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 ...

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

This paper presents eMediator, an electronic commerce server prototype that demonstrates ways in which algorithmic support and game-theoretic incentive engineering can jointly improve the efficiency of ecommerce. eAuctionHouse, the configurable auction server, includes a variety of generalized combinatorial auctions and exchanges, pricing schemes, bidding languages, mobile agents, and user support for choosing an auction type. We introduce two new logical bidding languages for combinatorial markets: the XOR bidding language and the OR-of-XORs bidding language. Unlike the traditional OR bidding language, these are fully expressive. They therefore enable the use of the Clarke-Groves pricing mechanism for motivating the bidders to bid truthfully. eAuctionHouse also supports supply/demand curve bidding. eCommitter, the leveled commitment contract optimizer, determines the optimal contract price and decommitting penalties for a variety of leveled commitment contracting mechanisms, taking into account that rational agents will decommit strategically in Nash equilibrium. It also determines the optimal decommitting strategies for any given leveled commitment contract. eExchangeHouse, the safe exchange planner, enables unenforced anonymous exchanges by dividing the exchange into chunks and sequencing those chunks to be delivered safely in alternation between the buyer and the seller.

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

Mobile agents are programs that can migrate from machine to machine in a heterogeneous, partially disconnected network. As mobile agents move across a network, they consume resources. We discuss a system for controlling the activities of mobile agents that uses electronic cash, a banking system, and a set of resource managers. We describe protocols for transactions between agents. We present xed-pricing and dynamic-pricing policies for resources. We focus on and analyze the sealed-bid second-price auction as a mechanism for dynamic pricing. 1

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 [...

The Vickrey auction has been widely advocated for multiagent systems. First we review its limitations so as to guide practitioners in their decision of when to use that protocol. These limitations include lower revenue than alternative protocols, lying in non-private-value auctions, bidder collusion, a lying auctioneer, and undesirable revelation of sensitive information. We discuss the special characteristics of Internet auctions: third party auction servers, cryptography, and how proxy agents relate to the revelation principle and fail to promote truth-telling.

The design of social laws for artificial agent societies is a basic approach to coordinating multi-agent systems. It exposes the spectrum between fully-centralized and fully-decentralized coordination mechanisms. Useful social laws set constraints on the agents' activities which allow them to work individually in a mutually compatible manner.

Automated negotiation and coalition formation among self-interested agents are playing an increasingly important role in electronic commerce. Such agents cannot be coordinated by externally imposing their strategies. Instead the interaction protocols have to be designed so that each agent is motivated to follow the strategies that the protocol designer wants it to follow. This paper reviews six component technologies that we have developed for making such interactions less manipulable and more e cient in terms of the computational processes and the outcomes: 1. OCSM-contracts in marginal cost based contracting, 2. leveled commitment contracts, 3. anytime coalition structure generation with worst case guarantees, 4. trading o computation cost against optimization quality within each coalition, 5. distributing search among insincere agents, and 6. unenforced contract execution. Each of these technologies represents a di erent way of battling selfinterest and combinatorial complexity simultaneously. This is a key battle when multiagent systems move into large-scale open settings.