A case-based approach to heuristic planning

Most of the great success of heuristic search as an approach to AI Planning is due to the right design of domain-independent heuristics. Although many heuristic planners perform reasonably well, the computational cost of computing the heuristic function in every search node is very high, causing the planner to scale poorly when increasing the size of the planning tasks. For tackling this problem, planners can incorporate additional domain-dependent heuristics in order to improve their performance. Learning-based planners try to automatically acquire these domain-dependent heuristics using previous solved problems. In this work, we present a case-based reasoning approach that learns abstracted state transitions that serve as domain control knowledge for improving the planning process. The recommendations from the retrieved cases are used as guidance for pruning or ordering nodes in different heuristic search algorithms applied to planning tasks. We show that the CBR guidance is appropriate for a considerable number of planning benchmarks.This work has been partially supported by the Spanish MEC projects PELEA: TIN2008-6701-C03-03 and PlanInteraction: TIN2011-27652-C03-02.Publicad

FMAP: Distributed Cooperative Multi-Agent Planning

This paper proposes FMAP (Forward Multi-Agent Planning), a fully-distributed multi-agent planning method that integrates planning and coordination. Although FMAP is specifically aimed at solving problems that require cooperation among agents, the flexibility of the domain-independent planning model allows FMAP to tackle multi-agent planning tasks of any type. In FMAP, agents jointly explore the plan space by building up refinement plans through a complete and flexible forward-chaining partial-order planner. The search is guided by h D T G , a novel heuristic function that is based on the concepts of Domain Transition Graph and frontier state and is optimized to evaluate plans in distributed environments. Agents in FMAP apply an advanced privacy model that allows them to adequately keep private information while communicating only the data of the refinement plans that is relevant to each of the participating agents. Experimental results show that FMAP is a general-purpose approach that efficiently solves tightly-coupled domains that have specialized agents and cooperative goals as well as loosely-coupled problems. Specifically, the empirical evaluation shows that FMAP outperforms current MAP systems at solving complex planning tasks that are adapted from the International Planning Competition benchmarks.This work has been partly supported by the Spanish MICINN under projects Consolider Ingenio 2010 CSD2007-00022 and TIN2011-27652-C03-01, the Valencian Prometeo project II/2013/019, and the FPI-UPV scholarship granted to the first author by the Universitat Politecnica de Valencia.Torreño Lerma, A.; Onaindia De La Rivaherrera, E.; Sapena Vercher, O. (2014). FMAP: Distributed Cooperative Multi-Agent Planning. Applied Intelligence. 41(2):606-626. https://doi.org/10.1007/s10489-014-0540-2S606626412Benton J, Coles A, Coles A (2012) Temporal planning with preferences and time-dependent continuous costs. 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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

Generación automática de narrativas

En este trabajo se desarrolla un dominio de planificación de manera que, ejecutado en un planificador, se obtengan historias con interés narrativo. Para ello se hace uso del lenguaje PDDL y el planificador Sayphi. Este trabajo se centrará en primer lugar en las decisiones de diseño tomadas para modelar la información. En segundo lugar, se experimentará con el dominio desarrollado, utilizando para ello varios problemas de diferente complejidad y analizando los resultados obtenidos tanto desde el punto de vista de la planificación como desde el punto de vista narrativo.In this bachelor thesis a planning domain is developed so that, executed in a planner, stories obtained could be interesting from a narrative point of view. To do that, the PDDL language and Sayphi planner are used. This paper will focus primarily on the design decisions taken to model the information. Second, the testing phase will take place, using problems with different complexity and analizing the results from the point of view of planning and from the narrative point of view.Ingeniería Informátic

Generación automática de narrativas

El objetivo central de este trabajo, es conseguir generar automáticamente narraciones coherentes y de interés, mediante la planificación automática. Para ello se genera un dominio, que, ejecutado en un planificador, sea capaz de generar un plan de resolución (historias) a distintos problemas. Además, se busca que los planes o historias encontradas tengan coherencia. Por último, se pretende que para un mismo problema se puedan encontrar un gran número de planes distintos, pudiendo incorporar cierta aleatoriedad al planificador en el que se ejecutará el dominio. La temática de las historias que se pretenden generar tiene que ver con la guerra civil de la región que se simula en el juego Far Cry 4. Por lo tanto, el objetivo es narrar las acciones que van a ejecutar los personajes de cada Bando de la guerra. Dado que la región que se simula en el juego es bastante extensa, se van a generar narraciones que no incluirán todos los detalles que simula Ubisoft dentro del juego, pero se pretende que, aunque las historias que se generen en este trabajo no tengan todos los personajes, lugares, criaturas que aparecen en el juego, éstas sean lo bastante significativas como para sumergir al lector en la región que simula el videojuego Far Cry 4. La motivación principal es conseguir que las historias generadas por el dominio implementado puedan ser utilizadas por distintas empresas relacionadas con la industria del entretenimiento.The main objective on this project has been to develop a system that allows us to create coherent narrative stories based on the automatic planning. After analyzing our results when doing the experimentation process we can now tell that this main objective has been achieved. From a narrative point of view, the stories developed by the system are both coherent and interesting and, even if the system would be able to generate only a sequence of the events that take place in one of the stories, this sequence of events could be used to create a book story, a film script or a videogame plot. The most difficult part of the work has been to achieve a very good command of PDDL language, because in the beginning my level of PDDL was not as high as needed and it took me quite a long time to be used to it. I found as well some difficulties on the process of modeling the planning domain, and I sometimes had to change, along the process, my decisions about the modeling.Ingeniería Informátic

Generación automática de narrativas: aventura fantástica

En este trabajo se desarrolla un dominio que, al ejecutarlo en un planificador, genere historias de interés narrativo. Se emplea la aventura fantástica como temática principal de la narración, basada en la disputa por objetos poderosos entre personajes con buenos y malos motivos. Para el desarrollo de dicho dominio, se emplea el lenguaje PDDL para su creación y el planificador Sayphi para su ejecución. Estudiaremos sus objetivos, planificación y diseño, así como el análisis de los resultados obtenidos en los problemas creados para él.This bachelor thesis develops a domain which, when executed in a planner, generates stories of narrative interest. The fantastic adventure is used as the main theme of the story, based on powerful items dispute between characters with good and evil motives. For its development, PDDL language is used for the creation of that domain, and Sayphi planner for its execution. We are going to study its objectives, planning and design, as well as the analysis of the results obtained from the problems created for it.Ingeniería Informátic