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 ...

Abstract — We study the role of pricing policies in multiple service class networks. We first argue that some form of serviceclass sensitive pricing is required for any multiclass service discipline ’ to attain the desired level of performance. Borrowing heavily from the Nash implementation paradigm in economics, we then present an abstract formulation of service disciplines and pricing policies. This formulation allows us to describe more clearly the interplay between service disciplines and pricing policies in determining overall network performance. Effective mrdtichtss service disciplines allow networks to focus resources on performance sensitive applications, while effective pricing policies allow us to spread the benefits of multiple service classes around to all users, rather than just having these benefits remain exclusively with the users of applications that are performance sensitive. Furthermore, service disciplines and pricing policies combine to form the incentive system facing a user; these incentives must be carefully tuned so that user self-interest leads to optimaf overall network performance. Finally, we illustrate some of these concepts through simulation of several simple example networks. In our simulations, we find that it is possible to set the prices so that users of every application type are more satisfied with the combined cost and performance of a network with service-class sensitive prices. For some application types the performance penalty received for requesting a less-than-optimal service class is offset by the reduced price of the service. For the other application types the monetary penalty incurred by using the more expensive, higher-quality service classes is offset by the improved performance they receive.z I.

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

Introduction Automated negotiation systems with self-interested agents are becoming increasingly important. One reason for this is the technology push of a growing standardized communication infrastructure---Internet, WWW, NII, EDI, KQML, FIPA, Concordia, Voyager, Odyssey, Telescript, Java, etc---over which separately designed agents belonging to different organizations can interact in an open environment in realtime and safely carry out transactions. The second reason is strong application pull for computer support for negotiation at the operative decision making level. For example, we are witnessing the advent of small transaction electronic commerce on the Internet for purchasing goods, information, and communication bandwidth [29]. There is also an industrial trend toward virtual enterprises: dynamic alliances of small, agile enterprises which together can take advantage of economies of scale when available (e.g., respond to mor

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

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...

A Dissertation Presented by TUOMAS W. SANDHOLM

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This paper analyzes coalition formation among self-interested agents that need to solve combinatorial optimization problems to operate efficiently in the world. By colluding (coordinating their actions by solving a joint optimization problem), 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 worth solving the problems optimally: solution quality is decision-theoretically traded off against computation cost. A normative theory of coalitions among bounded rational (BR) agents is devised. The optimal coalition structure and its stability are significantly affected by the agents ' algorithms ' performance profiles (PPs) and the cost of computation. This relationship is first analyzed theoretically. A domain classification including rational and BR agents is introduced. Experimental results are presented in the distributed vehicle routing domain using real data from 5 dispatch centers; the optimal coalition structure for BR agents differs significantly from the one for rational agents. These problems are NP-complete and the instances are so large that, with current technology, any agent's rationality is bounded by computational complexity. 1