- -

A Non-cooperative Game-Theoretic Approach for Conflict Resolution in Multi-agent Planning

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

A Non-cooperative Game-Theoretic Approach for Conflict Resolution in Multi-agent Planning

Mostrar el registro sencillo del ítem

Ficheros en el ítem

dc.contributor.author Jordán, Jaume es_ES
dc.contributor.author Torreño Lerma, Alejandro es_ES
dc.contributor.author de Weerdt, Mathijs es_ES
dc.contributor.author Onaindia De La Rivaherrera, Eva es_ES
dc.date.accessioned 2022-07-20T18:06:00Z
dc.date.available 2022-07-20T18:06:00Z
dc.date.issued 2021-02 es_ES
dc.identifier.issn 0926-2644 es_ES
dc.identifier.uri http://hdl.handle.net/10251/184570
dc.description.abstract [EN] This paper presents FENOCOP, a game-theoretic approach for solving non-cooperative planning problems that involve a set of self-interested agents. Each agent wants to execute its own plan in a shared environment but the plans may be rendered infeasible by the appearance of potential conflicts; agents are willing to coordinate their plans in order to avoid conflicts during a joint execution. In order to attain a conflict-free combination of plans, agents must postpone the execution of some of their actions, which negatively affects their individual utilities. FENOCOP is a two-level game approach: the General Game selects a Nash equilibrium among several combinations of plans, and the Scheduling Game generates, for a combination of plans, an executable outcome by introducing delays in the agents¿ plans. For the Scheduling Game, we developed two algorithms that return a Pareto optimal and fair equilibrium from which no agent would be willing to deviate. es_ES
dc.description.sponsorship This work is supported by the Spanish MINECO project TIN2017-88476-C2-1-R. Jaume Jordan is funded by grant APOSTD/2018/010 of Generalitat Valenciana - Fondo Social Europeo and by UPV PAID-06-18 project. es_ES
dc.language Inglés es_ES
dc.publisher Springer-Verlag es_ES
dc.relation.ispartof Group Decision and Negotiation es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Planning es_ES
dc.subject Multi-agent planning es_ES
dc.subject Game theory es_ES
dc.subject Nash equilibrium es_ES
dc.subject Pareto optimal es_ES
dc.subject Fairness es_ES
dc.subject.classification LENGUAJES Y SISTEMAS INFORMATICOS es_ES
dc.title A Non-cooperative Game-Theoretic Approach for Conflict Resolution in Multi-agent Planning es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1007/s10726-020-09703-0 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2017-88476-C2-1-R/ES/RECONOCIMIENTO DE ACTIVIDADES Y PLANIFICACION AUTOMATICA PARA EL DISEÑO DE ASISTENTES INTELIGENTES/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/UPV//PAID-06-18/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/GENERALITAT VALENCIANA//APOSTD%2F2018%2F010//CONTRATACION DE INVESTIGADOR POSTDOCTORAL GVA-JORDAN PRUNERA. PROYECTO: TECNOLOGIAS INTELIGENTES PARA OPTIMIZACION DE FLOTAS URBANAS DE VEHICULOS ELECTRICOS. / es_ES
dc.relation.projectID info:eu-repo/grantAgreement/UPV-VIN//SP20180184//Técnicas inteligentes para optimización de la localización de estaciones de recarga de vehículos eléctricos y mejora de la movilidad en ciudades/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació es_ES
dc.description.bibliographicCitation Jordán, J.; Torreño Lerma, A.; De Weerdt, M.; Onaindia De La Rivaherrera, E. (2021). A Non-cooperative Game-Theoretic Approach for Conflict Resolution in Multi-agent Planning. Group Decision and Negotiation. 30(1):7-41. https://doi.org/10.1007/s10726-020-09703-0 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1007/s10726-020-09703-0 es_ES
dc.description.upvformatpinicio 7 es_ES
dc.description.upvformatpfin 41 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 30 es_ES
dc.description.issue 1 es_ES
dc.relation.pasarela S\418125 es_ES
dc.contributor.funder GENERALITAT VALENCIANA es_ES
dc.contributor.funder AGENCIA ESTATAL DE INVESTIGACION es_ES
dc.contributor.funder UNIVERSIDAD POLITECNICA DE VALENCIA es_ES
dc.contributor.funder Universitat Politècnica de València es_ES
dc.description.references Blum A, Furst ML (1997) Fast planning through planning graph analysis. Artif Intell 90(1–2):281–300 es_ES
dc.description.references Bowling MH, Jensen RM, Veloso MM (2003) A formalization of equilibria for multiagent planning. In: Proceedings of the 18th international joint conference on artificial intelligence (IJCAI), pp 1460–1462 es_ES
dc.description.references Brafman RI, Domshlak C, Engel Y, Tennenholtz M. Planning games. In: Proceedings of the 21st international joint conference on artificial intelligence (IJCAI), pp 73–78 (2009) es_ES
dc.description.references Brandt F, Conitzer V, Endriss U, Lang J, Procaccia AD (eds) (2016) Handbook of computational social choice. Cambridge University Press, Cambridge es_ES
