Learning in open adaptive networks

International audienceWe propose a learn-and-adapt model for building efficient and resilient networks of cooperative agents. Agents are involved into three interconnected types of activities. Firstly, agents bid for handling random structured tasks. Secondly, agents learn the (exogenous) features of the random task source, by aggregating local information (such as success rates, average load, etc). And, thirdly, agents adapt the composition of their neighbourhoods following the (endoge-nous) targets set by their learning process. Neighbourhood readjustment proceeds by judicious rewiring steps which stay entirely local. Thus an agent continuously works, adjusts its neighbourhood , and based on his local metrics, learns how to inflect its own adaptation targets. Because of this tight coupling of all three activities, the network as a whole can reconfigure in a fully decentralized way to cope with changes in: the network composition (node failures, new incoming nodes, etc), and the parameters of the task source (changes in the size, structure, and frequency), while attaining robustly a near-optimal performance level (compared to the centralised solution)

Heuristic methods for coalition structure generation

The Coalition Structure Generation (CSG) problem requires finding an optimal partition of a set of n agents. An optimal partition means one that maximizes global welfare. Computing an optimal coalition structure is computationally hard especially when there are externalities, i.e., when the worth of a coalition is dependent on the organisation of agents outside the coalition. A number of algorithms were previously proposed to solve the CSG problem but most of these methods were designed for systems without externalities. Very little attention has been paid to finding optimal coalition structures in the presence of externalities, although externalities are a key feature of many real world multiagent systems. Moreover, the existing methods, being non-heuristic, have exponential time complexity which means that they are infeasible for any but systems comprised of a small number of agents. The aim of this research is to develop effective heuristic methods for finding optimal coalition structures in systems with externalities, where time taken to find a solution is more important than the quality of the solution. To this end, four different heuristics methods namely tabu search, simulated annealing, ant colony search and particle swarm optimisation are explored. In particular, neighbourhood operators were devised for the effective exploration of the search space and a compact representation method was formulated for storing details about the multiagent system. Using these, the heuristic methods were devised and their performance was evaluated extensively for a wide range of input data

COIN@AAMAS2015

COIN@AAMAS2015 is the nineteenth edition of the series and the fourteen papers included in these proceedings demonstrate the vitality of the community and will provide the grounds for a solid workshop program and what we expect will be a most enjoyable and enriching debate.Peer reviewe