Kate Larson

Coalition structure generation with worst case guarantees

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 search across self-interested manipulative agents.

Bargaining with limited computation: deliberation equilibrium

We develop a normative theory of interaction-negotiation in particular-among self-interested computationally limited agents where computational actions are game theoretically treated as part of an agents strategy. We focus on a 2-agent setting where each agent has an intractable individual problem, and there is a potential gain from pooling the problems, giving rise to an intractable joint problem. At any time, an agent can compute to improve its solution to its own problem, its opponents problem, or the joint problem. At a deadline the agents then decide whether to implement the joint solution, and if so, how to divide its value (or cost). We present a fully normative model for controlling anytime algorithms where each agent has statistical performance profiles which are optimally conditioned on the problem instance as well as on the path of results of the algorithm run so far. Using this model, we introduce a solution concept, which we call deliberation equilibrium. It is the perfect Bayesian equilibrium of the game where deliberation actions are part of each agents strategy. The equilibria differ based on whether the performance profiles are deterministic or stochastic, whether the deadline is known or not, and whether the proposer is known in advance or not. We present algorithms for finding the equilibria. Finally, we show that there exist instances of the deliberation-bargaining problem where no pure strategy equilibria exist and also instances where the unique equilibrium outcome is not Pareto efficient. Copyright 2001 Elsevier Science B.V.

Anytime coalition structure generation: an average case study

Coalition formation is a key topic in multiagent systems. One would 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 for exhaustive search for the optimal one. We present experimental results for three anytime algorithms that search the space of coalition structures. We show that, in the average case, all three algorithms do much better than the recently established theoretical worst case results in Sandholm et al. (1999a). We also show that no one algorithm is dominant. Each algorithms performance is influenced by the particular instance distribution, with each algorithm outperforming the others for different instances. We present a possible explanation for the behaviour of the algorithms and support our hypothesis with data collected from a controlled experimental run.

Argumentation and Game Theory

In a large class of multi-agent systems, agents are self-interested in the sense that each agent is interested only in furthering its individual goals, which may or may not coincide with others’ goals. When such agents engage in argument, they would be expected to argue strategically in such a way that makes it more likely for their argumentative goals to be achieved. What we mean by arguing strategically is that instead of making arbitrary arguments, an agent would carefully choose its argumentative moves in order to further its own objectives. The mathematical study of strategic interaction is Game Theory, which was pioneered by von Neuman and Morgenstern [13]. A setting of strategic interaction is modelled as a game, which consists of a set of players, a set of actions available to them, and a rule that determines the outcome given players’ chosen actions. In an argumentation scenario, the set of actions are typically the set of argumentative moves (e.g. asserting a claim or challenging a claim), and the outcome rule is the criterion by which arguments are evaluated (e.g. a judge’s attitude or a social norm). Generally, game theory can be used to achieve two goals:

An alternating offers bargaining model for computationally limited agents

An alternating offers bargaining model for computationally limited agents is presented. The gents compute to determine plans, but deadlines restrict them from determining an optimal solution. As the agents compute, they also negotiate over whether to perform a joint plan or whether to act independently and how, if implemented, the value of the joint plan would be divided. Their computing actions and bargaining actions are interrelated and both incorporated into each agents strategy. We analyze the model for equilibrium strategies for agents under different conditions. It is shown that the equilibrium strategies for the alternating offers model where agents take turns making offers and counter-offers, even with its extremely large action space, are equivalent to those of a much simpler single shot, take--it--or--leave--it bargaining model. In particular, agents will compute and make no offers until the first agents deadline.

Matching demand with supply in the smart grid using agent-based multiunit auction

Recent work has suggested reducing electricity generation cost by cutting the peak to average ratio (PAR) without reducing the total amount of the loads. However, most of these proposals rely on consumers willingness to act. In this paper, we propose an approach to cut PAR explicitly from the supply side. The resulting cut loads are then distributed among consumers by the means of a multiunit auction which is done by an intelligent agent on behalf of the consumer. This approach is also in line with the future vision of the smart grid to have the demand side matched with the supply side. Experiments suggest that our approach reduces overall system cost and gives benefit to both consumers and the energy provider.

Social distance games

In this paper we introduce and analyze social distance games, a family of non-transferable utility coalitional games where an agents utility is a measure of closeness to the other members of the coalition. We study both social welfare maximisation and stability in these games from a graph theoretic perspective. We investigate the welfare of stable coalition structures, and propose two new solution concepts with improved welfare guarantees. We argue that social distance games are both interesting in themselves, as well as in the context of social networks.

Combining Trust Modeling And Mechanism Design For Promoting Honesty In E-Marketplaces

In this paper, we propose a novel incentive mechanism for promoting honesty in electronic marketplaces that is based on trust modeling. In our mechanism, buyers model other buyers and select the most trustworthy ones as their neighbors to form a social network which can be used to ask advice about sellers. In addition, however, sellers model the reputation of buyers based on the social network. Reputable buyers provide truthful ratings for sellers, and are likely to be neighbors of many other buyers. Sellers will provide more attractive products to reputable buyer to build their own reputation. We theoretically prove that a marketplace operating with our mechanism leads to greater profit both for honest buyers and honest sellers. We emphasize the value of our approach through a series of illustrative examples and in direct contrast to other frameworks for addressing agent trustworthiness. In all, we offer an effective approach for the design of e‐marketplaces that is attractive to users, through its promotion of honesty.

Service allocation for composite Web services based on quality attributes

Web services are software artifacts that can be accessed over the Internet. They can be seen as pay-per-view functionalities that are exposed by some service providers. If there are multiple providers for a Web service, then both the quality and price of the service become key factors when determining which provider to choose. We consider the problem of service allocation for multiple correlated Web services that can be serviced by potentially different Web service providers. We model this problem in a game-theoretic setting and design a reverse auction where optimal service allocation for the service requester is guaranteed. We also present an optimal strategy for the service provider when choosing its quality of service.

A Trust-Based Incentive Mechanism for E-Marketplaces

In the context of electronic commerce, when modeling the trustworthiness of selling agent relies (in part) on propagating ratings provided by buying agents that have personal experience with the seller, the problem of unfair ratings arises. Extreme diversity of open and dynamic electronic marketplaces causes difficulties in handling unfair ratings in trust management systems. To ease this problem, we propose a novel trust-based incentive mechanism for eliciting fair ratings of sellers from buyers. In our mechanism, buyers model other buyers, using an approach that combines both private and public reputation values. In addition, however, sellers model the reputation of buyers. Reputable buyers provide fair ratings of sellers, and are likely considered trustworthy by many other buyers. In marketplaces operating with our mechanism, sellers will offer more attractive products to satisfy reputable buyers, in order to build their reputation. In consequence, our mechanism creates incentives for buyers to provide fair ratings of sellers, leading to more effective e-marketplaces where honest buyers and sellers can gain more profit.