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 :

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.


Subject Categories : Computer Programming and Software

Distribution Statement : APPROVED FOR PUBLIC RELEASE