Learning World Models in Environments with Manifest Causal Structure

Item

Title
en_US Learning World Models in Environments with Manifest Causal Structure
Creator
en_US Bergman, Ruth
Date
2004-10-20T14:45:25Z
Date Available
2004-10-20T14:45:25Z
Date Issued
en_US 1995-05-05
Identifier
en_US AITR-1513
uri
http://hdl.handle.net/1721.1/6777
Abstract
en_US 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 --- environments with manifest causal structure --- for learning. In such environments the agent has an abundance of perceptions of its environment. Specifically, it perceives almost all the relevant information it needs to understand the environment. Many environments of interest have manifest causal structure and we show that an agent can learn the manifest aspects of these environments quickly using straightforward learning techniques. We present a new algorithm to learn a rule-based causal world model from observations in the environment. The learning algorithm includes (1) a low level rule-learning algorithm that converges on a good set of specific rules, (2) a concept learning algorithm that learns concepts by finding completely correlated perceptions, and (3) an algorithm that learns general rules. In addition this thesis examines the problem of finding a good expert from a sequence of experts. Each expert has an "error rate"; we wish to find an expert with a low error rate. However, each expert's error rate and the distribution of error rates are unknown. A new expert-finding algorithm is presented and an upper bound on the expected error rate of the expert is derived.
Extent
en_US 142 p.
12411678 bytes
1775267 bytes
Format
application/postscript
application/pdf
Language
en_US
Relation
en_US AITR-1513
Subject
en_US machine learning
en_US intelligent agents
Item sets
AI Technical Reports (1964 - 2004)