Thedevelopmentofmultistrategylearningsystemsrequiresaclearunderstandingoftherolesandthe applicabilityconditionsofdifferentlearningstrategies.Tothisend,thischapterintroducesthe InferentialTheoryofLearning thatprovidesaconceptualframeworkforexplaininglogicalcapabilities oflearningstrategies,i.e.,their competence.Viewinglearningasaprocessofmodifyingthelearner's knowledgebyexploringthelearner'sexperience,thetheorypostulatesthatanysuchprocesscanbe describedasasearchina knowledgespace, which involvesthelearner'sexperience,piorknowledgeand the learninggoal .Thesearchoperatorsareinstantiationsof knowledgetransmutations, whichare genericpatternsofknowledgechange.Transmutationsmayemployanybasictypeofinference --- deduction,inductionoranalogy.Severalfundamentalknowledg etransmutationsaredescribedinanovel andgeneralway,suchasgeneralization,abstraction,explanationandsimilization,andtheircounterparts, specialization,concretion,predictionanddissimilization,respectively.Generalizationenlargesthe referenceset ofadescription(thesetofentitiesthatarebeingdescribed).Abstractionreducesthe amountofthedetailaboutthereferenceset.Explanationgeneratespremisesthatexplain(orimply)the givenpropertiesofthereferenceset.Similization transfersknowledgefromonereferencesettoasimilar referenceset.Usingconceptsofthetheory,a multistrategytask -adaptivelearning(MTL)methodology isoutlined,andillustratedbyanexample.MTLdynamicallyadaptsstrategiestothe learningtask , definedbytheinputinformation,learner'sbackgroundknowledge,andthelearninggoal. Thegoalof MTLresearchisto synergisticallyintegrateawiderangeofinferentiallearningstrategies,suchas empiricalgeneralization,constructiveinduction, deductivegeneralization,explanation,prediction, abstraction,andsimilization. Keywords: learningtheory,inferencetheory,multi...

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.

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Multistrategy task-adaptive learning (MTL) comprises a class of methods in which the learner determines by itself which strategy or combination of strategies is most appropriate for a given learning task defined by the learner's goal, the leamer's background knowledge (BK) and the input to the learning process. The paper presents a MTL method which is based on building a plausible justification that the learner's input is a consequence of its BK. The method assumes a general learning goal of deriving any useful knowledge from a given input and integrates dynamically a whole range of learning sategies. It also behaves as a singlestrategy method when the relationship between the input and the BK satisfies the requirements of the single-strategy method, and the general learning goal of the MTL method is specialized to the goal of the single-strategy method.

This paper presents the system WHY, which learns and updates a diagnostic knowledge base using domain knowledge and a set of examples. The a-priori knowledge consists of a causal model of the domain, stating the relationships among basic phenomena, and a body of phenomenological theory, describing the links between abstract concepts and their possible manifestations in the world. The phenomenological knowledge is used deductively, the causal model is used abductively and the examples are used inductively. The problems of imperfection and intractability of the theory are handled by allowing the system to make assumptions during its reasoning. In this way, robust knowledge can be learned with limited complexity and limited number of examples. The system works in a first order logic environment and has been applied in a real domain. 2 1. Introduction Several authors have advocated the necessity of using deep models of the structure and behaviour of the entities involved in a given doma...

The paper presents an approach to the automation of knowledge acquisition for expert systems. The approach is based on several general principles emerging from the field of machine learning: expert system building as a three phase process, understanding-based knowledge extension, knowledge acquisition through multistrategy learning, consistency-driven concept formation and refinement, closed-loop learning. and synergistic cooperation between the human expert and the learning system. In this approach, an expert system is built by a human expert and a learning system. The human expert defmes the framework for the expert system and provides an incomplete and partially incorrect knowledge base. The learning system incrementally extends, updates, and improves the knowledge base through learning from the human expert. This approach is illustrated by the learning system shell NeoDISCIPLE.

This paper outlines basic assumptions and a theoretical basis for multistrategy task.adaptive learning (MTL) methodology, which aims at ultimately integrating a spectrum of learning strategies, such as empirical teaming, constructive induction, abduction, analytic learning. learning by analogy, and reinforcement learning. In MTL, in response to an input, a learner deternines the su'ategy, or a combination of su'ategies, that is mo. st appropriate for the learning task. This detemination is based on the relationship between the input, the leamegs background knowledge and the leamer's task. By means of a simple example we show how an MTL learner can employ, depending on the above relationship, emprical learning, constructive inductive generaiiz. afion, abduction, explanation-based learning and abstraction.

Initial results toward developing a unifying conceptual framework for characterizing diverse learning strategies and paradigms are presented. We outline the inferential theory of learning that aims at understanding competence aspects of learning processes, in contrast to computational theory that is concerned with complexity aspects. The theory views learning as a goal-oriented process of creating or modifying knowledge representations. Such a process may involve any type of inference (deduction, analogy or induction) or information transmutation (e.g., reformulation, abstraction or copying). Any type of learning can therefore be characterized in terms of the types of such knowledge transformations that occur in a learning process. Several concepts fundamental to understanding learning are analyzed in a novel way and compared, such as analytic vs. synthetic learning, deduction, induction, abduction, abstraction and generalization. It is shown, for example, that inductive generalization, inductive specialization and abduction can be viewed as various forms of general induction, and that abstraction is a form of constructive deduction. Based on these concepts, a general multicriterion classification of learning processes is proposed. The presented ideas have a special significance for the development of a new generation of learning systems, called multistrategy systems, that integrate diverse learning strategies in a goal-oriented fashion. 1.

This chapter describes a general framework for multistrategy learning. One idea of this framework is to view learning as an inference process and to integrate the elementary inferences that are employed by the single-strategy learning methods. Another idea is to base learning on building and generalizing a special type of explanation structure called plausible justification tree which is composed of different types of inference and relates the learner's knowledge to the input. In this framework, learning consists of extending and/or improving the knowledge base of the system so that to explain the input received from an external source of information. The framework is illustrated with a specific method that integrates deeply and dynamically explanation-based learning, determination-based analogy, empirical induction, constructive induction, and abduction. 1

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.