A Parameterisation of Algorithms for Distributed Constraint Optimisation via Potential Games

This paper introduces a parameterisation of learning algorithms for distributed constraint optimisation problems (DCOPs). This parameterisation encompasses many algorithms developed in both the computer science and game theory literatures. It is built on our insight that when formulated as noncooperative games, DCOPs form a subset of the class of potential games. This result allows us to prove convergence properties of algorithms developed in the computer science literature using game theoretic methods. Furthermore, our parameterisation can assist system designers by making the pros and cons of, and the synergies between, the various DCOP algorithm components clear

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

Coalition Formation For Distributed Constraint Optimization Problems

This dissertation presents our research on coalition formation for Distributed Constraint Optimization Problems (DCOP). In a DCOP, a problem is broken up into many disjoint sub-problems, each controlled by an autonomous agent and together the system of agents have a joint goal of maximizing a global utility function. In particular, we study the use of coalitions for solving distributed k-coloring problems using iterative approximate algorithms, which do not guarantee optimal results, but provide fast and economic solutions in resource constrained environments. The challenge in forming coalitions using iterative approximate algorithms is in identifying constraint dependencies between agents that allow for effective coalitions to form. We first present the Virtual Structure Reduction (VSR) Algorithm and its integration with a modified version of an iterative approximate solver. The VSR algorithm is the first distributed approach for finding structural relationships, called strict frozen pairs, between agents that allows for effective coalition formation. Using coalition structures allows for both more efficient search and higher overall utility in the solutions. Secondly, we relax the assumption of strict frozen pairs and allow coalitions to form under a probabilistic relationship. We identify probabilistic frozen pairs by calculating the propensity between two agents, or the joint probability of two agents in a k-coloring problem having the same value in all satisfiable instances. Using propensity, we form coalitions in sparse graphs where strict frozen pairs may not exist, but there is still benefit to forming coalitions. Lastly, we present a cooperative game theoretic approach where agents search for Nash stable coalitions under the conditions of additively separable and symmetric value functions

Multiagent Teamwork: Hybrid Approaches

Conference paper published in CSI Communications</p

Multiagent systems: games and learning from structures

Multiple agents have become increasingly utilized in various fields for both physical robots and software agents, such as search and rescue robots, automated driving, auctions and electronic commerce agents, and so on. In multiagent domains, agents interact and coadapt with other agents. Each agent's choice of policy depends on the others' joint policy to achieve the best available performance. During this process, the environment evolves and is no longer stationary, where each agent adapts to proceed towards its target. Each micro-level step in time may present a different learning problem which needs to be addressed. However, in this non-stationary environment, a holistic phenomenon forms along with the rational strategies of all players; we define this phenomenon as structural properties. In our research, we present the importance of analyzing the structural properties, and how to extract the structural properties in multiagent environments. According to the agents' objectives, a multiagent environment can be classified as self-interested, cooperative, or competitive. We examine the structure from these three general multiagent environments: self-interested random graphical game playing, distributed cooperative team playing, and competitive group survival. In each scenario, we analyze the structure in each environmental setting, and demonstrate the structure learned as a comprehensive representation: structure of players' action influence, structure of constraints in teamwork communication, and structure of inter-connections among strategies. This structure represents macro-level knowledge arising in a multiagent system, and provides critical, holistic information for each problem domain. Last, we present some open issues and point toward future research