Analysis of Watson's Strategies for Playing Jeopardy!

@article{Tesauro2013AnalysisOW,
  title={Analysis of Watson's Strategies for Playing Jeopardy!},
  author={Gerald Tesauro and David Gondek and Jonathan Lenchner and James Fan and John M. Prager},
  journal={J. Artif. Intell. Res.},
  year={2013},
  volume={47},
  pages={205-251}
}
Major advances in Question Answering technology were needed for IBM Watson1 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… 

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References

SHOWING 1-10 OF 29 REFERENCES

Simulation, learning, and optimization techniques in Watson's game strategies

Application of machine learning and Monte Carlo methods used in simulations to optimize the respective strategy algorithms yielded superhuman game strategies for IBM Watsoni that significantly enhanced its overall competitive record.

Introduction to "This is Watson"

A brief history of the events and ideas that positioned the team to take on the Jeopardy! challenge, build Watson, IBM Watson™, and ultimately triumph is provided, and how the system performed at champion levels is summarized.

Special Questions and techniques

The design of the Special Question solving procedures motivated architectural design decisions that are applicable to general open-domain question-answering systems and are an important class of question to address in the Jeopardy! context.

Building Watson: An Overview of the DeepQA Project

The results strongly suggest that DeepQA is an effective and extensible architecture that may be used as a foundation for combining, deploying, evaluating and advancing a wide range of algorithmic techniques to rapidly advance the field of QA.

On-line Policy Improvement using Monte-Carlo Search

A Monte-Carlo simulation algorithm for real-time policy improvement of an adaptive controller and results are reported for a wide variety of initial policies, ranging from a random policy to TD-Gammon, an extremely strong multi-layer neural network.

Reinforcement Learning: An Introduction

This book provides a clear and simple account of the key ideas and algorithms of reinforcement learning, which ranges from the history of the field's intellectual foundations to the most recent developments and applications.

GIB: Steps Toward an Expert-Level Bridge-Playing Program

GIB, the first bridge-playing program to approach the level of a human expert, is described and the results of experiments comparing GIB to both human opponents and other programs are presented.

World-championship-caliber Scrabble

Game theory.

  • M. Dufwenberg
  • Psychology
    Wiley interdisciplinary reviews. Cognitive science
  • 2011
The nature of game-theoretic analysis, the history of game theory,Why game theory is useful for understanding human psychology, and why game theory has played a key role in the recent explosion of interest in the field of behavioral economics are discussed.

On the generation of correlated artificial binary data

This paper presents a computationally fast method to simulate multivariate binary distributions with a given correlation structure, and main interest is in the segmentation of marketing data, where data come from customer questionnaires with "yes/no" questions.