A key issue in artificial intelligence lies in finding the amount of input detail needed to do successful learning. Too much detail causes overhead and makes learning prone to ove...
In this paper, we present a multi-agent control method for a large-scale network system. We propose an extension of a token-based coordination technique to improve the tradeoff be...
We have introduced a search engine that can extract opinion sentences relevant to an open-domain query from Japanese blog pages. The engine identifies opinions based not only on p...
It is now well-known that the size of the model is the bottleneck when using model-based approaches to diagnose complex systems. To answer this problem, decentralized/distributed ...
In this paper we propose a framework for decentralized model-based diagnosis of complex systems modeled with qualitative constraints and whose models are distributed among their s...
We investigate legal and philosophical notions of privacy in the context of artificial agents. Our analysis utilizes a normative account of privacy that defends its value and the ...
In this paper we present an improved version of the Probabilistic Ant based Clustering Algorithm for Distributed Databases (PACE). The most important feature of this algorithm is ...
A key problem in reinforcement learning is finding a good balance between the need to explore the environment and the need to gain rewards by exploiting existing knowledge. Much ...