Adaptive Learning in Evolving Task Allocation Networks (extended abstract)

Software Computer TechnologyElectrical Engineering, Mathematics and Computer Scienc

Evolutionary Dynamics for Designing Multi-Period Auctions (extended abstract)

Mechanism design (MD) has recently become a very popular approach in the design of distributed systems of autonomous agents. Also called ‘inverse game theory’ [4], MD is concerned with designing the games or systems in which agents interact, and to do this in such a way that rational agent behavior in those games leads to certain desirable properties for the system as a whole. A key assumption in MD is that agents behave rationally, since this provides the predictability of agent behavior required for optimizing the mechanism’s design. In many practical circumstances, however, agents don’t behave rationally since, in general, finding Nash equilibrium strategies to play is intractable [2]. Because of such negative results, many have resorted to heuristic approaches to these problems. Here, we propose studying the interaction between the mechanism designer and the game participants as a higher level, ‘meta-game,’ in which the designer chooses among alternative mechanism designs, while the agents choose among alternative strategies to play. We solve this game ‘heuristically’ using evolutionary game theory techniques, specifically, the (coupled) replicator dynamics (RD) [3]. To illustrate, we adopt the multi-period auction scenario developed by Pardoe and Stone (PS) [5].Software Computer TechnologyElectrical Engineering, Mathematics and Computer Scienc

Optimal Temporal Decoupling in Task Scheduling with Preferences

Multi-agent planning and scheduling concerns finding a joint plan to achieve some set of common goals with several independent agents each aiming to find a plan or schedule for their part of the goals. To avoid conflicts in these individual plans or schedules decoupling is used. Such a decoupling entails adding local constraints for the agents such that they can schedule autonomously within those constraints, while they guarantee that a conflict-free global solution can be constructed from the individual agents’ schedules. In this paper we investigate finding an ‘optimal’ decoupling, that maximizes the sum of the agents’ preferences about their scheduling of tasks. We show using a Linear Programming (LP) approach that optimal decouplings can be found efficiently by exploiting the properties of a task scheduling instance.Software Computer TechnologyElectrical Engineering, Mathematics and Computer Scienc

Enumeration and Exact Design of Weighted Voting Games (extended abstract)

Software Computer TechnologyElectrical Engineering, Mathematics and Computer Scienc

Evaluation and Improvement of Laruelle-Widgrén Inverse Banzhaf Approximation

Voting is a popular way of reaching decisions in multi-agent systems. Weighted voting in particular allows different agents to have varying levels of influence on the decision taken: each agent’s vote carries a weight, and a proposal is accepted if the sum of the weights of the agents in favor of the proposal is at least equal to a given quota. Unfortunately, there is no clear and unambiguous relation between a player’s weight and the extent of her influence on the outcome of the decision making process. Different measures of ‘power’ have been proposed, such as the Banzhaf and the Shapley-Shubik indices. Here we consider the ‘inverse’ problem: given a vector of desired power indices for the players, how should we set their weights and the quota such that the players’ power in the resulting game comes as close as possible to the target vector? There has been some work on this problem, both heuristic and exact, but little is known about the approximation quality of the heuristics for this problem. The goal of this paper is to empirically evaluate the heuristic algorithm for the Inverse Banzhaf Index problem by Laruelle and Widgrén. We analyze and evaluate the intuition behind this algorithm. We found that the algorithm can not handle general inputs well, and often fails to improve inputs. It is also shown to diverge after only tens of iterations. Based on our analysis, we present three alternative extensions of the algorithm that do not alter the complexity but can result in up to a factor 6.5 improvement in solution quality.Software Computer TechnologyElectrical Engineering, Mathematics and Computer Scienc

Flexibility and Decoupling in the Simple Temporal Problem (abstract)

Software Computer TechnologyElectrical Engineering, Mathematics and Computer Scienc