Plan merging by reuse for multi-agent planning

Multi-Agent Planning deals with the task of generating a plan for/by a set of agents that jointly solve a planning problem. One of the biggest challenges is how to handle interactions arising from agents' actions. The first contribution of the paper is Plan Merging by Reuse, pmr, an algorithm that automatically adjusts its behaviour to the level of interaction. Given a multi-agent planning task, pmr assigns goals to specific agents. The chosen agents solve their individual planning tasks and the resulting plans are merged. Since merged plans are not always valid, pmr performs planning by reuse to generate a valid plan. The second contribution of the paper is rrpt-plan, a stochastic plan-reuse planner that combines plan reuse, standard search and sampling. We have performed extensive sets of experiments in order to analyze the performance of pmr in relation to state of the art multi-agent planning techniques.This work has been partially supported by the MINECO projects TIN2017-88476-C2-2-R, RTC-2016-5407-4, and TIN2014-55637-C2-1-R and MICINN project TIN2011-27652-C03-02

Multi-Agent Planning by Plan Reuse (Extended Abstract)

ABSTRACT Generating plans for a single agent has been shown to be a difficult task. If we generalize to a multi-agent setting, the problem becomes exponentially harder in general. The centralized approach where a plan is jointly generated for all agents is only possible in some applications when agents do not have private goals, actions or states. We describe in this paper an alternative approach, mapr (Multi-Agent Planning by plan Reuse), that considers both the agents private and public information. We have been inspired by iterative Multi-Agent Planning (MAP) techniques as the one presented in Categories and Subject Descriptors MULTI-AGENT PLANNING TASK A single-agent strips planning task can formally defined as a tuple Π = {F, A, I, G}, where F is a set of propositions, A is a set of instantiated actions, I ⊆ F is an initial state, and G ⊆ F is a set of goals. Each action a ∈ A is described by a set of preconditions, and a set of effects. The solution of planning tasks are sequences of actions π = (a1, . . . , an) such that, if applied in order, they result in a state s, where goals are true, G ⊆ s. In a multi-agent setting, the planner generates a plan for a set of agents, Φ = {φ1, . . . , φm}. We define the MAP task as a set of planning subtasks, one for each agent, M = {Π1, . . . , Πm}. Each planning subtask can be defined as a single-agent planning task, Πi = {Ai, Fi, Ii, Gi}. All these components have a public part, that can be shared with the rest of agents, and a private part. In order to differentiate those parts, we provide each agent with the list of domain predicates and types that are private. Thus, all actions, states and goals of each agent can have a private and a public part. MULTI-AGENT PLANNING IN MAP

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. In: Proceedings of the 22nd international conference on automated planning and scheduling (ICAPS). AAAI, pp 2–10Borrajo D. (2013) Multi-agent planning by plan reuse. In: Proceedings of the 12th international conference on autonomous agents and multi-agent systems (AAMAS). IFAAMAS, pp 1141–1142Boutilier 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). AAAI, pp 28–35Brenner M, Nebel B (2009) Continual planning and acting in dynamic multiagent environments. J Auton Agents Multiagent Syst 19(3):297–331Bresina J, Dearden R, Meuleau N, Ramakrishnan S, Smith D, Washington R (2002) Planning under continuous time and resource uncertainty: a challenge for AI. In: Proceedings of the 18th conference on uncertainty in artificial intelligence (UAI). Morgan Kaufmann, pp 77–84Cox J, Durfee E (2009) Efficient and distributable methods for solving the multiagent plan coordination problem. Multiagent Grid Syst 5(4):373–408Crosby M, Rovatsos M, Petrick R (2013) Automated agent decomposition for classical planning. In: Proceedings of the 23rd international conference on automated planning and scheduling (ICAPS). AAAI, pp 46–54Dimopoulos Y, Hashmi MA, Moraitis P (2012) μ-satplan: Multi-agent planning as satisfiability. Knowl-Based Syst 29:54–62Fikes R, Nilsson N (1971) STRIPS: a new approach to the application of theorem proving to problem solving. Artif Intell 2(3):189–208Gerevini A, Haslum P, Long D, Saetti A, Dimopoulos Y (2009) Deterministic planning in the fifth international planning competition: PDDL3 and experimental evaluation of the planners. Artif Intell 173(5-6):619–668Ghallab M, Nau D, Traverso P (2004) Automated planning. Theory and practice. Morgan KaufmannGünay A, Yolum P (2013) Constraint satisfaction as a tool for modeling and checking feasibility of multiagent commitments. Appl Intell 39(3):489–509Helmert M (2004) A planning heuristic based on causal graph analysis. In: Proceedings of the 14th international conference on automated planning and scheduling ICAPS. AAAI, pp 161–170Hoffmann J, Nebel B (2001) The FF planning system: fast planning generation through heuristic search. J Artif Intell Res 14:253–302Jannach D, Zanker M (2013) Modeling and solving distributed configuration problems: a CSP-based approach. IEEE Trans Knowl Data Eng 25(3):603–618Jonsson 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 114–121Kala R, Warwick K (2014) Dynamic distributed lanes: motion planning for multiple autonomous vehicles. Appl Intell:1–22Koehler J, Ottiger D (2002) An AI-based approach to destination control in elevators. AI Mag 23(3):59–78Kovacs DL (2011) Complete BNF description of PDDL3.1. Technical reportvan der Krogt R (2009) Quantifying privacy in multiagent planning. Multiagent Grid Syst 5(4):451–469Kvarnström 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(1–2):87–143Long D, Fox M (2003) The 3rd international planning competition: results and analysis. J Artif Intell Res 20:1–59Nissim 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). IFAAMAS, pp 1323–1330O’Brien P, Nicol R (1998) FIPA - towards a standard for software agents. BT Tech J 16(3):51–59Öztürk P, Rossland K, Gundersen O (2010) A multiagent framework for coordinated parallel problem solving. Appl Intell 33(2):132–143Pal A, Tiwari R, Shukla A (2013) Communication constraints multi-agent territory exploration task. Appl Intell 38(3):357–383Richter S, Westphal M (2010) The LAMA planner: guiding cost-based anytime planning with landmarks. J Artif Intell Res 39(1):127–177de la Rosa T, García-Olaya A, Borrajo D (2013) A case-based approach to heuristic planning. Appl Intell 39(1):184–201Sapena O, Onaindia E (2008) Planning in highly dynamic environments: an anytime approach for planning under time constraints. Appl Intell 29(1):90–109Sapena O, Onaindia E, Garrido A, Arangú M (2008) A distributed CSP approach for collaborative planning systems. Eng Appl Artif Intell 21(5):698–709Serrano E, Such J, Botía J, García-Fornes A (2013) Strategies for avoiding preference profiling in agent-based e-commerce environments. Appl Intell:1–16Smith D, Frank J, Jónsson A (2000) Bridging the gap between planning and scheduling. Knowl Eng Rev 15(1):47–83Such J, García-Fornes A, Espinosa A, Bellver J (2012) Magentix2: a privacy-enhancing agent platform. Eng Appl Artif Intell:96–109Tonino H, Bos A, de Weerdt M, Witteveen C (2002) Plan coordination by revision in collective agent based systems. Artif Intell 142(2):121–145Torreño A, Onaindia E, Sapena O (2012) An approach to multi-agent planning with incomplete information. In: Proceedings of the 20th European conference on artificial intelligence (ECAI), vol 242. IOS Press, pp 762–767Torreño A, Onaindia E, Sapena O (2014) A flexible coupling approach to multi-agent planning under incomplete information. Knowl Inf Syst 38(1):141–178Van 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). AAAI, pp 161–170de Weerdt M, Clement B (2009) Introduction to planning in multiagent systems. Multiagent Grid Syst 5(4):345– 355Yokoo M, Durfee E, Ishida T, Kuwabara K (1998) The distributed constraint satisfaction problem: formalization and algorithms. IEEE Trans Knowl Data Eng 10(5):673–685Zhang J, Nguyen X, Kowalczyk R (2007) Graph-based multi-agent replanning algorithm. In: Proceedings of the 6th international joint conference conference on autonomous agents and multiagent systems (AAMAS). IFAAMAS, pp 798–80

