Probabilistic logic based on Bayesian network with first order inference.

Author(s)

Rodrigo Bellizia Polastro

• Document type: Doctoral Thesis
• Press: São Paulo.
• Institution: Universidade de São Paulo (USP). Escola Politécnica (EP/BC)
• Defense date: 2012-05-03

Examining board members

Fabio Gagliardi Cozman; Jaime Shinsuke Ide; Newton Maruyama; Kate Cerqueira Revoredo; José Reinaldo Silva

• Advisor: Fabio Gagliardi Cozman

Abstract

This work presents two major contributions: i. a new probabilistic description logic; ii. a new algorithm for inference in terminologies expressed in this logic; iii. practical applications in real tasks. The proposed logic, referred to as crALC (credal ALC), adds probabilistic inclusions to the popular logic ALC, combining the usual acyclicity and Markov conditions, and adopting interpretation-based semantics. As exact inference does not seem scalable due to the presence of quantifiers (existential and universal), we present a first-order loopy propagation algorithm that behaves appropriately for non-trivial domain sizes. A series of tests were done comparing the performance of the proposed algorithm against traditional ones; the presented results are favorable to the first-order algorithm. Two applications in the field of mobile robotics are presented, using the new probabilistic logic and the inference algorithm. Though the problems can be considered simple, they constitute the basis for many other tasks in mobile robotics, being a important step in knowledge representation and in reasoning about it. (AU)