Abstract-- Recent research on multi-agent systems has produced a plethora of decentralized controllers that implicitly assume various degrees of agent localization. However, many p...
In this paper, we present a general machine learning approach to the problem of deciding when to share probabilistic beliefs between agents for distributed monitoring. Our approac...
In this paper, we present several general policies for deciding when to share probabilistic beliefs between agents for distributed monitoring. In order to evaluate these policies,...
This paper develops Probabilistic Hybrid Action Models (PHAMs), a realistic causal model for predicting the behavior generated by modern concurrent percept-driven robot plans. PHA...
We discuss the problem of overfitting of probabilistic neural networks in the framework of statistical pattern recognition. The probabilistic approach to neural networks provides a...