(Enter summary)
Abstract: AI planning agents are goal-directed: success is measured in terms of whether or
not an input goal is satisfied, and the agent's computational processes are driven by
those goals. A decision-theoretic agent, on the other hand, has no explicit goals---
success is measured in terms of its preferences or a utility function that respects those
preferences.
The two approaches have complementary strengths and weaknesses. Symbolic planning
provides a computational theory of plan generation, but under... (Update)
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BibTeX entry: (Update)
Peter Haddawy and Steve Hanks. Utility Models for Goal-Directed Decision-Theoretic Planners. Technical Report 93--06--04, Univ. of Washington, Dept. of Computer Science and Engineering, September 1993. Submitted to Artificial Intelligence. Available via FTP from pub/ai/ at cs.washington.edu. http://citeseer.ist.psu.edu/haddawy93utility.html More
@techreport{ haddawy93utility,
author = "Haddawy and S. Hanks",
title = "Utility Models for Goal-Directed Decision Theoretic Planners",
number = "TR-93-06-04",
year = "1993",
url = "citeseer.ist.psu.edu/haddawy93utility.html" }
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Hierarchical Planning involving deadlines (context) - Dean, Firby et al. - 1988
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35
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Embedding Decision-analytic Control in a Learning Architectu.. (context) - Etzioni - 1991
29
Representing Plans Under Uncertainty: A Logic of Time (context) - Haddawy - 1991
27
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25
Projecting Plans for Uncertain Worlds (context) - Hanks - 1990
23
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Linear Goal Programming (context) - Schniederjans - 1984
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