Causal Reconstruction
Abstract
Causal reconstruction is the task of reading a written causal description of a physical behavior, forming an internal model of the described activity, and demonstrating comprehension through question answering. This task is difficult because written descriptions often do not specify exactly how referenced events fit together. This article (1) characterizes the causal reconstruction problem, (2) presents a representation called transition space, which portrays events in terms of transitions, or collections of changes expressible in everyday language, and (3) describes a program called PATHFINDER, which uses the transition space representation to perform casual reconstruction on simplified English descriptions of physical activity. PATHFINDER works by identifying partial matches between the representations of events and using these matches to form causal chains, fill causal gaps, and merge overlapping accounts of activity. By applying transformations to events prior to matching, PATHFINDER is also able to handle a range of discontinuities arising from a writer's use of analogy or abstraction. Explanation, Knowledge representation, Analogy, Natural language, Abstraction, Causal reasoning.
Document Details
- Document Type
- Technical Report
- Publication Date
- Feb 01, 1993
- Accession Number
- ADA271692
Entities
People
- Gary C. Brochardt
Organizations
- Massachusetts Institute of Technology