Automated compilation of Object-Oriented Probabilistic Relational Models
This paper addresses the issues of knowledge representation and reasoning in large, complex, uncertain domains, focusing on the tactical military domain, which is characterized by all these properties. The key to reasoning efficiently under these circumstances is to provide a knowledge representation language and reasoning techniques which take advantage of the structure of the domain and facilitate reuse. First order representations such as relational logic are useful for representing structured domains because they can represent both entities and relations. However, the constraint that first order logic statements must be either true or false makes these languages unsuited to representing real world domains which involve uncertainty. Probability theory, on the other hand, provides a sound mathematical basis for representing and reasoning with uncertain information. For example, Bayesian Networks (BNs) are a well known probabilistic representation technique. However, there are several characteristics of large, complex domains which are challenging for traditional BNs. Recent research, for example [1,2], has shown there are advantages to be derived from combining probability theory with some of the expressive power of first order logics. Languages which combine probability theory with aspects of first order logic are called First Order Probabilistic Languages (FOPLs). There are a number of such languages, for example [1-4]. FOPLs have been used to model a number of domains such as military situation awareness [2], traffic surveillance [3], information extraction [5], natural language processing [6], intelligent tutoring systems [7], web site user behavior modelling [8] and automated internet fault diagnosis [9]. In this paper, we present the Object-Oriented Probabilistic Relational Modelling Language (OPRML) [10,11], a new FOPL which combines the generality and modularity of relational logic representation with a principled treatment of uncertainty. We describe the language in detail, outlining its formal syntax and semantics and compare it against its most closely related language: Probabilistic Relational Models (PRMs). We also present four novel algorithms for the automatic construction of domain models from knowledge-bases expressed using the OPRML. Two of the algorithms are based on the knowledge-based model construction approach and two are based on an Object-Oriented Bayesian Network instance tree triangulation method. We discuss the strengths and limitations of each of the algorithms and compare their performance against the algorithms developed for PRMs. Crown Copyright (C) 2009 Published by Elsevier Inc. All rights reserved.
knowledge representation first order probabilistic languages uncertainty reasoning