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Showing papers by "Thomas G. Dietterich published in 1984"


Book ChapterDOI
01 Jan 1984
TL;DR: This article analyzes the three tasks of the Critic as threefold: evaluation of the past actions of the performance element of the learning system, localization of credit and blame to particular portions of that performance element, and recommendation of possible improvements and modifications in the performanceelement.
Abstract: Buchanan, Mitchell, Smith, and Johnson (1978) described a general model of learning systems that included a component called the Critic. The task of the Critic was described as threefold: evaluation of the past actions of the performance element of the learning system, localization of credit and blame to particular portions of that performance element, and recommendation of possible improvements and modifications in the performance element. This article analyzes these three tasks in detail and surveys the methods that have been employed in existing learning systems to accomplish them. The principle method used to evaluate the performance element is to develop a global performance standard by (a) consulting an external source of knowledge, (b) consulting an internal source of knowledge, or (c) conducting deep search. Credit and blame have been localized by (a) asking an external knowledge source to do the localization, (b) factoring the global performance standard to produce a local performance standard, and (c) conducting controlled experiments on the performance element. Recommendations have been conmiunicated to the learning element using (a) local training instances, (b) correlation coefficients, and (c) partially-instantiated schemata.

19 citations


Proceedings Article
06 Aug 1984
TL;DR: This paper formalizes this learning problem and presents a method called the iterative extension method for solving it, which is being implemented and applied to the problem of learning UNIX file system commands by observing a tutorial interaction with UNIX.
Abstract: It is difficult to learn about systems that contain state variables when those variables are not directly observable. This paper formalizes this learning problem and presents a method called the iterative extension method for solving it. In the iterative extension method, the learner gradually constructs a partial theory of the state-containing system. At each stage, the learner applies this partial theory to interpret the I/O behavior of the system and obtain additional constraints on the structure and values of its state variables. These constraints can be applied to extend the partial theory by hypothesizing additional internal state variables. The improved theory can then be applied to interpret more complex I/O behavior. This process continues until a theory of the entire system is obtained. Several sufficient conditions for the success of this method are presented including (a) the observability and decomposability of the state information in the system, (b) the learnability of individual state transitions in the system, (c) the ability of the learner to perform synthesis of straight-line programs and conjunctive predicates from examples and (d) the ability of the learner to perform theory-driven data interpretation. The method is being implemented and applied to the problem of learning UNIX file system commands by observing a tutorial interaction with UNIX.

7 citations