A Simple-Transition Model for Relational Sequences

Abstract

We use “nearly sound ” logical constraints to infer hidden states of relational processes. We introduce a simple-transition cost model, which is parameterized by weighted constraints and a statetransition cost. Inference for this model, i.e. finding a minimum-cost state sequence, reduces to a single-state minimization (SSM) problem. For relational Horn constraints, we give a practical approach to SSM based on logical reasoning and bounded search. We present a learning method that discovers relational constraints using CLAUDIEN [De Raedt and Dehaspe, 1997] and then tunes their weights using perceptron updates. Experiments in relational video interpretation show that our learned models improve on a variety of competitors. 1