Mostrar el registro sencillo del 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 |