Constraint Optimisation Techniques for Real-World Applications
Filippo Bistaffa · 2016
Constraint optimisation represents a fundamental technique that has been successfully employed in Multi-Agent Systems (MAS) in order to face a number of multi-agent coordination challenges. In this thesis we focus on Coalition Formation (CF), one of the key approaches for coordination in MAS. CF aims at the formation of groups that maximise a particular objective functions (e.g., arrange shared rides among multiple agents in order to minimise travel costs). Specifically, we discuss a special case of CF known as Graph-Constrained CF (GCCF) where a network connecting the agents constrains the formation of coalitions. We focus on this type of problem given that in many real-world applications, agents may be connected by a communication network or only trust certain peers in their social network. In particular, the contributions of this thesis are the following. We propose a novel representation of this problem and we design an efficient solution algorithm, i.e., CFSS. We evaluate CFSS on GCCF scenarios like collective energy purchasing and social ridesharing using realistic data (i.e., energy consumption profiles from households in the UK, GeoLife for spatial data, and Twitter as social network). Results show that CFSS outperforms state of the art GCCF approaches both in terms of runtime and scalability. CFSS is the first algorithm that provides solutions with good quality guarantees for large-scale GCCF instances with thousands of agents (i.e., more that 2700). In addition, we address the problem of computing the transfer or payment to each agent to ensure it is fairly rewarded for its contribution to its coalition. This aspect of CF, denoted as payment computation, is of utmost importance in scenario characterised by agents with rational behaviours, such as collective energy purchasing and social ridesharing. In this perspective, we propose PRF, the first method to compute payments in large-scale GCCF scenarios that are also stable in a game-theoretic sense. Finally, we provide an alternative method for the solution of GCCF, by exploiting the close relation between such problem and Constraint Optimisation Problems (COPs). We consider Bucket Elimination (BE), one of the most important algorithmic frameworks to solve COPs, and we propose CUBE, a highly-parallel GPU implementation of the most computationally intensive operations of BE. CUBE adopts an efficient memory layout that results in a high computational throughput. In addition, CUBE is not limited by the amount of memory of the GPU and,