We propose Markov random fields (MRFs) as a probabilistic mathematical model for unifying approaches to multi-robot coordination or, more specifically, distributed action selectio...
Jesse Butterfield, Odest Chadwicke Jenkins, Brian ...
In this paper we present an extension of logic programming (LP) that is suitable not only for the "rational" component of a single agent but also for the "reactive&...
An important issue in reinforcement learning is how to incorporate expert knowledge in a principled manner, especially as we scale up to real-world tasks. In this paper, we presen...
Eric Wiewiora, Garrison W. Cottrell, Charles Elkan
Partially-observable Markov decision processes (POMDPs) provide a powerful model for sequential decision-making problems with partially-observed state and are known to have (appro...
Abstract. The paper presents an intelligent GIS architecture that enables us to extend GIS functionality by using domain specific knowledge and inference engine. In this architectu...