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.

The allocation of resources within a system of autonomous agents, that not only have preferences over alternative allocations of resources but also actively participate in computing an allocation, is an exciting area of research at the interface of Computer Science and Economics. This paper is a survey of some of the most salient issues in Multiagent Resource Allocation. In particular, we review various languages to represent the preferences of agents over alternative allocations of resources as well as different measures of social welfare to assess the overall quality of an allocation. We also discuss pertinent issues regarding allocation procedures and present important complexity results. Our presentation of theoretical issues is complemented by a discussion of software packages for the simulation of agent-based market places. We also introduce four major application areas for Multiagent Resource Allocation, namely industrial procurement, sharing of satellite resources, manufacturing control, and grid computing.

this article) and (2) a desirable social outcome will follow even though every agent uses its self-interested strategy

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.

this paper we discuss when and how to use deontic logic in multi agent systems

Decommitment [1, 2] is the action of foregoing of a contract for another (superior) o#er. It has been shown that, using decommitment, agents can reach higher utility levels in case of negotiations with uncertainty about future prospects. In this paper (originally published at AAMAS-03 Workshop on Agent-Mediated Electronic Commerce V (AMEC-V), 2003 Melbourne, Australia) , we study the decommitment concept for the novel setting of a large-scale logistics setting with multiple, competing companies. Orders for transportation of loads are acquired by agents of the (competing) companies by bidding in online auctions. Using computational experiments, we find significant increases in profit that scale with the size of operations and uncertainty of future prospects when computerized agents can decommit and postpone the transportation of a load to a more suitable time . The observed profit margins in the experiments are significant from the perspective of the transportation sector where a 4% profit is considered exceptional. For example, the average profit margin before taxes for the Dutch road transport sector (from 1989 to 1999) was only 1.6% [3].

In automated negotiation systems consisting of self-interested agents, contracts have traditionally been binding. Leveled commitment contracts -- i.e., contracts where each party can decommit by paying a predetermined penalty -- were recently shown to improve expected social welfare even if agents decommit strategically in Nash equilibrium. Such contracts differ based on whether agents have to declare their decommitting decisions sequentially or simultaneously, and whether or not agents have to pay the penalties if both decommit. For a given contract, these mechanisms lead to different decommitting thresholds, probabilities, and expected social welfare. However, this paper shows that each of these mechanisms leads to the same social welfare when the contract price and penalties are optimized for each mechanism separately.

Bids submitted in auctions are usually treated as enforceable commitments in most bidding and auction theory literature. In reality bidders often withdraw winning bids before the transaction when it is in their best interests to do so. Given a bid-withdrawal in a combinatorial auction, finding an alternative repair solution of adequate revenue without causing undue disturbance to the remaining winning bids in the original solution may be difficult or even impossible. We have called this the “Bid-taker’s Exposure Problem”. When faced with such unreliable bidders, it is preferable for the bid-taker to preempt such uncertainty by having a solution that is robust to bid-withdrawal and provides a guarantee that possible withdrawals may be repaired easily with a bounded loss in revenue. Firstly, we use the Weighted Super Solutions framework [13], from the field of Constraint Programming, to solve the problem of finding a robust solution of maximum revenue. A weighted super solution guarantees that any subset of bids likely to be withdrawn can be repaired to form a new solution of at least a given revenue by making a limited number of changes. Secondly, we introduce an auction model that uses a form of leveled commitment contract [27, 28], which we have called mutual bid bonds, to improve solution reparability by facilitating backtracking on winning bids by the bid-taker. We then examine the trade-off between robustness and revenue in different economically motivated auction scenarios for different constraints on the revenue of repair solutions. We also demonstrate experimentally that fewer winning bids partake in robust solutions, thereby reducing any associated overhead in dealing with extra bidders.

This paper analyzes bilateral multi-issue negotiation between selfinterested autonomous agents. The agents have time constraints in the form of both deadlines and discount factors. There are m> 1 issues for negotiation where each issue is viewed as a pie of size one. The issues are “indivisible ” (i.e., individual issues cannot be split between the parties; each issue must be allocated in its entirety to either agent). Here different agents value different issues differently. Thus, the problem is for the agents to decide how to allocate the issues between themselves so as to maximize their individual utilities. For such negotiations, we first obtain the equilibrium strategies for the case where the issues for negotiation are known a priori to the parties. Then, we analyse their time complexity and show that finding the equilibrium offers is an NP-hard problem, even in a complete information setting. In order to overcome this computational complexity, we then present negotiation strategies that are approximately optimal but computationally efficient, and show that they form an equilibrium. We also analyze the relative error (i.e., the difference between the true optimum and the approximate). The time complexity of the approximate equilibrium strategies is O(nm/ɛ 2) where n is the negotiation deadline and ɛ the relative error. Finally, we extend the analysis to online negotiation where different issues become available at different time points and the agents are uncertain about their valuations for these issues. Specifically, we show that an approximate equilibrium exists for online negotiation and show that the expected difference between the optimum and the approximate is O ( √ m). These approximate strategies also have polynomial time complexity.

A cooperation of agents in competitive environments is more complicated than in collaborative ones. Both the replanning and reconfiguration play the crucial role in the cooperation and introduce a means for an implementation of a system flexibility. The concepts of commitments, decommitments with the penalties and subcontractions may facilitate e#ective reconfiguration and replanning.