This paper analyzes coalitions among self-interested agents that need to solve combinatorial optimization problems to operate e ciently in the world. By colluding (coordinating their actions by solving a joint optimization prob-lem) the agents can sometimes save costs compared to operating individually. A model of bounded rationality is adopted where computation resources are costly. It is not worthwhile solving the problems optimally: solution quality is decision-theoretically traded o against computation cost. A normative, application- and protocol-independent theory of coalitions among bounded-rational agents is devised. The optimal coalition structure and its stability are signi cantly a ected by the agents ' algorithms ' performance pro les and the cost of computation. This relationship is rst analyzed theoretically. Then a domain classi cation including rational and bounded-rational agents is in-troduced. Experimental results are presented in vehicle routing with real data from ve dispatch centers. This problem is NP-complete and the instances are so large that|with current technology|any agent's rationality is bounded by computational complexity. 1

Autonomous agents working in multi-agent environments may need to cooperate in order to fulfill tasks. Given a set of agents and a set of tasks which they have to satisfy, we consider situations where each task should be attached to a group of agents which will perform the task. The allocation of tasks to groups of agents is necessary when tasks cannot be performed by a single agent. It may also be useful to assign groups of agents to tasks when the group's performance is more efficient than the performance of single agents. In this paper we give an efficient solution to the problem of task allocation among autonomous agents, and suggest that the agents will form coalitions in order to perform tasks or improve the efficiency. We present a distributed algorithm with a low ratio bound and with a low computational complexity. Our algorithm is an any-time algorithm, it is simple, efficient and easy to implement. 1

The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies or endorsements, either expressed or implied, of Carnegie Mellon University. The problem of efficient multirobot coordination has risen to the forefront of robotics research in recent years. Interest in this problem is motivated by the wide range of application domains demanding multirobot solutions. In general, multirobot coordination strategies assume either a centralized approach, where a single robot/agent plans for the group, or a distributed approach, where each robot is responsible for its own planning. Inherent to many centralized approaches are difficulties such as intractable solutions for large groups, sluggish response to changes in the local environment, heavy communication requirements, and brittle systems with single points of failure. The key advantage of centralized approaches is that they can produce globally optimal plans. While most distributed approaches can overcome the obstacles inherent to centralized approaches, they can only produce suboptimal plans. This work explores the development of a market-based architecture that will be inherently distributed, but will also opportunistically form centralized sub-groups to improve efficiency, and thus

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 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.

We present herein a fully distributed algorithm for group or coalition formation among autonomous agents. The algorithm is based on a distributed computation of maximal cliques (of up to pre-specified size) in the underlying graph that captures the interconnection topology of the agents. Hence, given the current configuration of the agents, the groups that are formed are characterized by a high degree of connectivity, and therefore high fault tolerance with respect to node and link failures. We also briefly discuss how our basic algorithm can be adapted in various ways so that the formed groups satisfy the requirements ("goodness" criteria) other than mere strong inter-group communication connectivity. We envision various variants of our basic algorithm to prove themselves useful subroutines in many multi-agent system and ad hoc network applications where the agents may repeatedly need to form temporary groups or coalitions in an e#cient, fully distributed and online manner.

The goal of a rational agent is to maximize utility. We consider situations where a rational agent has to choose one of several contenders to enter into a partnership. We assume that the agent has a model of the likelihood of different outcomes and corresponding utilities for each such partnership. Given a fixed, finite number of interactions, the problem is to choose a particular partner to interact with where the goal is to maximize the sum of utilities received from all the interactions. We develop a multinomial distribution based mechanism for partner selection and contrast its performance with other well-known approaches which provide exact solution to this problem for infinite interactions.

This paper is concerned with the formalization of a automated contracting mechanism that enables a society of cooperative resource allocation agents to negotiate rationally in a self-interested metasociety.

This paper gives a survey of work on decentralized coordination techniques, sources mainly taken from multi-agent research, coordination science and market-oriented control by the use of virtual markets for resource allocation

Coalition formation in multi-agent systems is a process where agents form coalitions and work together to solve a joint problem via cooperating or coordinating their actions within each coalition. It is important for distributed applications ranging from electronic business to mobile and ubiquitous computing where adaptation to changing resources and environments is crucial. Coalition formation is useful as it may increase the ability of agents to accomplish tasks and achieve their goals. However, in complex real-world environments that agents operate in, the available resources are generally constrained. Agents only have incomplete even inaccurate information about the dynamically changing world. The occurrence of events may require the agents to react in a real-time manner. Agents’ actions may result in uncertain outcomes. These factors inevitably influence the formation process and formation outcome of a coalition. We employ a learning-based two-phased coalition formation approach to help agents form coalitions in complex environments. The approach consists of (1) a two-phase (planning and instantiation) coalition formation model, (2) a two-level (strategic and tactical) learning mechanism, (3) an adaptive, confidence-based negotiation strategy, and