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    FMAP: Distributed Cooperative Multi-Agent Planning

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

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    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. 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    Deep learning for video game playing

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    In this article, we review recent Deep Learning advances in the context of how they have been applied to play different types of video games such as first-person shooters, arcade games, and real-time strategy games. We analyze the unique requirements that different game genres pose to a deep learning system and highlight important open challenges in the context of applying these machine learning methods to video games, such as general game playing, dealing with extremely large decision spaces and sparse rewards

    MAA*: A Heuristic Search Algorithm for Solving Decentralized POMDPs

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    We present multi-agent A* (MAA*), the first complete and optimal heuristic search algorithm for solving decentralized partially-observable Markov decision problems (DEC-POMDPs) with finite horizon. The algorithm is suitable for computing optimal plans for a cooperative group of agents that operate in a stochastic environment such as multirobot coordination, network traffic control, `or distributed resource allocation. Solving such problems efiectively is a major challenge in the area of planning under uncertainty. Our solution is based on a synthesis of classical heuristic search and decentralized control theory. Experimental results show that MAA* has significant advantages. We introduce an anytime variant of MAA* and conclude with a discussion of promising extensions such as an approach to solving infinite horizon problems.Comment: Appears in Proceedings of the Twenty-First Conference on Uncertainty in Artificial Intelligence (UAI2005
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