A Simple Model for Sequences of Relational State Descriptions

author: Ingo Thon, KU Leuven
author: Niels Landwehr, University of Freiburg
author: Luc De Raedt, Department of Computer Science, KU Leuven
published: Oct. 10, 2008,   recorded: September 2008,   views: 3431


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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.

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