Argumentation-based Reasoning about Plans, Maintenance Goals and Norms

Peer reviewedPostprin

A flexible coupling approach to multi-agent planning under incomplete information

The final publication is available at Springer via http://dx.doi.org/10.1007/s10115-012-0569-7Multi-agent planning (MAP) approaches are typically oriented at solving loosely coupled problems, being ineffective to deal with more complex, strongly related problems. In most cases, agents work under complete information, building complete knowledge bases. The present article introduces a general-purpose MAP framework designed to tackle problems of any coupling levels under incomplete information. Agents in our MAP model are partially unaware of the information managed by the rest of agents and share only the critical information that affects other agents, thus maintaining a distributed vision of the task. Agents solve MAP tasks through the adoption of an iterative refinement planning procedure that uses single-agent planning technology. In particular, agents will devise refinements through the partial-order planning paradigm, a flexible framework to build refinement plans leaving unsolved details that will be gradually completed by means of new refinements. Our proposal is supported with the implementation of a fully operative MAP system and we show various experiments when running our system over different types of MAP problems, from the most strongly related to the most loosely coupled.This work has been partly supported by the Spanish MICINN under projects Consolider Ingenio 2010 CSD2007-00022 and TIN2011-27652-C03-01, and the Valencian Prometeo project 2008/051.Torreño Lerma, A.; Onaindia De La Rivaherrera, E.; Sapena Vercher, O. (2014). A flexible coupling approach to multi-agent planning under incomplete information. Knowledge and Information Systems. 38:141-178. https://doi.org/10.1007/s10115-012-0569-7S14117838Argente E, Botti V, Carrascosa C, Giret A, Julian V, Rebollo M (2011) An abstract architecture for virtual organizations: the THOMAS approach. Knowl Inf Syst 29(2):379–403Barrett A, Weld DS (1994) Partial-order planning: evaluating possible efficiency gains. Artif Intell 67(1):71–112Belesiotis A, Rovatsos M, Rahwan I (2010) Agreeing on plans through iterated disputes. In: Proceedings of the 9th international conference on autonomous agents and multiagent systems. pp 765–772Bellifemine F, Poggi A, Rimassa G (2001) JADE: a FIPA2000 compliant agent development environment. In: Proceedings of the 5th international conference on autonomous agents (AAMAS). ACM, pp 216–217Blum A, Furst ML (1997) Fast planning through planning graph analysis. Artif Intell 90(1–2):281–300Boutilier C, Brafman R (2001) Partial-order planning with concurrent interacting actions. J Artif Intell Res 14(105):136Brafman R, Domshlak C (2008) From one to many: planning for loosely coupled multi-agent systems. In: Proceedings of the 18th international conference on automated planning and scheduling (ICAPS). pp 28–35Brenner M, Nebel B (2009) Continual planning and acting in dynamic multiagent environments. J Auton Agents Multiag Syst 19(3):297–331Coles A, Coles A, Fox M, Long D (2010) Forward-chaining partial-order planning. In: Proceedings of the 20th international conference on automated planning and scheduling (ICAPS). pp 42–49Coles A, Fox M, Long D, Smith A (2008) Teaching forward-chaining planning with JavaFF. In: Colloquium on AI education, 23rd AAAI conference on artificial intelligenceCox J, Durfee E, Bartold T (2005) A distributed framework for solving the multiagent plan coordination problem. In: Proceedings of the 4th international joint conference on autonomous agents and multiagent systems (AAMAS). ACM, pp 821–827de Weerdt M, Clement B (2009) Introduction to planning in multiagent systems. Multiag Grid Syst 5(4):345–355Decker K, Lesser VR (1992) Generalizing the partial global planning algorithm. Int J Coop Inf Syst 2(2):319–346desJardins M, Durfee E, Ortiz C, Wolverton M (1999) A survey of research in distributed continual planning. AI Mag 20(4):13–22Doshi P (2007) On the role of interactive epistemology in multiagent planning. In: Artificial intelligence and, pattern recognition. pp 208–213Dréo J, Savéant P, Schoenauer M, Vidal V (2011) Divide-and-evolve: the marriage of descartes and darwin. In: Proceedings of the 7th international planning competition (IPC). Freiburg, GermanyDurfee EH (2001) Distributed problem solving and planning. In: Multi-agents systems and applications: selected tutorial papers from the 9th ECCAI advanced course (ACAI) and agentLink’s third European agent systems summer school (EASSS), vol LNAI 2086. Springer, pp 118–149Durfee EH, Lesser V (1991) Partial global planning: a coordination framework for distributed hypothesis formation. IEEE Trans Syst Man Cybern Special Issue Distrib Sens Netw 21(5):1167–1183Ephrati E, Rosenschein JS (1996) Deriving consensus in multiagent systems. Artif Intell 87(1–2):21–74Fikes R, Nilsson N (1971) STRIPS: a new approach to the application of theorem proving to problem solving. Artif Intell 2(3):189–208Fogués R, Alberola J, Such J, Espinosa A, Garcia-Fornes A (2010) Towards dynamic agent interaction support in open multiagent systems. In: Proceedings of the 2010 conference on artificial intelligence research and development: proceedings of the 13th international conference of the Catalan association for artificial intelligence’. IOS Press, pp 89–98Gerevini