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19
Principles of Metareasoning
 Artificial Intelligence
, 1991
"... In this paper we outline a general approach to the study of metareasoning, not in the sense of explicating the semantics of explicitly specified metalevel control policies, but in the sense of providing a basis for selecting and justifying computational actions. This research contributes to a devel ..."
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Cited by 178 (10 self)
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In this paper we outline a general approach to the study of metareasoning, not in the sense of explicating the semantics of explicitly specified metalevel control policies, but in the sense of providing a basis for selecting and justifying computational actions. This research contributes to a developing attack on the problem of resourcebounded rationality, by providing a means for analysing and generating optimal computational strategies. Because reasoning about a computation without doing it necessarily involves uncertainty as to its outcome, probability and decision theory will be our main tools. We develop a general formula for the utility of computations, this utility being derived directly from the ability of computations to affect an agent's external actions. We address some philosophical difficulties that arise in specifying this formula, given our assumption of limited rationality. We also describe a methodology for applying the theory to particular problemsolving systems, a...
Reasoning Under Varying and Uncertain Resource Constraints
, 1988
"... We describe the use of decisiontheory to optimize the value of computation under uncertain and varying resource limitations. The research is motivated by the pursuit of formal models of rational decision making for computational agents, centering on the explicit consideration of preferences and res ..."
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Cited by 139 (23 self)
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We describe the use of decisiontheory to optimize the value of computation under uncertain and varying resource limitations. The research is motivated by the pursuit of formal models of rational decision making for computational agents, centering on the explicit consideration of preferences and resource availability. We focus here on the importance of identifying the multiattribute structure of partial results generated by approximation methods for making control decisions. Work on simple algorithms and on the control of decisiontheoretic inference itself is described. 1 Computation Under Uncertainty We are investigating the decisiontheoretic control of problem solving under varying constraints in resources required for reasoning, such as time and memory. This work is motivated by the pursuit of formal models of rational decision making under resource constraints and our goal of extending foundational work on normative rationality to computational agents. We describe here a portion...
Preferential Semantics for Goals
 In Proceedings of the National Conference on Artificial Intelligence
, 1991
"... Goals, as typically conceived in AI planning, provide an insufficient basis for choice of action, and hence are deficient as the sole expression of an agent's objectives. Decisiontheoretic utilities offer a more adequate basis, yet lack many of the computational advantages of goals. We provide ..."
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Cited by 136 (24 self)
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Goals, as typically conceived in AI planning, provide an insufficient basis for choice of action, and hence are deficient as the sole expression of an agent's objectives. Decisiontheoretic utilities offer a more adequate basis, yet lack many of the computational advantages of goals. We provide a preferential semantics for goals that grounds them in decision theory and preserves the validity of some, but not all, common goal operations performed in planning. This semantic account provides a criterion for verifying the design of goalbased planning strategies, thus providing a new framework for knowledgelevel analysis of planning systems. Planning to achieve goals In the predominant AI planning paradigm, planners construct plans designed to produce states satisfying particular conditions called goals. Each goal represents a partition of possible states of the world into those satisfying and those not satisfying the goal. Though planners use goals to guide their reasoning, the crude b...
Reflection and Action Under Scarce Resources: Theoretical Principles and Empirical Study
 In Proceedings of the Eleventh International Joint Conference on Artificial Intelligence
, 1989
"... We define and exercise the expected value of computation as a fundamental component of reflection about alternative inference strategies. We present a portion of Protos research focused on the interlacing of reflection and action under scarce resources, and discuss how the techniques have been appli ..."
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Cited by 92 (10 self)
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We define and exercise the expected value of computation as a fundamental component of reflection about alternative inference strategies. We present a portion of Protos research focused on the interlacing of reflection and action under scarce resources, and discuss how the techniques have been applied in a highstakes medical domain. The work centers on endowing a computational agent with the ability to harness incomplete characterizations of problemsolving performance to control the amount of effort applied to a problem or subproblem, before taking action in the world or turning to another problem. We explore the use of the techniques in controlling decisiontheoretic inference itself, and pose the approach as a model of rationality under scarce resources. 1 Reflection and Flexibility Reflection about the course of problem solving and about the interleaving of problem solving and physical activity is a hallmark of intelligent behavior. Applying a portion of available reasoning resour...
