A stochastic language for modelling opponent agents
Gerardo I. Simari, Amy L. Sliva, Dana S. Nau, V. S. Subrahmanian · 2006
There are numerous cases where a reasoning agent needs to reason about the behavior of an opponent agent. In this paper, we propose a hybrid probabilistic logic language within which we can express what actions an opponent may take in a given situation. We present the syntaxis and semantics of the language, and the concept of a Maximally Probable Course of Action.