Accession Number : ADA575748


Title :   Bayesian Logic Programs for Plan Recognition and Machine Reading


Descriptive Note : Doctoral thesis


Corporate Author : TEXAS UNIV AT AUSTIN


Personal Author(s) : Raghavan, Sindhu V


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/a575748.pdf


Report Date : Dec 2012


Pagination or Media Count : 191


Abstract : Several real world tasks involve data that is uncertain and relational in nature. Traditional approaches like first-order logic and probabilistic models either deal with structured data or uncertainty, but not both. To address these limitations, statistical relational learning (SRL), a new area in machine learning integrating both first-order logic and probabilistic graphical models, has emerged in the recent past. The advantage of SRL models is that they can handle both uncertainty and structured/ relational data. As a result, they are widely used in domains like social network analysis, biological data analysis, and natural language processing. Bayesian Logic Programs (BLPs), which integrate both first-order logic and Bayesian networks are a powerful SRL formalism developed in the recent past. In this dissertation, we develop approaches using BLPs to solve two real world tasks plan recognition and machine reading.


Descriptors :   *COMPUTER LOGIC , LEARNING MACHINES , PROBABILITY , THESES


Subject Categories : Computer Programming and Software


Distribution Statement : APPROVED FOR PUBLIC RELEASE