Subject: Master's thesis available for anonymous FTP or WWW access

From: [email-masked] (Michael P. Frank)
Date: Sat, 7 May 94 13:03:50 EDT

The following Master's thesis is available for anonymous FTP from
medg.lcs.mit.edu in the directory "/pub/mpf/papers/Frank-94".  It
is also available on the World-Wide Web at the URL:
	http://medg.lcs.mit.edu/mpf/papers/Frank/Frank-94/Frank-94.html

Some of you may be particularly interested in section 2.5.4, which
presents some direct measurements of the amount of interdependence
between the values of sibling nodes in the game trees of some actual
games.  These results are useful for evaluating the validity of the
independence assumptions made by the Probability Product [Pearl-81]
and BPIP [Baum-92] node-value propagation techniques.

Any comments would be greatly appreciated.

Thanks,
Mike Frank
    
----------------------------------------------------------------------    
[AAAI-92] Artificial Intelligence: Proceedings of the 10th National
    Conference, San Jose, California, July 12-16, 1992. American
    Association for Artificial Intelligence. AAAI/MIT Press,
    Cambridge, Massachusetts.

[Baum-92] Eric B. Baum. On optimal game tree propagation for imperfect
    players. In [AAAI-92], pages 507-512.
    
[IJCAI-81] Artificial Intelligence: Proceedings of the 7th
    International Joint Conference, Vancouver, B.C., Canada, August
    24-28, 1981. Morgan Kaufmann, Inc., Los Altos, California.

[Pearl-81] Judea Pearl. Heuristic search theory: Survey of recent
    results. In [IJCAI-81], pages 554-562.

   , ,                       __               [email-masked]
  /|/| .  _ |_   _   _  |   |_  _  _  ,_  |,  Clinical Decision-Making Group
 / | | | (_ | | (_| (-' |   |  |  (_| | | |\  MIT Lab for Computer Science
545 Technology Sq. rm. 421, Cambridge, MA 02139. (617) 253-2351 (fax 3-5060)
<a href="http://medg.lcs.mit.edu/mpf/mpf.html">Personal Web Page</a>

----------------------------------------------------------------------
		  Advances in Decision-Theoretic AI:
	       Limited Rationality and Abstract Search

			   by Michael Frank

This thesis reports on the current state of ongoing research by the
author and others investigating the use of decision theory---its
principles, methods, and philosophy---in Artificial Intelligence. Most
of the research discussed in this thesis concerns decision problems
arising within the context of Computer Game-Playing domains, although
we discuss applications in other areas such as Clinical
Decision-Making and Planning as well. We discuss in detail several AI
techniques that use decision-theoretic methods, offer some initial
experimental tests of their underlying hypotheses, and present work on
some new decision-theoretic techniques under development by the
author.

We also discuss some attempts, by the author and others, to transcend
a few of the limitations of the earlier approaches, and of traditional
decision theory, and synthesize broader, more powerful theories of
abstract reasoning and limited rationality that can be applied to
solve decision problems more effectively. These attempts have not yet
achieved conclusive success. We discuss the most promising of the
current avenues of research, and propose some possible new directions
for future work.

			Citation information:

Michael P. Frank.  Advances in decision-theoretic AI: Limited
rationality and abstract search.  Master's thesis, Massachusetts
Institute of Technology, Cambridge, Massachusetts, May 1994.
Available on the World-Wide Web at URL
http://medg.lcs.mit.edu/mpf/papers/Frank-94/Frank-94.html.

@MASTERSTHESIS{Frank-94,
	AUTHOR = {Michael P. Frank},
	TITLE = {Advances in Decision-Theoretic {AI}: Limited Rationality
		 and Abstract Search},
	SCHOOL = {Massachusetts Institute of Technology},
	YEAR = 1994,
	ADDRESS = {Cambridge, Massachusetts},
	MONTH = may,
	note = {Available on the World-Wide Web at URL {\tt
		http://medg.lcs.mit.edu/mpf/papers/Frank-94/Frank-94.html}},
	url = {http://medg.lcs.mit.edu/mpf/papers/Frank-94/Frank-94.html},
}