dc.description.references Buzing P, Mors AT, Valk J, Witteveen C (2006) Coordinating self-interested planning agents. Auton Agents Multi-Agent Syst 12(2):199–218. https://doi.org/10.1007/s10458-005-6104-4 es_ES
dc.description.references Chevaleyre Y, Dunne PE, Endriss U, Lang J, LemaÎtre M, Maudet N, Padget J, Phelps S, Rodríguez-Aguilar JA, Sousa P (2006) Issues in multiagent resource allocation. Informatica 30(1):3–31 es_ES
dc.description.references Crosby M, Rovatsos M (2011) Heuristic multiagent planning with self-interested agents. In: Proceedings of the 10th international conference on autonomous agents and multiagent systems (AAMAS), vol 1–3, pp 1213–1214 es_ES
dc.description.references Endriss U, Maudet N, Sadri F, Toni F (2006) Others: negotiating socially optimal allocations of resources. J Artif Intell Res 25:315–348 es_ES
dc.description.references Fikes R, Nilsson N (1971) STRIPS: A new approach to the application of theorem proving to problem solving. Artif Intell 2(3):189–208 es_ES
dc.description.references Gal K, Procaccia AD, Mash M, Zick Y (2018) Which is the fairest (rent division) of them all? Commun ACM 61(2):93–100 es_ES
dc.description.references Gerevini A, Serina I (2002) Lpg: a planner based on local search for planning graphs with action costs. In: Proceedings of the 6th international conference on artificial intelligence planning systems. AAAI Press, pp 13–22. http://dl.acm.org/citation.cfm?Id=3036884.3036887 es_ES
dc.description.references Ghallab M, Nau D, Traverso P (2004) Automated planning: theory & practice. Elsevier, Amsterdam es_ES
dc.description.references Gillies DB (1959) Solutions to general non-zero-sum games. Contrib Theory Games 4(40):47–85 es_ES
dc.description.references Jensen RM, Veloso MM, Bowling MH (2001) Obdd-based optimistic and strong cyclic adversarial planning. In: Proceedings of the 6th European conference on planning, pp 265–276 es_ES
dc.description.references Jordán J, Onaindía E (2015) Game-theoretic approach for non-cooperative planning. In: Proceedings of the 29th AAAI conference on artificial intelligence (AAAI), pp 1357–1363 es_ES
dc.description.references Komenda A, Stolba M, Kovacs DL (2016) The international competition of distributed and multiagent planners (CoDMAP). AI Mag 37(3):109–115 es_ES
dc.description.references Larbi RB, Konieczny S, Marquis P (2007) Extending classical planning to the multi-agent case: a game-theoretic approach. In: Symbolic and quantitative approaches to reasoning With uncertainty, 9th European conference, ECSQARU, pp 731–742 es_ES
dc.description.references McKelvey RD, McLennan AM, Turocy TL (2014) Gambit: software tools for game theory, version 13.1.2. http://www.gambit-project.org. Accessed 2 Oct 2015 es_ES
dc.description.references Myerson RB (1981) Utilitarianism, egalitarianism, and the timing effect in social choice problems. Econometrica 49(4):883–897 es_ES
dc.description.references Myerson RB (2013) Game theory. Harvard University Press, Cambridge es_ES
dc.description.references Nguyen N, Katarzyniak R (2009) Actions and social interactions in multi-agent systems. Knowl Inf Syst 18(2):133–136 es_ES
dc.description.references Nissim R, Brafman R I (2013) Cost-optimal planning by self-interested agents. In: Proceedings of the 27th AAAI conference on artificial intelligence, pp 732–738. http://www.aaai.org/ocs/index.php/AAAI/AAAI13/paper/view/6384 es_ES
dc.description.references Osborne MJ, Rubinstein A (1994) A course in game theory. MIT Press, Cambridge es_ES
dc.description.references Rawls J (1971) A theory of justice. Harvard University Press, Harvard Paperback es_ES
dc.description.references Sailer F, Buro M, Lanctot M (2007) Adversarial planning through strategy simulation. In: 2007 IEEE symposium on computational intelligence and games, pp 80–87. https://doi.org/10.1109/CIG.2007.368082 es_ES
dc.description.references Shoham Y, Leyton-Brown K (2009) Multiagent systems: algorithmic, game-theoretic, and logical foundations. Cambridge University Press, Cambridge es_ES
dc.description.references Torreño A, Onaindia E, Komenda A, Stolba M (2018) Cooperative multi-agent planning: a survey. ACM Comput Surv 50(6):84:1–84:32 es_ES
dc.description.references Torreño A, Onaindia E, Sapena Ó (2014) FMAP: distributed cooperative multi-agent planning. Appl Intell 41(2):606–626 es_ES
dc.description.references Van Der Krogt R, De Weerdt M (2005) Self-interested planning agents using plan repair. In: ICAPS 2005 workshop on multiagent planning and scheduling, pp 36–44 es_ES
dc.description.references Von Neumann J, Morgenstern O (2007) Theory of games and economic behavior. Princeton University Press, Princeton es_ES
dc.description.references Weerdt MD, Clement B (2009) Introduction to planning in multiagent systems. Multiagent Grid Syst 5(4):345–355 es_ES


Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem