Automated intelligent agents inhabiting a shared environmentmust coordinate their activities. Cooperation { not merely coordination { may improve the performance of the individual agents or the overall behavior of the system they form. Research in Distributed Arti cial Intelligence (DAI) addresses the problem of designing automated intelligent systems which interact e ectively. DAI is not the only eld to take on the challenge of understanding cooperation and coordination. There are a variety of other multi-entity environments in which the entities coordinate their activity and cooperate. Among them are groups of people, animals, particles, and computers. We argue that in order to address the challenge of building coordinated and collaborated intelligent agents, it is bene cial to combine AI techniques with methods and techniques from a range of multi-entity elds, such as game theory, operations research, physics and philosophy. To support this claim, we describe some of our projects, where we have successfully taken an interdisciplinary approach. We demonstrate the bene ts in applying multi-entity methodologies and show the adaptations, modi cations and extensions necessary for solving the DAI problems.

Cooperating and sharing resources by creating coalitions of agents are an important way for autonomous agents to execute tasks and to maximize payoff. Such coalitions will form only if each member of a coalition gains more if it joins the coalition than it could gain otherwise. There are several ways of creating such coalitions and dividing the joint payoff among the members. In this paper we present algorithms for coalition formation and payoff distribution in non-super-additive environments. We focus on a low-complexity kernel-oriented coalition formation algorithm. The properties of this algorithm were examined via simulations. These have shown that the model increases the benefits of the agents within a reasonable time period, and more coalition formations provide more benefits to the agents. Key Words Distributed AI, Coalition Formation, Multi-Agent Systems. This material is based upon work supported in part by the NSF under grant No. IRI-9423967, ARPA/Rome Labs contract F30602...

Coalition formation methods allow agents to join together and are thus necessary in cases where tasks can only be performed cooperatively by groups. This is the case in the Request For Proposal (RFP) domain, where some requester business agent issues an RFP - a complex task comprised of sub-tasks - and several service provider agents need to join together to address this RFP. In such environments the value of the RFP may be common knowledge, however the costs that an agent incurs for performing a specific sub-task are unknown to other agents. Additionally, time for addressing RFPs is limited. These constraints make it hard to apply traditional coalition formation mechanisms, since those assume complete information, and time constraints are of lesser significance there. To address this problem, we have developed a protocol that enables agents to negotiate and form coalitions, and provide them with simple heuristics for choosing coalition partners. The protocol and the heuristics allow the agents to form coalitions in the face of time constraints and incomplete information. The overall payoff of agents using our heuristics is very close to an experimentally measured optimal value, as our extensive experimental evaluation shows. Categories and Subject Descriptors I.2.11 [Distributed Artificial Intelligence]: Multi-agent Systems, Coherence and Coordination, Intelligent Agents.

This article proposes a simulation-based DCF scheme designed to let rational agents form coalitions in dynamic environments

Abstract—As the community strives towards autonomous multirobot systems, there is a need for these systems to autonomously form coalitions to complete assigned missions. Numerous coalition formation algorithms have been proposed in the software agent literature. Algorithms exist that form agent coalitions in both super additive and non-super additive environments. The algorithmic techniques vary from negotiation-based protocols in multi-agent system (MAS) environments to those based on computation in distributed problem solving (DPS) environments. Coalition formation behaviors have also been discussed in relation to game theory. Despite the plethora of MAS coalition formation literature, to the best of our knowledge none of the proposed algorithms have been demonstrated with an actual multi-robot system. There exists a discrepancy between the multi-agent algorithms and their applicability to the multi-robot domain. This paper aims to bridge that discrepancy by unearthing the issues that arise while attempting to tailor these algorithms to the multi-robot domain. A well-known multi-agent coalition formation algorithm has been studied in order to identify the necessary modifications to facilitate its application to the multi-robot domain. This paper reports multi-robot coalition formation results based upon simulation and actual robot experiments. A multi-agent coalition formation algorithm has been demonstrated on an actual robot system. Index Terms—Coalition formation, coalition imbalance, task allocation.

Abstract. This paper presents some of the key techniques for reaching agreements in multi-agent environments. It discusses game-theory and economics based techniques: strategic negotiation, auctions, coalition formation, market-oriented programming and contracting. It also presents logical based mechanisms for argumentations. The focus of the survey is on negotiation of self-interested agents, but several mechanisms for cooperative agents who need to resolve conflicts that arise from conflicting beliefs about different aspects of their environment are also mentioned. For space reasons, we couldn’t cover all the relevant works, and the papers that are mentioned only demonstrate the possible approaches. We present some of the properties of the approaches using our own previous work. 1

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Coalition formation is a fundamental type of interaction that involves the creation of coherent groupings of distinct, autonomous, agents in order to efficiently achieve their individual or collective goals. Forming effective coalitions is a major research challenge in the field of multi-agent systems. Central to this endeavour is the problem of determining which of the many possible coalitions to form in order to achieve some goal. This usually requires calculating a value for every possible coalition, known as the coalition value, which indicates how beneficial that coalition would be if it was formed. Once these values are calculated, the agents usually need to find a combination of coalitions, in which every agent belongs to exactly one coalition, and by which the overall outcome of the system is maximized. However, this coalition structure generation problem is extremely challenging due to the number of possible solutions that need to be examined, which grows exponentially with the number of agents involved. To date, therefore, many algorithms have been proposed to solve this problem using different techniques — ranging from dynamic programming, to integer programming, to stochastic search — all of which suffer from major limitations relating to execution time, solution quality, and memory requirements.

This paper presents protocols and strategies for coalition formation with incomplete information under time constraints. It focuses on strategies for coalition members to distribute revenues amongst themselves. Such strategies should preferably be stable, lead to a fair distribution, and maximize the social welfare of the agents. These properties are only partially supported by existing coalition formation mechanisms. In particular, stability and the maximization of social welfare are supported only in the case of complete information, and only at a high computational complexity. Recent studies on coalition formation with incomplete and uncertain information address revenue distribution in a naïve manner. In this study we specifically refer to environments with limited computational resources and incomplete information. We propose a variety of strategies for revenue distribution, including the strategy in which the agents attempt to distribute the estimated net value of a coalition equally. A variation of the equal distribution strategy in which agents compromise and agree to a payoff lower than their estimated equal share, was specifically examined. Our experimental results show that, under time constraints, the compromise strategy is stable and increases the social welfare compared to non-compromise strategies. 1.

Abstract. We introduce the notion, issues, and challenges of dynamic coalition formation (DCF) among rational software agents in open, heterogeneous and world widely distributed environments such as the Internet and Web. Selected relevant approaches coping with only parts of the DCF problem domain in different disciplines such as decision theory, social reasoning, and machine learning are briefly discussed. Finally, we sketch one novel DCF scheme, and highlight some future research work towards a general framework of dynamic coalition formation. 1