A, Long D (2006) Preferences and soft constraints in PDDL3. In: ICAPS workshop on planning with preferences and soft constraints, vol 6. Citeseer, pp 46–53Ghallab M, Howe A, Knoblock C, McDermott D, Ram A, Veloso M, Weld D, Wilkins D (1998) PDDL-the Planning Domain Definition Language. In: AIPS-98 planning committeeGmytrasiewicz P, Doshi P (2005) A framework for sequential planning in multi-agent settings. J Artif Intell Res 24:49–79Haslum P, Jonsson P (1999) Some results on the complexity of planning with incomplete information. In: Proceedings of the 5th European conference on, planning (ECP). pp 308–318Helmert M (2006) The fast downward planning system. J Artif Intell Res 26(1):191–246Hoffmann J, Nebel B (2001) The FF planning system: fast planning generation through heuristic search. J Artif Intell Res 14:253–302Jonsson A, Rovatsos M (2011) Scaling up multiagent planning: a best-response approach. In: Proceedings of the 21st international conference on automated planning and scheduling (ICAPS). AAAI, pp 114–121Kambhampati S (1997) Refinement planning as a unifying framework for plan synthesis. AI Mag 18(2):67–97Kaminka GA, Pynadath DV, Tambe M (2002) Monitoring teams by overhearing: a multi-agent plan-recognition approach. J Artif Intell Res 17:83–135Kone M, Shimazu A, Nakajima T (2000) The state of the art in agent communication languages. Knowl Inf Syst 2(3):259–284Kovacs DL (2011) Complete BNF description of PDDL3.1. Technical reportKraus S (1997) Beliefs, time and incomplete information in multiple encounter negotiations among autonomous agents. Ann Math Artif Intell 20(1–4):111–159Kumar A, Zilberstein S, Toussaint M (2011) Scalable multiagent planning using probabilistic inference. In: Proceedings of the 22nd international joint conference on artificial intelligence (IJCAI)’. Barcelona, Spain, pp 2140–2146Kvarnström J. (2011) Planning for loosely coupled agents using partial order forward-chaining. In: Proceedings of the 21st international conference on automated planning and scheduling (ICAPS). AAAI, pp 138–145Lesser V, Decker K, Wagner T, Carver N, Garvey A, Horling B, Neiman D, Podorozhny R, Prasad M, Raja A et al (2004) Evolution of the GPGP/TAEMS domain-independent coordination framework. Auton Agents Multi Agent Syst 9(1):87–143Lipovetzky N, Geffner H (2011) Searching for plans with carefully designed probes. In: Proceedings of the 21th international conference on automated planning and scheduling (ICAPS)Micacchi C, Cohen R (2008) A framework for simulating real-time multi-agent systems. Knowl Inf Syst 17(2):135–166Nguyen N, Katarzyniak R (2009) Actions and social interactions in multi-agent systems. Knowl Inf Syst 18(2):133–136Nguyen X, Kambhampati S (2001) Reviving partial order planning. In: Proceedings of the 17th international joint conference on artificial intelligence (IJCAI). Morgan Kaufmann, pp 459–464Nissim R, Brafman R, Domshlak C (2010) A general, fully distributed multi-agent planning algorithm. In: Proceedings of the 9th international conference on autonomous agents and multiagent systems (AAMAS). pp 1323–1330Pajares S, Onaindia E (2012) Defeasible argumentation for multi-agent planning in ambient intelligence applications. In: Proceedings of the 11th international conference on autonomous agents and multiagent systems (AAMAS) pp 509–516Paolucci M, Shehory O, Sycara K, Kalp D, Pannu A (2000) A planning component for RETSINA agents. Intelligent Agents VI. Agent Theories Architectures, and Languages pp 147–161Parsons S, Sierra C, Jennings N (1998) Agents that reason and negotiate by arguing. J Logic Comput 8(3):261Penberthy J, Weld D (1992) UCPOP: a sound, complete, partial order planner for ADL. In: Proceedings of the 3rd international conference on principles of knowledge representation and reasoning (KR). Morgan Kaufmann, pp 103–114Richter S, Westphal M (2010) The LAMA planner: guiding cost-based anytime planning with landmarks. J Artif Intell Res 39(1):127–177Sycara K, Pannu A (1998) The RETSINA multiagent system (video session): towards integrating planning, execution and information gathering. In: Proceedings of the 2nd international conference on autonomous agents (Agents). ACM, pp 350–351Tambe M (1997) Towards flexible teamwork. J Artif Intell Res 7:83–124Tang Y, Norman T, Parsons S (2010) A model for integrating dialogue and the execution of joint plans. Argumentation in multi-agent systems, pp 60–78Tonino H, Bos A, de Weerdt M, Witteveen C (2002) Plan coordination by revision in collective agent based systems. Artif Intell 142(2):121–145Van Der Krogt R, De Weerdt M (2005), Plan repair as an extension of planning. In: Proceedings of the 15th international conference on automated planning and scheduling (ICAPS). pp 161–170Weld D (1994) An introduction to least commitment planning. AI Mag 15(4):27Weld D (1999) Recent advances in AI planning. AI Mag 20(2):93–123Wilkins D, Myers K (1998) A multiagent planning architecture. In: Proceedings of the 4th international conference on artificial intelligence planning systems (AIPS), pp 154–162Wu F, Zilberstein S, Chen X (2011) Online planning for multi-agent systems with bounded communication. Artif Intell 175(2):487–511Younes H, Simmons R (2003) VHPOP: versatile heuristic partial order planner. J Artif Intell Res 20: 405–430Zhang J, Nguyen X, Kowalczyk R (2007) Graph-based multi-agent replanning algorithm. In: Proceedings of the 6th conference on autonomous agents and multiagent systems (AAMAS

