Asimovian Adaptive Agents

The goal of this research is to develop agents that are adaptive and predictable and timely. At first blush, these three requirements seem contradictory. For example, adaptation risks introducing undesirable side effects, thereby making agents' behavior less predictable. Furthermore, although formal verification can assist in ensuring behavioral predictability, it is known to be time-consuming. Our solution to the challenge of satisfying all three requirements is the following. Agents have finite-state automaton plans, which are adapted online via evolutionary learning (perturbation) operators. To ensure that critical behavioral constraints are always satisfied, agents' plans are first formally verified. They are then reverified after every adaptation. If reverification concludes that constraints are violated, the plans are repaired. The main objective of this paper is to improve the efficiency of reverification after learning, so that agents have a sufficiently rapid response time. We present two solutions: positive results that certain learning operators are a priori guaranteed to preserve useful classes of behavioral assurance constraints (which implies that no reverification is needed for these operators), and efficient incremental reverification algorithms for those learning operators that have negative a priori results

Planning and scheduling research at NASA Ames Research Center

Planning and scheduling is the area of artificial intelligence research that focuses on the determination of a series of operations to achieve some set of (possibly) interacting goals and the placement of those operations in a timeline that allows them to be accomplished given available resources. Work in this area at the NASA Ames Research Center ranging from basic research in constrain-based reasoning and machine learning, to the development of efficient scheduling tools, to the application of such tools to complex agency problems is described

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. 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Knowledge Modelling in Multiagent Systems: The Case of the Management of a National Network

This paper presents the knowledge model of a distributed decision support system, that has been designed for the management of a national network in Ukraine. It shows how advanced Artificial Intelligence techniques (multiagent systems and knowledge modelling) have been applied to solve this real-world decision support problem: on the one hand its distributed nature, implied by different loci of decision-making at the network nodes, suggested to apply a multiagent solution; on the other, due to the complexity of problem-solving for local network administration, it was useful to apply knowledge modelling techniques, in order to structure the different knowledge types and reasoning processes involved. The paper sets out from a description of our particular management problem. Subsequently, our agent model is described, pointing out the local problem-solving and coordination knowledge models. Finally, the dynamics of the approach is illustrated by an example

Fostering resilient execution of multi-agent plans through self-organisation

Traditional multi-agent planning addresses the coordination of multiple agents towards common goals, by producing an integrated plan of actions for each of those agents. For systems made of large numbers of cooperating agents, however, the execution and monitoring of a plan should enhance its high-level steps, possibly involving entire sub-teams, with a flexible and adaptable lower-level behaviour of the individual agents. In order to achieve such a goal, we need to integrate the behaviour dictated by a multi-agent plan with self-organizing, swarm-based approaches, capable of automatically adapting their behaviour based on the contingent situation, departing from the predetermined plan whenever needed. Moreover, in order to deal with multiple domains and unpredictable situations, the system should, as far as possible, exhibit such capabilities without hard-coding the agents behaviour and interactions. In this paper, we investigate the relationship between multi-agent planning and self-organisation through the combination of two representative approaches both enjoying declarativity. We consider a functional approach to self-organising systems development, called Aggregate Programming (AP), and propose to exploit collective adaptive behaviour to carry out plan revisions. We describe preliminary results in this direction on a case study of execution monitoring and repair of a Multi-Agent PDDL plan

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. 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