Distributed Artificial Intelligence (DAI) has existed as a subfield of AI for less than two decades. DAI is

In this paper we discuss a number of previously unaddressed issues that arise in automated negotiation among self-interested agents whose rationality is bounded by computational complexity. These issues are presented in the context of iterative task allocation negotiations. First, the reasons why such agents need to be able to choose the stage and level of commitment dynamically are identified. A protocol that allows such choices through conditional commitment breaking penalties is presented. Next, the implications of bounded rationality are analyzed. Several tradeoffs between allocated computation and negotiation benefits and risk are enumerated, and the necessity of explicit local deliberation control is substantiated. Techniques for linking negotiation items and multiagent contracts are presented as methods for escaping local optima in the task allocation process. Implementing both methods among self-interested bounded rational agents is discussed. Finally, the ...

Coalition formation is a key topic in multiagent systems. One may 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 exhaustive search for the optimal one. Furthermore, finding the optimal coalition structure is NP-complete. But then, can the coalition structure found via a partial search be guaranteed to be within a bound from optimum? We show that none of the previous coalition structure generation algorithms can establish any bound because they search fewer nodes than a threshold that we show necessary for establishing a bound. We present an algorithm that establishes a tight bound within this minimal amount of search, and show that any other algorithm would have to search strictly more. The fraction of nodes needed to be searched approaches zero as the number of agents grows. If additional time remains, our anytime algorithm searches further, and establishes a progressively lower tight bound. Surprisingly, just searching one more node drops the bound in half. As desired, our algorithm lowers the bound rapidly early on, and exhibits diminishing returns to computation. It also significantly outperforms its obvious contenders. Finally, we show how to distribute the desired

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

Auctions provide an efficient distributed mechanism for solving problems such as task and resource allocation in multiagent systems.

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.

In automated negotiation systems consisting of self-interested agents, contracts have traditionally been binding. Such contracts do not allow agents to efficiently accommodate future events. Game theory has proposed contingency contracts to solve this problem. Among computational agents, contingency contracts are often impractical due to large numbers of interdependent and unanticipated future events to be conditioned on, and because some events are not mutually observable. This paper proposes a leveled commitment contracting protocol that allows self-interested agents to efficiently accommodate future events by having the possibility of unilaterally decommitting from a contract based on local reasoning. A decommitment penalty is assigned to both agents in a contract: to be freed from the obligations of the contract, an agent only pays this penalty to the other party. It is shown through formal analysis of multiple contracting settings that this leveled commitment feature...

Software agents help automate a variety of tasks including those involved in buying and selling products over the Internet. Although shopping agents provide convenience for consumers and yield more efficient markets, today's first-generation shopping agents are limited to comparing merchant offerings only on price instead of their full range of value. As such, they do a disservice to both consumers and retailers by hiding important merchant value-added services from consumer consideration. Likewise, the increasingly popular online auctions pit sellers against buyers in distributive negotiation tug-of-wars over price. This paper analyzes these approaches from economic, behavioral, and software agent perspectives then proposes integrative negotiation as a more suitable approach to retail electronic commerce. Finally, we identify promising techniques (e.g., multi-attribute utility theory, distributed constraint satisfaction, and conjoint analysis) for implementing agent-mediated integrative negotiation. 1.

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A key lesson learned from economic and game theory research is that negotiation protocols have substantial, rippling effects on the overall nature of the system. online auctions are increasingly popular negotiation protocols for software agents (and humans) to compete on the prices of goods and services. This paper takes a critical look at these competitive protocols in retail markets from economic, game theoretic, and business perspectives. Our analysis suggests that online auction protocols are, in fact, less efficient and more hostile than would be expected (or desired) in retail markets. Furthermore, we identify the importance of customer satisfaction and propose more cooperative multi-agent decision analysis tools (e..g, Multi-Attribute Utility Theory) and negotiation protocols (e.g., Distributed Constraint Satisfaction) as promising techniques to support it. 1. Retail Market Negotiations Today's mass market retail is largely defined as monopolistic competition [...