Advances in practical optimal coalition structure algorithms

This thesis presents a number of algorithms for forming coalitions among cooperative agents in pragmatic domains where traditional cooperative game theory solution concepts do not apply due to bounded rationality of agents. While previous work in coalition formation in multi-agent systems research operated on relatively small number of agents, e.g. less than 30 agents, this work explores coalition formation among 100 agents, this is due to limited computational resources not the performance of the our algorithms. We explore a bestfirst search centralized algorithm for optimal coalition structures which is based on a novel idea of deciding what is the best coalition to put into coalition structure being generated. Empirical results show that the solution reaches optimality quickly and terminates quickly in pragmatic domains. We further explore on optimal coalition structures with distributed algorithms in linear and non-linear domains. For the linear domains, we explore linear production and integer programming. For the non-linear domains we explore logistic providers. Based on existing algorithms, we explore a novel environment of forming coalitions in supply networks involving buyers, sellers and logistics providers agents. In this setting, buyers form coalitions to increase their negotiation power while sellers and logistics providers form coalitions to aggregate their supply power and optimize their resources usage

Controlled Matching Game for Resource Allocation and User Association in WLANs

In multi-rate IEEE 802.11 WLANs, the traditional user association based on the strongest received signal and the well known anomaly of the MAC protocol can lead to overloaded Access Points (APs), and poor or heterogeneous performance. Our goal is to propose an alternative game-theoretic approach for association. We model the joint resource allocation and user association as a matching game with complementarities and peer effects consisting of selfish players solely interested in their individual throughputs. Using recent game-theoretic results we first show that various resource sharing protocols actually fall in the scope of the set of stability-inducing resource allocation schemes. The game makes an extensive use of the Nash bargaining and some of its related properties that allow to control the incentives of the players. We show that the proposed mechanism can greatly improve the efficiency of 802.11 with heterogeneous nodes and reduce the negative impact of peer effects such as its MAC anomaly. The mechanism can be implemented as a virtual connectivity management layer to achieve efficient APs-user associations without modification of the MAC layer

The computational complexity of rationalizing Pareto optimal choice behavior

We consider a setting where a coalition of individuals chooses one or several alternatives from each set in a collection of choice sets. We examine the computational complexity of Pareto rationalizability. Pareto rationalizability requires that we can endow each individual in the coalition with a preference relation such that the observed choices are Pareto efficient. We differentiate between the situation where the choice function is considered to select all Pareto optimal alternatives from a choice set and the situation where it only contains one or several Pareto optimal alternatives. In the former case we find that Pareto rationalizability is an NP-complete problem. For the latter case we demonstrate that, if we have no additional information on the individual preference relations, then all choice behavior is Pareto rationalizable. However, if we have such additional information, then Pareto rationalizability is again NP-complete. Our results are valid for any coalition of size greater or equal than two.

A tutorial on optimization for multi-agent systems

Research on optimization in multi-agent systems (MASs) has contributed with a wealth of techniques to solve many of the challenges arising in a wide range of multi-agent application domains. Multi-agent optimization focuses on casting MAS problems into optimization problems. The solving of those problems could possibly involve the active participation of the agents in a MAS. Research on multi-agent optimization has rapidly become a very technical, specialized field. Moreover, the contributions to the field in the literature are largely scattered. These two factors dramatically hinder access to a basic, general view of the foundations of the field. This tutorial is intended to ease such access by providing a gentle introduction to fundamental concepts and techniques on multi-agent optimization. © 2013 The Author.Peer Reviewe

The computational complexity of rationalizing Pareto optimal choice behavior.

We consider a setting where a coalition of individuals chooses one or several alternatives from each set in a collection of choice sets. We examine the computational complexity of Pareto rationalizability. Pareto rationalizability requires that we can endow each individual in the coalition with a preference relation such that the observed choices are Pareto efficient. We differentiate between the situation where the choice function is considered to select all Pareto optimal alternatives from a choice set and the situation where it only contains one or several Pareto optimal alternatives. In the former case we find that Pareto rationalizability is an NP-complete problem. For the latter case we demonstrate that, if we have no additional information on the individual preference relations, then all choice behavior is Pareto rationalizable. However, if we have such additional information, then Pareto rationalizability is again NP-complete. Our results are valid for any coalition of size greater or equal than two.

A Game Theoretic Analysis of Incentives in Content Production and Sharing over Peer-to-Peer Networks

User-generated content can be distributed at a low cost using peer-to-peer (P2P) networks, but the free-rider problem hinders the utilization of P2P networks. In order to achieve an efficient use of P2P networks, we investigate fundamental issues on incentives in content production and sharing using game theory. We build a basic model to analyze non-cooperative outcomes without an incentive scheme and then use different game formulations derived from the basic model to examine five incentive schemes: cooperative, payment, repeated interaction, intervention, and enforced full sharing. The results of this paper show that 1) cooperative peers share all produced content while non-cooperative peers do not share at all without an incentive scheme; 2) a cooperative scheme allows peers to consume more content than non-cooperative outcomes do; 3) a cooperative outcome can be achieved among non-cooperative peers by introducing an incentive scheme based on payment, repeated interaction, or intervention; and 4) enforced full sharing has ambiguous welfare effects on peers. In addition to describing the solutions of different formulations, we discuss enforcement and informational requirements to implement each solution, aiming to offer a guideline for protocol designers when designing incentive schemes for P2P networks.Comment: 31 pages, 3 figures, 1 tabl

Optimal Creation of Agent Coalitions for Manufacturing and Control

Cooperation among agents has been the object of many recently published papers. Cooperation might be formulated and work in many forms, between different kinds of agents and situations in which they are situated. In addition, it is also influenced a lot by the agent's intelligence, mutual relationships and the willingness to cooperate with other ones. The main focus of this paper is to solve the problem of how to create optimal coalitions of the given agents with the purpose to improve the collective performance. The coalition is a possible form of cooperation in which the common goal has the highest priority for all members included in it. Further, we introduce methods for finding the sub-optimal solutions, which are able to approximate the range of the optimal solutions. Finally we discuss the problem of creating coalition with more parameters