A survey of agent-oriented methodologies

This article introduces the current agent-oriented methodologies. It discusses what approaches have been followed (mainly extending existing object oriented and knowledge engineering methodologies), the suitability of these approaches for agent modelling, and some conclusions drawn from the survey

Lifelong Multi-Agent Path Finding in Large-Scale Warehouses

Multi-Agent Path Finding (MAPF) is the problem of moving a team of agents to their goal locations without collisions. In this paper, we study the lifelong variant of MAPF, where agents are constantly engaged with new goal locations, such as in large-scale automated warehouses. We propose a new framework Rolling-Horizon Collision Resolution (RHCR) for solving lifelong MAPF by decomposing the problem into a sequence of Windowed MAPF instances, where a Windowed MAPF solver resolves collisions among the paths of the agents only within a bounded time horizon and ignores collisions beyond it. RHCR is particularly well suited to generating pliable plans that adapt to continually arriving new goal locations. We empirically evaluate RHCR with a variety of MAPF solvers and show that it can produce high-quality solutions for up to 1,000 agents (= 38.9\% of the empty cells on the map) for simulated warehouse instances, significantly outperforming existing work.Comment: Published at AAAI 202

mPower: A component-based development framework for multi-agent systems to support business processes

One of the obstacles preventing the widespread adoption of multi-agent systems in industry is the difficulty of implementing heterogeneous interactions among participating agents via asynchronous messages. This difficulty arises from the need to understand how to combine elements of various content languages, ontologies, and interaction protocols in order to construct meaningful and appropriate messages. In this paper mPower, a component-based layered framework for easing the development of multi-agent systems, is described, and the facility for customising the components for reuse in similar domains is explained. The framework builds on the JADE-LEAP platform, which provides a homogeneous layer over diverse operating systems and hardware devices, and allows ubiquitous deployment of applications built on multi-agent systems both in wired and wireless environments. The use of the framework to develop mPowermobile , a multi-agent system to support mobile workforces, is reported

A Deep Hierarchical Approach to Lifelong Learning in Minecraft

We propose a lifelong learning system that has the ability to reuse and transfer knowledge from one task to another while efficiently retaining the previously learned knowledge-base. Knowledge is transferred by learning reusable skills to solve tasks in Minecraft, a popular video game which is an unsolved and high-dimensional lifelong learning problem. These reusable skills, which we refer to as Deep Skill Networks, are then incorporated into our novel Hierarchical Deep Reinforcement Learning Network (H-DRLN) architecture using two techniques: (1) a deep skill array and (2) skill distillation, our novel variation of policy distillation (Rusu et. al. 2015) for learning skills. Skill distillation enables the HDRLN to efficiently retain knowledge and therefore scale in lifelong learning, by accumulating knowledge and encapsulating multiple reusable skills into a single distilled network. The H-DRLN exhibits superior performance and lower learning sample complexity compared to the regular Deep Q Network (Mnih et. al. 2015) in sub-domains of Minecraft