Eric Wefald

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Eric Huang Wefald, († August 31, 1989) [1]
was an American computer scientiest and AI-researcher from University of California, Berkeley, California, who co-authored with Stuart Russell on multiple papers on search control, machine learning, and metareasoning, that is the process of reasoning about reasoning itself. Eric Wefald obtained a M.A. in philosophy from Princeton in 1985, and was completing a doctorate in artificial intelligence at U.C. Berkeley, when he died in a car accident near Bordeaux, France, on August 31, 1989.

Decision-Theoretic Search Control

Abstract from Stuart Russell and Eric Wefald (1988). Decision-Theoretic Search Control: General Theory and an Application to Game-Playing. [2]:
In this paper we outline a general approach to the study of problem-solving, in which search steps are considered decisions in the same sense as actions in the world. Unlike other metrics in the literature, the value of a search step is defined as a real utility rather than as a quasi-utility, and can therefore be computed directly from a model of the base-level problem-solver. We develop a formula for the value of a search step in a game-playing context using the single-step assumption, namely that a computation step can be evaluated as it was the last to be taken. We prove some meta-level theorems that enable the development of a low- overhead algorithm, MGSS, that chooses search steps in order of highest estimated utility. Although we show that the single-step assumption is untenable in general, a program implemented for the game of Othello appears to rival an alpha-beta search with equal node allocations or time allocations. Pruning and search termination subsumes or improve on many other algorithms. Single-agent search, as in the A algorithm, yields a simpler analysis, and we are currently investigating applications of the algorithm developed for this case.

Optimal Game-Tree Search

Abstract from Stuart Russell and Eric Wefald (1989). On optimal game-tree search using rational metareasoning [3]:
In this paper we outline a general approach to the study of problem-solving, in which search steps are considered decisions in the same sense as actions in the world. Unlike other metrics in the literature, the value of a search step is defined as a real utility rather than as a quasi-utility, and can therefore be computed directly from a model of the base-level problem-solver. We develop a formula for the expected value of a search step in a game-playing context using the single-step assumption, namely that a computation step can be evaluated as it was the last to be taken. We prove some meta-level theorems that enable the development of a low-overhead algorithm, MGSS*, that chooses search steps in order of highest estimated utility. Although we show that the single-step assumption is untenable in general, a program implemented for the game of Othello soundly beats an alpha-beta search while expanding significantly fewer nodes, even though both programs use the same evaluation function.

Selected Publications

[4]

References

  1. ^ Princeton Alumni Weekly: Eric Huang Wefald, Published in Feb. 6, 1991
  2. ^ Stuart Russell and Eric Wefald (1988). Decision-Theoretic Search Control: General Theory and an Application to Game-Playing. Computer Science Division Technical Report 88/435, University of California, Berkeley, CA.
  3. ^ Stuart Russell and Eric Wefald (1989). On optimal game-tree search using rational metareasoning. In Proceedings of the Eleventh International Joint Conference on Artificial Intelligence, Detroit, MI: Morgan Kaufmann, pdf
  4. ^ DBLP: Eric Wefald

What links here?

Page Date Edited
Eric Wefald Jun 23, 2011
Learning Feb 20, 2018
Othello Jan 4, 2018
People Feb 28, 2018
Search Feb 1, 2018
Stuart Russell Mar 16, 2015
University of California, Berkeley Sep 5, 2016

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