---
title: "A Simple-Transition Model for Relational Sequences"
authors:
  - "Alan Fern"
doi: ""
url: "https://ijcai.org/proceedings/2005/31"
pdf: "https://ijcai.org/proceedings/2005/0031.pdf"
venue: "Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence"
year: 2005
pages: "696-700"
track: ""
keywords:
---

# A Simple-Transition Model for Relational Sequences

Alan Fern

*Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence*, pages 696-700.

## 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.