Any learning process can be viewed as a self-modification of the leaxnefs current knowledge tArough an. interaction with some information source. Such knowledge modification is guided by the learner's deshe to achieve a certain outcome, and can engage any kind of inference. The type of inference involved depends on he input information, the current (background) knowledge and the learneFs task ax hand. Based on such a view of learning, several fundamental concepts are analized and clarified, in paxticular, analytic and synthetic learning, derivm:ional and hypothetical explanation, constnictive induction, abduction, abstraction and deductive generalization. It is shown that inductive generalization and abduction can be viewed as two basic forms of general induction, and that abstraction and deductive generalization axe two related forms of constructive deduction. Using this conceptual framework, a methodology for multistrategy task-adaptive learning (MTL) is outlined, in which learning strategies axe combined dynamically, depending on the current learning situation. Speccally, an MTL learner anaLizes a "wiad" relationship among the input information, the background knowledge and the learning task, and on that basis determines which strategy, or. a combination thereof, is most appropriate at a given learning step. To implement the MTL methodology, a new knowledge representation is proposed, based on the parametric association rules (PARs). Basic ideas of MTL are illustrated by means of the well-known "cup" example, through which is shown how an MTL learner can employ, depending the above mad relationship, emprical learning, constructive inductive generalization, abduction, explanation-based learning and absuaction.

Abstract—Abductive inference in Bayesian belief networks (BBNs) is intended as the process of generating the most probable configurations given observed evidence. When we are interested only in a subset of the network’s variables, this problem is called partial abductive inference. Both problems are NP-hard and so exact computation is not always possible. In this paper, a genetic algorithm is used to perform partial abductive inference in BBNs. The main contribution is the introduction of new genetic operators designed specifically for this problem. By using these genetic operators, we try to take advantage of the calculations previously carried out, when a new individual is evaluated. The algorithm is tested using a widely used Bayesian network and a randomly generated one and then compared with a previous genetic algorithm based on classical genetic operators. From the experimental results, we conclude that the new genetic operators preserve the accuracy of the previous algorithm, and also reduce the number of operations performed during the evaluation of individuals. The performance of the genetic algorithm is, thus, improved. Index Terms—Abductive inference, bayesian belief networks, evolutionary computation, genetic operators, most probable explanation, probabilistic reasoning. I.

In previous work we described a knowledge-intensive inductive learning algorithm called abductive explanation-based learning (A-EBL) that uses background knowledge to improve the performance of a concept learner. A disadvantage of A-EBL is that it is not incremental. This paper describes an alternative learning algorithm called IA-EBL that learns incrementally; IA-EBL replaces the set-cover based learning algorithm of A-EBL with an extension of a perceptron learning algorithm. However, IA-EBL is in most other respects comparable to A-EBL, except that the output of the learning system can no longer be easily expressed as a logical theory. In this paper, IA-EBL is described, analyzed according to Littlestone's model of mistake-bounded learnability, and finally compared experimentally to A-EBL. IA-EBL is shown to provide order-ofmagnitude speedups over A-EBL in two domains when used in an incremental setting.

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Any learning process can be viewed as a self-modification of the leamer's current knowledge through an interaction with some information source. Such knowledge modification s graded by the learner s destre to achieve a certain outcome, and can engage any kind of inference. The typ0 of inference involved depends on the input information, the current (background) knowledge and the learne's task,.at h, and: Based on such a view of learning, several fundamental concepts are ananzeu ano clarified, in particular, analytic and synthetic learning, derivational and hypothetical explanation, constructive induction, abduction, abstraction and deductive generalization. It is shown that inductive generalization and abduction can be viewed as two basic forms of general induction, and that abstraction and deductive generalization are two related forms of constructive deduction. Using this conceptual framework, a methodology for multistrategy task-adaptive learning (MTL) is outlined, in which learning strategies are combined dynamically, depending on the current learning situation. Specifically, an MTL learner anali?es a "triad" relationship among the input information, the background knowledge and the learning task, and on that basis determines which strategy, or a combination thereof, is most appropriate at a given learning step. To implement the MTL methodology, a new knowledge representation is proposed, based on the parametric association rules (PARs). Basic ideas of MTL are illustrated by means of the well-known "cup" example, through which is shown how an MTL leamer can employ, depending on the above triad relationship, emprical learning, constructive inductive generalization, abduction, explanation-based learning and abstraction.