Extending Bayesian Logic Programs for Plan Recognition and Machine Reading
Abstract
Statistical relational learning (SRL) is the area of machine learning that integrates both first-order logic and probabilistic graphical models. The advantage of these formalisms 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 proposal, we focus on applying BLPs to two real worlds tasks--plan recognition and machine reading. Plan recognition is the task of predicting an agent's top-level plans based on its observed actions. It is an abductive reasoning task that involves inferring cause from effect. In the first part of the proposal, we develop an approach to abductive plan recognition using BLPs. Since BLPs employ logical deduction to construct the networks, they cannot be used effectively for plan recognition as is. Therefore, we extend BLPs to use logical abduction to construct Bayesian networks and call the resulting model Bayesian Abductive Logic Programs (BALPs). Experimental evaluation on three benchmark data sets demonstrate that BALPs outperform the existing state-of-art methods like Markov Logic Networks (MLNs) for plan recognition. For future work, we propose to apply BLPs to the task of machine reading, which involves automatic extraction of knowledge from natural language text. Present day information extraction (IE) systems that are trained for machine reading are limited by their ability to extract only factual information that is stated explicitly in the text. We propose to improve the performance of an off-the-shelf IE system by inducing general knowledge rules about the domain using the facts already extracted by the IE system.
Document Details
- Document Type
- Technical Report
- Publication Date
- May 01, 2011
- Accession Number
- ADA544932
Entities
People
- Sindhu V. Raghavan
Organizations
- University of Texas at Arlington