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

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

In this paper, we address the problem of service availability in mobile ad-hoc WANs. We presentasecuremechanism to stimulate end users to keep their devices turned on, to refrain from overloading the network, and to thwart tampering aimed at converting the device into a "selfish" one. Our solution is based on the application of a tamper resistant security module in each device and cryptographic protection of messages. 1 Introduction 1.1 The context The Terminodes Project [1, 2] is a 10-year research program (2000-2010) with the aim to investigate wide area, large, totally wireless, mobile networks that we call mobile ad-hoc wide area networks. In this project, we follow a radically distributed approach, in which all networking functions are embedded in the terminals themselves. Because they act as network nodes and terminals at the same time, we call these devices terminodes. A network of terminodes is an autonomous, self-organized network, completely independentofany fixed infr...

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.

Abstract—This paper surveys and analyzes the state of the art of agent-mediated electronic commerce (e-commerce), concentrating particularly on the business-to-consumer (B2C) and business-to-business (B2B) aspects. From the consumer buying behavior perspective, agents are being used in the following activities: need identification, product brokering, buyer coalition formation, merchant brokering, and negotiation. The roles of agents in B2B e-commerce are discussed through the business-to-business transaction model that identifies agents as being employed in partnership formation, brokering, and negotiation. Having identified the roles for agents in B2C and B2B e-commerce, some of the key underpinning technologies of this vision are highlighted. Finally, we conclude by discussing the future directions and potential impediments to the wide-scale adoption of agent-mediated e-commerce. Index Terms—Agent-mediated electronic commerce, intelligent agents. 1

This paper analyzes automated distributive negotiation where agents have firm deadlines that are private information. The agents are allowed to make and accept offers in any order in continuous time. We show that the only sequential equilibrium outcome is one where the agents wait until the first deadline, at which point that agent concedes everything to the other. This holds for pure and mixed strategies. So, interestingly, rational agents can never agree to a nontrivial split because offers signal enough weakness of bargaining power (early deadline) so that the recipient should never accept. Similarly, the offerer knows that it offered too much if the offer gets accepted: the offerer could have done better by out-waiting the opponent. In most cases, the deadline effect completely overrides time discounting and risk aversion: an agent's payoff does not change with its discount factor or risk attitude. Several implications for the design of negotiating agents are discussed. We also present an effective protocol that implements the equilibrium outcome in dominant strategies.

In (automated) negotiation systems consisting of self-interested agents, contracts have traditionally been binding. Such contracts do not allow agents to capitalize on uncertain future events. Contingency contracts have been proposed to solve this problem. Contingency contracts are often impractical due to large numbers of interdependent and unanticipated future events on which to condition, and because some events are not mutually observable. We propose a leveled commitment contracting mechanism that allows agents to capitalize on uncertain future events byhaving the possibility of unilaterally decommitting from a contract based on local reasoning. Decommitment penalties are assigned to both agents in a contract: to be freed from the obligations of the contract, an agent only pays the penalty to the other party. One concern is that a self-interested agentwould be reluctant to decommit because there is a chance that the other party will decommit. In this case the former agen...

This paper studies coalition formation among self-interested agents that cannot make sidepayments. We show that alpha-core stability reduces to analyzing whether some utility profile is maximal for all agents. We also show that strategy profiles that lead to the alpha-core are a subset of Strong Nash equilibria. This fact carries our alpha-core-based stability results directly over to two other strategic solution concepts: Nash equilibrium and Coalition-Proof Nash equilibrium.

Mobile agents are programs capable of migrating from one host machine to another. We propose that mobile agents purchase resource access rights from host machines thereby establishing a market for computational resources and giving agents a metric to evenly distribute themselves throughout the network. Market participation requires quantitative information about resource consumption to define demand and calculate utility. We create a formal utility model to derive user-demand functions, allowing agents to efficiently plan expenditure and deal with price fluctuations. By quantifying demand and utility, resource owners can precisely set a value for a good. We simulate our model in a mobile agent scheduling environment and show how mobile agents may use server prices to distribute themselves evenly throughout a network. 1 Introduction Mobile agents are programs that may, under their own volition, jump from one host and resume execution at another host. We propose using electronic Revi...

Collective choice settings are the heart of society. Game theory provides a basis for engineering the incentives into the interaction mechanism (e.g., rules of an election or auction) so that a desirable system-wide outcome (e.g., president, resource allocation, or task allocation) is chosen even though every agent acts based on self-interest. However, there are a host of computer science issues not traditionally addressed in game theory that have to be addressed in order to make mechanisms work in the real world. Those computing, communication, and privacy issues are deeply intertwined with the economic incentive issues. For example, the fact that agents have limited computational capabilities to determine their own (and others') preferences ruins the incentive properties of established auction mechanisms, and gives rise to new issues. On the positive side, computational complexity can be used as a barrier to strategic behavior in settings where economic mechanism design falls short. Novel computational approaches also enable new economic institutions. For example, market clearing technology with specialized search algorithms is enabling a form of interaction that I call expressive competition. As another example, selective incremental preference elicitation can determine the optimal outcome while requiring the agents to determine and reveal only a small portion of their preferences. Furthermore, automated mechanism design can yield better mechanisms than the best known to date.