Combining Associational and Causal Reasoning to Solve Interpretation and Planning Problems

Item

Title: en_US Combining Associational and Causal Reasoning to Solve Interpretation and Planning Problems
Creator: en_US Simmons, Reid G.
Date: 2004-10-20T20:01:11Z
Date Available: 2004-10-20T20:01:11Z
Date Issued: en_US 1988-08-01
Identifier: en_US AITR-1048
uri: http://hdl.handle.net/1721.1/6842
Abstract: en_US This report describes a paradigm for combining associational and causal reasoning to achieve efficient and robust problem-solving behavior. The Generate, Test and Debug (GTD) paradigm generates initial hypotheses using associational (heuristic) rules. The tester verifies hypotheses, supplying the debugger with causal explanations for bugs found if the test fails. The debugger uses domain-independent causal reasoning techniques to repair hypotheses, analyzing domain models and the causal explanations produced by the tester to determine how to replace faulty assumptions made by the generator. We analyze the strengths and weaknesses of associational and causal reasoning techniques, and present a theory of debugging plans and interpretations. The GTD paradigm has been implemented and tested in the domains of geologic interpretation, the blocks world, and Tower of Hanoi problems.
Extent: en_US 215 p.; 20424253 bytes; 15960716 bytes
Format: application/postscript; application/pdf
Language: en_US
Relation: en_US AITR-1048
Subject: en_US associational reasoning; en_US causal reasoning; en_US planning; en_US sgeologic interpretation; en_US debugging

Item sets: AI Technical Reports (1964 - 2004)