Buyer coalitions are beneficial in e-marketplaces because they allow buyers to take advantage of volume discounts. However, existing buyer coalition formation schemes do not provide buyers with any means to declare and match their preferences or to calculate the division of the surplus in a stable manner. Concepts and algorithms for coalition formation have been investigated in game theory and multi-agent systems research, but because of the computational complexity, they cannot deal with thousands of buyers which could join a coalition in practice. In this paper, we propose a new buyer coalition formation scheme GroupBuyAuction. At GroupBuyAuction, buyers form a group based on a category of items. A buyer can post an OR-asking for multiple items within a category. An OR-asking is a list of items indicating that the buyer would buy any one of the items in the list with some particular reservation price. Sellers bid volume discount prices. The group leader agent splits the group into sub groups (coalitions), selects a winning seller for each coalition, and calculates surplus division among buyers. We prove that this scheme guarantees the stability in surplus division within each coalition in terms of the core in game theory. Simulation results show that, under most conditions, our scheme increases buyers' utility, and allows more buyers to obtain items compared to traditional group buying schemes, such as those used at existing commercial WWW sites.

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...

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.

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.

Research on coalition formation usually assumes the values of potential coalitions to be known with certainty. Furthermore, settings in which agents lack sufficient knowledge of the capabilities of potential partners is rarely, if ever, touched upon. We remove these often unrealistic assumptions and propose a model that utilizes Bayesian (multiagent) reinforcement learning in a way that enables coalition participants to reduce their uncertainty regarding coalitional values and the capabilities of others. In addition, we introduce the Bayesian Core, a new stability concept for coalition formation under uncertainty. Preliminary experimental evidence demonstrates the effectiveness of our approach. 1.

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.

A large variety of solutions exists for the problem of coalition formation among autonomous agents, at the theoretical level within game theory , and at the practical, algorithmic level, within multi-agentsy stems. However, one major underly ing assumption of algorithmic solutions suggested to date is that the values of the coalitions are known and are certain at the time of coalition formation negotiation. In many practical cases such as in open, dy namically changing environments this assumption does not hold. In this paper we propose an algorithmic solution to the coalition formation problem that overcomes this limitation of previous solutions. Our solution supports fuzzy coalition values and allows agents to form stable coalition configurations. For this, we combine concepts from the theory of fuzzy sets with the game-theoretic stability concept of the Kernel to deduce the new concept of a fuzzy Kernel. We further provide a low-complexity algorithm for forming fuzzy Kernel stable coalitions among agents.

Coalition formation is a problem of great interest in AI, allowing groups of autonomous, rational agents to form stable teams. Furthermore, the study of coalitional stability concepts and their relation to equilibria that guide the strategic interactions of agents during bargaining has lately attracted much attention. However, research to date in both AI and economics has largely ignored the potential presence of uncertainty when studying either coalitional stability or coalitional bargaining. This paper is the first to relate a (cooperative) stability concept under uncertainty, the Bayesian core (BC), with (non-cooperative) equilibrium concepts of coalitional bargaining games. We prove that if the BC of a coalitional game (and of each subgame) is non-empty, then there exists an equilibrium of the corresponding bargaining game that produces a BC element; and conversely, if there exists a coalitional bargaining equilibrium (with certain properties), then it induces a BC configuration. We thus provide a non-cooperative justification of the BC stability concept. As a corollary, we establish a sufficient condition for the existence of the BC. Finally, for small games, we provide an algorithm to decide whether the BC is non-empty.

Software agents can be useful in forming buyers ’ groups since humans have considerable difficulties in finding Pareto-optimal deals (no buyer can be better without another being worse) in negotiation situations. Then what are the computational and economical performances of software agents for a group buying problem? We developed a negotiation protocol for software agents that we evaluated to see if the problem is difficult on average and why. This protocol provably finds a Pareto-optimal solution and furthermore, minimizes the worst distance to ideal among all software agents given strict preference ordering. This evaluation demonstrated that memory requirements (and not execution time complexity) limit the performance of software agents in this group buying problem. We have also investigated if software agents following the developed protocol have a different buying behaviour than the customer they represented would have in the same situation. Results show that software agents have a greater difference of behaviour (and a better behaviour since they can always simulate the obvious customer behaviour of buying alone their preferred product) when they have similar preferences over the space of available products. We also discuss the type of behaviour changes and their frequencies based on the situation.

Abstract — In this paper, we lay the groundwork for extending our previously developed ASyMTRe architecture to enable constructivist learning for multi-robot team tasks. The ASyMTRe architecture automatically configures schemas within, and across, robots to form the highest utility solution that achieves a given multi-robot team task. We believe that the schemabased approach used in ASyMTRe is a useful abstraction not only for forming heterogeneous coalitions, but also for enabling constructivist learning, in which chunks of schemas that solve intermediate subproblems are learned and then made available for future task solutions. However, the existing ASyMTRe search algorithm for finding configurations of schemas that completely solve given tasks (Centralized ASyMTRe – CA) is not well-suited for identifying useful chunks of schemas that could solve intermediate subtasks that may be useful in the