Aneurin M. Easwaran, Jeremy Pitt
Deregulation of telecommunications has meant an increase in third-party service provision, personalized service delivery and integrated networks and media. The efficient allocation of services, without human intervention, to satisfy advanced service requirements spanning several networks is a crucial task. This can be modeled as a winner determination problem in combinatorial auctions where there are multiple services, service providers and winner determination criteria (like cost, bandwidth, delay, etc) but we have shown the problem is NP-complete. This paper describes a new two-stage algorithm for optimal anytime winner determination. In the first stage, a hierarchical task network planner is used to decompose a task into subtasks that can be solved by the available services. In the second stage, a genetic algorithm with heuristics is used to find the optimal combination of service providers to provide the services identified. We present our algorithm used to solve the second stage in detail and the results from various experiments. The results show the GA finds optimal solutions much quicker than a modified depth-first search with pruning. We also show the genetic algorithm a) finds optimal solutions quicker when deal lengths have a random distribution and b) initial anytime performance is better when deal lengths have an exponential distribution.
Keywords: Multi-Agent Systems, Genetic Algorithms, AI Architectures
Citation: Aneurin M. Easwaran, Jeremy Pitt: An Agent Service Brokering Algorithm for Winner Determination in Combinatorial Auctions. In W.Horn (ed.): ECAI2000, Proceedings of the 14th European Conference on Artificial Intelligence, IOS Press, Amsterdam, 2000, pp.286-290.