scholarly journals Dynamic Decision Making and Race Games

2013 ◽  
Vol 2013 ◽  
pp. 1-15
Author(s):  
Shipra De ◽  
Darryl A. Seale

Frequent criticism of dynamic decision making research pertains to the overly complex nature of the decision tasks used in experimentation. To address such concerns, we study dynamic decision making with respect to a simple race game, which has a computable optimal strategy. In this two-player race game, individuals compete to be the first to reach a designated threshold of points. Players alternate rolling a desired quantity of dice. If the number one appears on any of the dice, the player receives no points for his turn; otherwise, the sum of the numbers appearing on the dice is added to the player's score. Results indicate that although players are influenced by the game state when making their decisions, they tend to play too conservatively in comparison to the optimal policy and are influenced by the behavior of their opponents. Improvement in performance was negligible with repeated play. Survey data suggests that this outcome could be due to inadequate time for learning or insufficient player motivation. However, some players approached optimal heuristic strategies, which perform remarkably well.

2013 ◽  
Vol 47 ◽  
pp. 205-251 ◽  
Author(s):  
G. Tesauro ◽  
D. C. Gondek ◽  
J. Lenchner ◽  
J. Fan ◽  
J. M. Prager

Major advances in Question Answering technology were needed for IBM Watson to play Jeopardy! at championship level -- the show requires rapid-fire answers to challenging natural language questions, broad general knowledge, high precision, and accurate confidence estimates. In addition, Jeopardy! features four types of decision making carrying great strategic importance: (1) Daily Double wagering; (2) Final Jeopardy wagering; (3) selecting the next square when in control of the board; (4) deciding whether to attempt to answer, i.e., "buzz in." Using sophisticated strategies for these decisions, that properly account for the game state and future event probabilities, can significantly boost a player's overall chances to win, when compared with simple "rule of thumb" strategies. This article presents our approach to developing Watson's game-playing strategies, comprising development of a faithful simulation model, and then using learning and Monte-Carlo methods within the simulator to optimize Watson's strategic decision-making. After giving a detailed description of each of our game-strategy algorithms, we then focus in particular on validating the accuracy of the simulator's predictions, and documenting performance improvements using our methods. Quantitative performance benefits are shown with respect to both simple heuristic strategies, and actual human contestant performance in historical episodes. We further extend our analysis of human play to derive a number of valuable and counterintuitive examples illustrating how human contestants may improve their performance on the show.


2009 ◽  
Author(s):  
C. Dominik Guss ◽  
Jarrett Evans ◽  
Devon Murray ◽  
Harald Schaub

2009 ◽  
Author(s):  
Justin Weinhardt ◽  
Jeff Vancouver ◽  
Claudia Gonzalez Vallejo ◽  
Jason Harman

1991 ◽  
Author(s):  
Alexander J. Wearing ◽  
Chris Pivec ◽  
Mary M. Omodei

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