Learning World Models in Environments with Manifest Causal Structure,
Abstract
This thesis examines the problem of an autonomous agent learning a causal world model of its environment. Previous approaches to learning causal world models have concentrated on environments that are too "easy" (deterministic finite state machines) or too "hard" (containing much hidden state). We describe a new domain ?
Document Details
- Document Type
- Technical Report
- Publication Date
- May 01, 1995
- Accession Number
- ADA298004
Entities
People
- Ruth Bergman
Organizations
- Massachusetts Institute of Technology