all the parameters n ts decson process. But t s really nterestng to analyze how the cooperatve IAS performance ncreases when the system takes the parameters nto consderaton. In concluson, the stuaton matchng approach s a promsng method to be used as utlty functon between task requrements and physcal agent capabltes n MAS. In (Table IV we show some approaches regardng archtecture for mult-agent cooperaton. In partcular, these archtectures express behavor by mplementng dfferent knds of knowledge whch can be related to our approach. ISBN: ISSN: (Prnt; ISSN: (Onlne WCE 0

6 Proceedngs of the World Congress on Engneerng 0 Vol II, WCE 0, July - 5, 0, London, U.K. TABLE IV. OUR APPROACH VS OTHER APPROACHES REFERENCES [] S. Ibarra, C. Quntero, J. A.Ramon, J. Ll de la Rosa and J. Castán, PAULA: Mult-agent Archtecture for Coordnaton to Intellgent Agent Systems, Proc. In. European Control Conference (ECC 07, Kos, Greece -5 July, 007. [] D. Jung and A. Zelnsky, An Archtecture for Dstrbuted Cooperatve-Plannng n a Behavour-based Mult-robot System, Journal of Robotcs & Autonomous Systems (RA&S, specal ssue on Feld & Servce Robotcs, vol 6, 999, pp [] M. Esteva, J.A. Rodríguez, C. Serra, J.L. Arcos, On the Formal Specfcaton of Electronc Insttutons, In. Agent Medated Electronc Commerce, The European AgentLnk Perspectve, Sprng-Verlag, 00, pp [4] A. Oller, DPA: Archtecture for Co-operatve Dynamcal Physcal Agents, Dept. d Engnyera Electrca, Electronca Automatca, Unverstat Rovra I Vrgl, Lecture Notes n Computer Scence. [5] C.G. Quntero, J.Ll. de la Rosa, J. Vehí, Physcal Intellgent Agents Capabltes Management for Sure Commtments n a Collaboratve World, Fronter n Artfcal Intellgence and Applcatons, IOS Press, ISBN I , ISSN , 004, pp [6] C.G. Qunero, J. Zubelzu, J.A. Ramon, J. Ll. de la Rosa, Improvng the Decson Makng Structure about Commtments among Physcal Intellgent Agents n a Collaboratve World, In. Proc. of V Workshop on Physcal Agents, ISBN , Grona, Span, 004, pp. 9-. [7] C.G. Quntero, J. Ll. de la Rosa, J. Vehí, Self-knowledge based on the Atomc Capabltes Concept. A Perspectve to Acheve Sure Commtments among Physcal Agents, nd Internatonal Conference on Informatcs n Control Automaton and Robotcs, Barcelona, Span, 005. [8] R. S. Aylett and D. P. Barnes, A Mult-robot Archtecture for Planetary Rovers, Centre for Vrtual Envronments, Departament Electronc & Electrcal Engneerng, Unversty of Salford. [9] R. Smmons, T. Smth, M. Bernardne, D. Goldberg, D. Hershberger, A. Stentz, R. Zlot, A Layered Archtecture for Coordnaton of Moble Robots, Robotcs Insttute, Carnege Mellon Unversty, Mult-robot Systems: From Swarms to Intellgent Automata p. Kluwer, 00. [0] Langley Pat, An Adaptatve Archtecture for Physcal Agents, IEEE /WIC/ACM Internatonal Conference on Intellgent Agent Technology, pp. 8-5, 005. ISBN: [] Busquets D., López de Màntaras R., Serra C., Detterch T.G., A mult-agent archtecture ntegratng learnng and fuzzy technques for landmark-based robot navgaton, Lecture Notes In Comp. Scence; vol. 504., Proc. of 5th Catalonan Conference on AI. Topc n Artfcal Intellgence. pp ISBN: [] Ibarra S., Quntero C., Ramon J., De la Rosa J. LL. & Castan J., Studes about Mult-agent teamwork Coordnaton n the Robot Soccer Envronment, Proc. In th Fra Robot World Congress 006, pp. 6-67, ISBN: [] Federaton of Internatonal Robosoccer Assocaton (FIRA. SmuroSot smulator avalable from the web: [4] B. Duffy, Robots Socal Embodment n Autonomous Moble Robotcs, Int. J. of Advanced Robotc Systems, vol., no., pp ISSN [5] Russell S. & Norvng P., Artfcal Intellgence: A Modern Approach, ISBN: [6] Farnell A., Iocch L. and Nard D., Multrobot Systems: A Classfcaton Focused on Coordnaton, Int. IEEE Transacton on Systems, Man. And Cybernetcs, part B: Cybernetcs, vol. 4, no. 5, October 004. [7] Luck M., McNurne P., Shehory O. and Wllmott S. Agent Technology: Computng as Interacton, A Roadmap for Agent Based Computng, pp. -. ISBN: ISSN: (Prnt; ISSN: (Onlne WCE 0