Design and implementation of a Multi-Agent Planning System

This work introduces the design and implementation of a Multi-Agent Planning framework, in which a set of agents work jointly in order to devise a course of action to solve a certain planning problem.Torreño Lerma, A. (2011). Design and implementation of a Multi-Agent Planning System. http://hdl.handle.net/10251/15358Archivo delegad

Diseño e implementación de un sistema de planificación distribuido

Torreño Lerma, A. (2012). Diseño e implementación de un sistema de planificación distribuido. http://hdl.handle.net/10251/14760.Archivo delegad

Agreeing on plans through iterated disputes

Autonomous agents transcend their individual capabilities by cooperating towards achieving shared goals. The differ-ent viewpoints agents have on the environment cause dis-agreements about the anticipated effects of plans. Reaching agreement requires the resolution of such inconsistencies and the alignment of the agents ’ viewpoints. We present a dialogue protocol that enables agents to dis-cuss candidate plans and reach agreements. The dialogue is based on an argumentation process in the language of sit-uation calculus. Agreement is reached through persuasion, thereby aligning the planning beliefs of the agents. We describe our abstract iterated dialogue protocol, and extend it for the specific problem of arguing about plans. We show that our method always terminates and produces sound results. Furthermore, we detail a set of extensions to simplify reasoning and reduce the exchanged information

