A stochastic language for modelling opponent agents

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 Probabl

Autonomous Agents and Multiagent Systems (AAMAS), 2006. A Stochastic Language for Modelling Opponent Agents ABSTRACT

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

Finding Most Probable Worlds of Probabilistic Logic Programs

Abstract. Probabilistic logic programs have primarily studied the problem of entailment of probabilistic atoms. However, there are some interesting applications where we are interested in finding a possible world that is most probable. Our first result shows that the problem of computing such ”maximally probable worlds” (MPW) is intractable. We subsequently show that we can often greatly reduce the size of the linear program used in past work (by Ng and Subrahmanian) and yet solve the problem exactly. However, the intractability results still make computational efficiency quite impossible. We therefore also develop several heuristics to solve the MPW problem and report extensive experimental results on the accuracy and efficiency of such heuristics.