This paper appears in:
IBM Journal of Research and Development
Date of Publication:
May-June 2012
Author(s):
Tesauro, G.
IBM Research Division, Thomas J. Watson Research Center, Yorktown Heights, NY, USA
Gondek, D. C.
;
Lenchner, J.
;
Fan, J.
;
Prager, J. M.
Volume: 56,
Issue: 3.4
On Page(s):
16:1
-
16:11
Product Type:
Journals & Magazines
Abstract
The game of Jeopardy!™ features four types of strategic decision-making: 1) Daily Double wagering; 2) Final Jeopardy! wagering; 3) selecting the next square when in control of the board; and 4) deciding whether to attempt to answer, i.e., buzz in. Strategies that properly account for the game state and future event probabilities can yield a huge boost in overall winning chances, when compared with simple rule-of-thumb strategies. In this paper, we present an approach to developing and testing components to make said strategy decisions, founded upon development of reasonably faithful simulation models of the players and the Jeopardy! game environment. We describe machine learning and Monte Carlo methods used in simulations to optimize the respective strategy algorithms. Application of these methods yielded superhuman game strategies for IBM Watson™ that significantly enhanced its overall competitive record.
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ISSN : 0018-8646
Digital Object Identifier : 10.1147/JRD.2012.2188931
Date of Current Version :
Tue Apr 03 00:00:00 EDT 2012
Issue Date :
May-June 2012
Sponsored by :
IBM
Available to subscribers and IEEE members.
Available to subscribers and IEEE members.