Bayesian Logic Programs for Plan Recognition and Machine Reading

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.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 2012
Accession Number
ADA575748

Entities

People

  • Sindhu V. Raghavan

Organizations

  • University of Texas at Austin

Tags

Communities of Interest

  • Biomedical
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Artificial Intelligence Computing
  • Bayesian Networks
  • Computational Science
  • Computer Languages
  • Computer Science
  • Data Mining
  • Databases
  • Information Processing
  • Language
  • Machine Learning
  • Natural Language Processing
  • Ontologies
  • Probabilistic Models
  • Probability
  • Probability Distributions
  • Reasoning

Fields of Study

  • Computer science

Readers

  • Distributed Systems and Data Platform Development
  • Mathematical Modeling and Probability Theory.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks