Coalition formation is a desirable behavior in a multiagent system, when a group of agents can perform a task more efficiently than any single agent can. Computational and communications complexity of traditional approaches to coalition formation, e.g., through negotiation, make them impractical for large systems. We propose an alternative, physics-motivated mechanism for coalition formation that treats agents as randomly moving, locally interacting entities. A new coalition may form when two agents encounter one another, and it may grow when a single agent encounters it. Such agent-level behavior leads to a macroscopic model that describes how the number and distribution of coalitions change with time. We increase the generality and complexity of the model by letting the agents leave coalitions with some probability. The model is expressed mathematically as a series of differential equations. These equations have steady state solutions that describe the equilibrium distribution of coa...

The coalition formation process, in which a number of independent, autonomous agents come together to act as a collective, is an important form of interaction in multiagent systems. When e#ective, such coalitions can improve the performance of the individual agents and/or of the system as a whole. However, one of the main problems that hinders the wide spread adoption of coalition formation technologies is the computational complexity of coalition structure generation. That is, once a group of agents has been identified, how can it be partitioned in order to maximise the social payoff? This problem has been shown to be NP-hard and even finding a sub-optimal solution requires searching an exponential number of solutions. Against this background, this paper reports on a novel anytime algorithm for coalition structure generation that produces solutions that are within a finite bound from the optimal. Our algorithm is benchmarked against Sandholm et al.'s algorithm [8] (the only other known algorithm for this task that can also establish a worst-case bound from the optimal) and is shown to be up to 10^379 times faster (for systems containing 1000 agents) when small bounds from the optimal are desirable.

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 paper studies coalition formation among self-interested agents that cannot make sidepayments. We show that alpha-core stability reduces to analyzing whether some utility profile is maximal for all agents. We also show that strategy profiles that lead to the alpha-core are a subset of Strong Nash equilibria. This fact carries our alpha-core-based stability results directly over to two other strategic solution concepts: Nash equilibrium and Coalition-Proof Nash equilibrium.

Abstract. The European Commission’s eEurope initiative aims to bring every citizen, home, school, business and administration online to create a digitally literate Europe. The value lies not in the objective itself, but in its ability to facilitate the advance of Europe into new ways of living and working. Just as in the first literacy revolution, our lives will change in ways never imagined. The vision of eEurope is underpinned by a technological infrastructure that is now taken for granted. Yet it provides us with the ability to pioneer radical new ways of doing business, of undertaking science, and, of managing our everyday activities. Key to this step change is the development of appropriate mechanisms to automate and improve existing tasks, to anticipate desired actions on our behalf (as human users) and to undertake them, while at the same time enabling us to stay involved and retain as much control as required. For many, these mechanisms are now being realised by agent technologies, which are already providing dramatic and sustained benefits in several business and industry domains, including B2B exchanges, supply chain management, car manufacturing, and so on. While there are many real successes of agent technologies to report, there is still much to be done in research and development for the full benefits to be achieved. This is especially true in the context of environments of pervasive computing devices that are envisaged in coming years. This paper describes the current state-of-the-art of agent technologies and

Despite more than a decade of experimental work in multi-robot systems, important theoretical aspects of multi-robot coordination mechanisms have, to date, been largely untreated. To address this issue, we focus on the problem of multi-robot task allocation (MRTA). Most work on MRTA has been ad hoc and empirical, with many coordination architectures having been proposed and validated in a proof-of-concept fashion, but infrequently analyzed. With the goal of bringing objective grounding to this important area of research, we present a formal study of MRTA problems. A domain-independent taxonomy of MRTA problems is given, and it is shown how many such problems can be viewed as instances of other, well-studied, optimization problems. We demonstrate how relevant theory from operations research and combinatorial optimization can be used for analysis and greater understanding of existing approaches to task allocation, and show how the same theory can be used in the synthesis of new approaches.

We propose a general mathematical methodology for studying the dynamics of multiagent systems in which complex collective behavior arises out of local interactions between many simple agents. The mathematical model is composed of a system of coupled differential equations describing the macroscopic, or collective, dynamics of an agent-based system. We illustrate our approach by applying it to analyze several agent-based systems, including coalition formation in an electronic marketplace, and foraging and collaboration in a group of robots. 1.

Large scale open multi-agent systems where agents need services of other agents but may not know their contact information require agent location mechanisms. Solutions to this problem are usually based on middle-ware such as matchmakers, brokers, yellow-pages agents and other middle agents. The disadvantage of these is that they impose infrastructure, protocol and communication overheads, and they do not easily scale up. We suggest a new approach to agent location, which does not require middle agents and protocols for using them. Our approach is simple and scales up with no infrastructure or protocol overheads, thus may be very useful for large scale MAS. In this paper, we analytically study the properties of our approach and discuss its advantages. 1 Introduction Multi-agent systems (MAS) are taking an increasing role in the solution of highly distributed computational problems in dynamic, open domains. We assume that large-scale open MAS will be an inevitable part of this trend. T...

Coalition formation is a key problem in automated negotiation among selfinterested agents, and other multiagent applications. A coalition of agents can sometimes accomplish things that the individual agents cannot, or can accomplish them more efficiently. Motivating the agents to abide by a solution requires careful analysis: only some of the solutions are stable in the sense that no group of agents is motivated to break off and form a new coalition. This constraint has been studied extensively in cooperative game theory: the set of solutions that satisfy it is known as the core. The computational questions around the core have received less attention. When it comes to coalition formation among software agents (that represent real-world parties), these questions become increasingly explicit. In this

Abstract. Coalition formation is a key topic in multiagent systems. One would prefer a coalition structure that maximizes the sum of the values of the coalitions, but often the number of coalition structures is too large to allow for exhaustive search for the optimal one. We present experimental results for three anytime algorithms that search the space of coalition structures. We show that, in the average case, all three algorithms do much better than the recently established theoretical worst case results in Sandholm et al. (1999a). We also show that no one algorithm is dominant. Each algorithm’s performance is in¯uenced by the particular instance distribution, with each algorithm outperforming the others for diŒerent instances. We present a possible explanation for the behaviour of the algorithms and support our hypothesis with data collected from a controlled experimental run. K eywords: coalition structure, algorithm, multiagent systems 1.