Lecture notes in computer science vol:5212 pages:506-521
European Conference on Machine Learning edition:19 location:Antwerp, Belgium date:September 15th to 19th, 2008
Artificial intelligence aims at developing agents that learn and act in
complex environments. Realistic environments typically feature a variable number of objects, relations amongst them, and non-deterministic transition behavior.
Standard probabilistic sequence models provide efficient inference and learning techniques, but typically cannot fully capture the relational complexity. On the other hand, statistical relational learning techniques are often too inefficient. In this paper, we present a simple model that occupies an intermediate position in this expressiveness/efficiency trade-off. It is based on CP-logic, an expressive probabilistic logic for modeling causality. However, by specializing CP-logic to represent a probability distribution over sequences of relational state descriptions, and employing a Markov assumption, inference and learning become more tractable and effective. We show that the resulting model is able to handle probabilistic relational domains with a substantial number of objects and relations.