Principles and applications of continual computation
 Artificial Intelligence J
"... Automated problem solving is viewed typically as the allocation of computational resources to solve one or more problems passed to a reasoning system. In response to each problem received, eort is applied in real time to generate a solution and problem solving ends when a solution is rendered. We ..."
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Cited by 47 (10 self)
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Automated problem solving is viewed typically as the allocation of computational resources to solve one or more problems passed to a reasoning system. In response to each problem received, eort is applied in real time to generate a solution and problem solving ends when a solution is rendered. We examine continual computation, reasoning policies that capture a broader conception of problem by considering the proactive allocation of computational resources to potential future challenges. We explore policies for allocating idle time for several settings and present applications that highlight opportunities for harnessing continual computation in realworld tasks.
A Bayesian Approach to Relevance in Game Playing
 ARTIFICIAL INTELLIGENCE
, 1997
"... The point of game tree search is to insulate oneself from errors in the evaluation function. The standard approach is to grow a full width tree as deep as time allows, and then value the tree as if the leaf evaluations were exact. This has been effective in many games because of the computational e ..."
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Cited by 42 (0 self)
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The point of game tree search is to insulate oneself from errors in the evaluation function. The standard approach is to grow a full width tree as deep as time allows, and then value the tree as if the leaf evaluations were exact. This has been effective in many games because of the computational efficiency of the alphabeta algorithm. Our approach is to form a Bayesian model of our uncertainty. We adopt an evaluation function that returns a probability distribution estimating the probability of various errors in valuing each position. These estimates are obtained by training from data. We thus use additional information at each leaf not available to the standard approach. We utilize this information in three ways: to evaluate which move is best after we are done expanding, to allocate additional thinking time to moves where additional time is most relevant to game outcome, and, perhaps most importantly, to expand the tree along the most relevant lines. Our measure of the relevan...
Corroboration, explanation, evolving probability, simplicity, and a sharpened razor
 British Journal of the Philosophy of Science
, 1968
"... My aim is to discuss (degrees of) corroboration, explanatory power, and complexity, and the relationships between them, and to suggest a new form of Ockham's razor which is applicable in particular to practical statistical problems. It will be found necessary to distinguish between two meanings ..."
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Cited by 18 (0 self)
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My aim is to discuss (degrees of) corroboration, explanatory power, and complexity, and the relationships between them, and to suggest a new form of Ockham's razor which is applicable in particular to practical statistical problems. It will be found necessary to distinguish between two meanings
Best Play for Imperfect Players and Game Tree Search; part I  theory
, 1995
"... 1 . The point of game tree search is to insulate oneself from errors in the evaluation function. The standard approach is to grow a full width tree as deep as time allows, and then value the tree as if the leaf evaluations were exact. This has been effective in many games because of the computatio ..."
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Cited by 16 (2 self)
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1 . The point of game tree search is to insulate oneself from errors in the evaluation function. The standard approach is to grow a full width tree as deep as time allows, and then value the tree as if the leaf evaluations were exact. This has been effective in many games because of the computational efficiency of the Alphabeta algorithm. But as Bayesians, we want to know the best way to use the inexact statistical information provided by the leaf evaluator to choose our next move. We add a model of uncertainty to the standard evaluation function. Within such a formal model, there is an optimal tree growth procedure and an optimal method of valuing the tree. We describe how to optimally value the tree within our model, and how to efficiently approximate the optimal tree to search. Our tree growth procedure provably approximates the contribution of each leaf to the utility in the limit where we grow a large tree, taking explicit account of the interactions between expanding different ...
Computer Chess And Search
 ARTICLE PREPARED FOR THE 2ND EDITION OF THE ENCYCLOPEDIA OF ARTIFICIAL INTELLIGENCE, S. SHAPIRO (EDITOR), TO BE PUBLISHED BY JOHN WILEY, 1992.
, 1991
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