Cooperative planning in multi-agent systems

Tesis por compendio[EN] Automated planning is a centralized process in which a single planning entity, or agent, synthesizes a course of action, or plan, that satisfies a desired set of goals from an initial situation. A Multi-Agent System (MAS) is a distributed system where a group of autonomous agents pursue their own goals in a reactive, proactive and social way. Multi-Agent Planning (MAP) is a novel research field that emerges as the integration of automated planning in MAS. Agents are endowed with planning capabilities and their mission is to find a course of action that attains the goals of the MAP task. MAP generalizes the problem of automated planning in domains where several agents plan and act together by combining their knowledge, information and capabilities. In cooperative MAP, agents are assumed to be collaborative and work together towards the joint construction of a competent plan that solves a set of common goals. There exist different methods to address this objective, which vary according to the typology and coordination needs of the MAP task to solve; that is, to which extent agents are able to make their own local plans without affecting the activities of the other agents. The present PhD thesis focuses on the design, development and experimental evaluation of a general-purpose and domain-independent resolution framework that solves cooperative MAP tasks of different typology and complexity. More precisely, our model performs a multi-agent multi-heuristic search over a plan space. Agents make use of an embedded search engine based on forward-chaining Partial Order Planning to successively build refinement plans starting from an initial empty plan while they jointly explore a multi-agent search tree. All the reasoning processes, algorithms and coordination protocols are fully distributed among the planning agents and guarantee the preservation of the agents' private information. The multi-agent search is guided through the alternation of two state-based heuristic functions. These heuristic estimators use the global information on the MAP task instead of the local projections of the task of each agent. The experimental evaluation shows the effectiveness of our multi-heuristic search scheme, obtaining significant results in a wide variety of cooperative MAP tasks adapted from the benchmarks of the International Planning Competition.[ES] La planificación automática es un proceso centralizado en el que una única entidad de planificación, o agente, sintetiza un curso de acción, o plan, que satisface un conjunto deseado de objetivos a partir de una situación inicial. Un Sistema Multi-Agente (SMA) es un sistema distribuido en el que un grupo de agentes autónomos persiguen sus propias metas de forma reactiva, proactiva y social. La Planificación Multi-Agente (PMA) es un nuevo campo de investigación que surge de la integración de planificación automática en SMA. Los agentes disponen de capacidades de planificación y su propósito consiste en generar un curso de acción que alcance los objetivos de la tarea de PMA. La PMA generaliza el problema de planificación automática en dominios en los que diversos agentes planifican y actúan conjuntamente mediante la combinación de sus conocimientos, información y capacidades. En PMA cooperativa, se asume que los agentes son colaborativos y trabajan conjuntamente para la construcción de un plan competente que resuelva una serie de objetivos comunes. Existen distintos métodos para alcanzar este objetivo que varían de acuerdo a la tipología y las necesidades de coordinación de la tarea de PMA a resolver; esto es, hasta qué punto los agentes pueden generar sus propios planes locales sin afectar a las actividades de otros agentes. La presente tesis doctoral se centra en el diseño, desarrollo y evaluación experimental de una herramienta independiente del dominio y de propósito general para la resolución de tareas de PMA cooperativa de distinta tipología y nivel de complejidad. Particularmente, nuestro modelo realiza una búsqueda multi-agente y multi-heurística sobre el espacio de planes. Los agentes hacen uso de un motor de búsqueda embebido basado en Planificación de Orden Parcial de encadenamiento progresivo para generar planes refinamiento de forma sucesiva mientras exploran conjuntamente el árbol de búsqueda multiagente. Todos los procesos de razonamiento, algoritmos y protocolos de coordinación están totalmente distribuidos entre los agentes y garantizan la preservación de la información privada de los agentes. La búsqueda multi-agente se guía mediante la alternancia de dos funciones heurísticas basadas en estados. Estos estimadores heurísticos utilizan la información global de la tarea de PMA en lugar de las proyecciones locales de la tarea de cada agente. La evaluación experimental muestra la efectividad de nuestro esquema de búsqueda multi-heurístico, que obtiene resultados significativos en una amplia variedad de tareas de PMA cooperativa adaptadas a partir de los bancos de pruebas de las Competición Internacional de Planificación.[CA] La planificació automàtica és un procés centralitzat en el que una única entitat de planificació, o agent, sintetitza un curs d'acció, o pla, que satisfau un conjunt desitjat d'objectius a partir d'una situació inicial. Un Sistema Multi-Agent (SMA) és un sistema distribuït en el que un grup d'agents autònoms persegueixen les seues pròpies metes de forma reactiva, proactiva i social. La Planificació Multi-Agent (PMA) és un nou camp d'investigació que sorgeix de la integració de planificació automàtica en SMA. Els agents estan dotats de capacitats de planificació i el seu propòsit consisteix en generar un curs d'acció que aconseguisca els objectius de la tasca de PMA. La PMA generalitza el problema de planificació automàtica en dominis en què diversos agents planifiquen i actúen conjuntament mitjançant la combinació dels seus coneixements, informació i capacitats. En PMA cooperativa, s'assumeix que els agents són col·laboratius i treballen conjuntament per la construcció d'un pla competent que ressolga una sèrie d'objectius comuns. Existeixen diferents mètodes per assolir aquest objectiu que varien d'acord a la tipologia i les necessitats de coordinació de la tasca de PMA a ressoldre; és a dir, fins a quin punt els agents poden generar els seus propis plans locals sense afectar a les activitats d'altres agents. La present tesi doctoral es centra en el disseny, desenvolupament i avaluació experimental d'una ferramenta independent del domini i de propòsit general per la resolució de tasques de PMA cooperativa de diferent tipologia i nivell de complexitat. Particularment, el nostre model realitza una cerca multi-agent i multi-heuristica sobre l'espai de plans. Els agents fan ús d'un motor de cerca embegut en base a Planificació d'Ordre Parcial d'encadenament progressiu per generar plans de refinament de forma successiva mentre exploren conjuntament l'arbre de cerca multiagent. Tots els processos de raonament, algoritmes i protocols de coordinació estan totalment distribuïts entre els agents i garanteixen la preservació de la informació privada dels agents. La cerca multi-agent es guia mitjançant l'aternança de dues funcions heurístiques basades en estats. Aquests estimadors heurístics utilitzen la informació global de la tasca de PMA en lloc de les projeccions locals de la tasca de cada agent. L'avaluació experimental mostra l'efectivitat del nostre esquema de cerca multi-heurístic, que obté resultats significatius en una ampla varietat de tasques de PMA cooperativa adaptades a partir dels bancs de proves de la Competició Internacional de Planificació.Torreño Lerma, A. (2016). Cooperative planning in multi-agent systems [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/65815TESISPremiadoCompendi