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Accession Number ADA575748
Title Bayesian Logic Programs for Plan Recognition and Machine Reading.
Publication Date Dec 2012
Media Count 191p
Personal Author S. V. Raghavan
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.
Keywords Blps(Bayesian logic programs)
Computer logic
Learning machines
Probability
Srl(Statistical relational learning)
Theses

 
Source Agency Non Paid ADAS
NTIS Subject Category 62B - Computer Software
Corporate Author Texas Univ. at Austin.
Document Type Thesis
Title Note Doctoral thesis.
NTIS Issue Number 1319
Contract Number W911NF-08-1-0242 FA8750-09-C-0172

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