88 research outputs found
Rational Value of Information Estimation for Measurement Selection
Computing value of information (VOI) is a crucial task in various aspects of
decision-making under uncertainty, such as in meta-reasoning for search; in
selecting measurements to make, prior to choosing a course of action; and in
managing the exploration vs. exploitation tradeoff. Since such applications
typically require numerous VOI computations during a single run, it is
essential that VOI be computed efficiently. We examine the issue of anytime
estimation of VOI, as frequently it suffices to get a crude estimate of the
VOI, thus saving considerable computational resources. As a case study, we
examine VOI estimation in the measurement selection problem. Empirical
evaluation of the proposed scheme in this domain shows that computational
resources can indeed be significantly reduced, at little cost in expected
rewards achieved in the overall decision problem.Comment: 7 pages, 2 figures, presented at URPDM2010; plots fixe
Rational Deployment of CSP Heuristics
Heuristics are crucial tools in decreasing search effort in varied fields of
AI. In order to be effective, a heuristic must be efficient to compute, as well
as provide useful information to the search algorithm. However, some well-known
heuristics which do well in reducing backtracking are so heavy that the gain of
deploying them in a search algorithm might be outweighed by their overhead.
We propose a rational metareasoning approach to decide when to deploy
heuristics, using CSP backtracking search as a case study. In particular, a
value of information approach is taken to adaptive deployment of solution-count
estimation heuristics for value ordering. Empirical results show that indeed
the proposed mechanism successfully balances the tradeoff between decreasing
backtracking and heuristic computational overhead, resulting in a significant
overall search time reduction.Comment: 7 pages, 2 figures, to appear in IJCAI-2011, http://www.ijcai.org
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