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Title: Inference processes for probabilistic first order languages
Author: Rad, Soroush Rafiee
ISNI:       0000 0004 2680 6264
Awarding Body: The University of Manchester
Current Institution: University of Manchester
Date of Award: 2009
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In this thesis we will investigate inference processes for predicate languages. The main question we are concerned with in this thesis is how to choose a probability function amongst those that satisfy a certain knowledge base. This question has been extensively studied for propositional logic and we shall investigate it for first order languages. We will first study the generalisation of Minimum Distance. MD. and Centre of Mass. CMco inference processes to unary predicate languages and then we will investigate the generalisations of itie Maximum Entropy inference process to general polyadic languages. For the case of the Maximum Entropy inference process we will study and compare two generalisations. the BP-method and the W-method. We will show that the two methods agree for the unary and :E, knowledge bases and we conjecture that the result holds for the II, knowledge bases too. We shall show that neither of these generalisations for the Maximum Entropy inference process is universally well defined for a first order language and we shall study some of the problems associated with generalising this inference process to polyadic languages.
Supervisor: Not available Sponsor: Not available
Qualification Name: Not available Qualification Level: Doctoral
EThOS ID:  DOI: Not available