Multi-agent problem domains may require distributed algorithms for a variety of reasons: local sensors, limitations of communication, and availability of distributed computational resources. In the absence of these constraints, centralized algorithms are often more efficient, simply because they are able to take advantage of more information. We introduce a variant of the cooperative target observation domain which is free of such constraints. We propose two algorithms, inspired by K-means clustering and hill-climbing respectively, which are scalable in degree of decentralization. Neither algorithm consistently outperforms the other across over all problem domain settings. Surprisingly, we find that hill-climbing is sensitive to degree of decentralization, while K-means is not. We also experiment with a combination of the two algorithms which draws strength from each.
['Sean Luke', 'Keith Sullivan', 'Liviu Panait', 'Gabriel Catalin Balan']
Tunably decentralized algorithms for cooperative target observation