Reasoning about Probabilities in Neural Networks.

In this chapter, we show that artificial neural networks can reason about probabilities, thus being able to integrate reasoning about uncertainty with modal, temporal, and epistemic logics, which have found a large number of applications, notably in game theory and in models of knowledge and interaction in multiagent systems [87,103,207]; artificial intelligence and computer science have made extensive use of decidable modal logics, including in the analysis and model checking of distributed and multiagent systems, in program verification and specification, and in hardware model checking. Finally, the combination of knowledge, time, and probability in a connectionist system provides support for integrated knowledge representation and learning in a distributed environment, dealing with the various dimensions of reasoning of an idealised agent [94, 202]. [ABSTRACT FROM AUTHOR]