Explanation-Based Learning (EBL) is a widely-used technique for acquiring searchcontrol knowledge. Recently, Prieditis, van Harmelen, and Bundy pointed to the similarity between Partial Evaluation (PE) and EBL. However, EBL utilizes training examples whereas PE does not. It is natural to inquire, therefore, whether PE can be used to acquire searchcontrol knowledge, and if so at what cost? This paper answers these questions by means of a case study comparing prodigy/ebl, a state-of-the-art EBL system, and static, a PEbased analyzer of problem-space definitions. When tested in prodigy/ebl's benchmark problem spaces, static generated search-control knowledge that was up to three times as effective as the knowledge learned by prodigy/ebl, and did so from twenty-six to seventyseven times faster. The paper describes static's algorithms, compares its performance to prodigy/ebl's, noting when static's superior performance will scale up and when it will not. The paper concludes with several le...

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...

The impact of Explanation-Based Learning (EBL) on problem-solving efficiency varies greatly from one problem space to another. In fact, seemingly minute modifications to problem space encoding can drastically alter EBL's impact. For example, while prodigy/ebl (a state-of-the-art EBL system) significantly speeds up the prodigy problem solver in the Blocksworld, prodigy/ebl actually slows prodigy down in a representational variant of the Blocksworld constructed by adding a single, carefully chosen, macro-operator to the Blocksworld operator set. Although EBL has been tested experimentally, no theory has been put forth that accounts for such phenomena. This paper presents such a theory. The theory exhibits a correspondence between a graph representation of problem spaces and the proofs used by EBL systems to generate search-control knowledge. The theory relies on this correspondence to account for the variations in EBL's impact. This account is validated by static, a program that extract...

Autonomous systems require the ability to plan effective courses of action under potentially uncertain or unpredictable contingencies. Planning requires knowledge of the environment that is accurate enough to allow reasoning about actions. If the environment is too complex or very dynamic, goal-driven learning with reactive feedback becomes a necessity. This chapter addresses the issue of learning by experimentation as an integral component of PRODIGY. PRODIGY is a flexible planning system that encodes its domain knowledge as declarative operators, and applies the operator refinement method to acquire additional preconditions or postconditions when observed consequences diverge from internal expectations. When multiple explanations for the observed divergence are consistent with the existing domain knowledge, experiments to discriminate among these explanations are generated. The experimentation process isolates the deficient operator and inserts the discriminant condition or unforeseen side-effect to avoid similar impasses in future planning. Thus, experimentation is demand-driven and exploits both the internal state of the planner and any external feedback received. A detailed example of integrated experiment formulation in presented as the basis for a systematic approach to extending an incomplete domain theory or correcting a potentially inaccurate one.

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.

This paper describes an explanation-based approach lo learning plans despite a computationally intractable domain theory. In this approach, the system learns an initial plan using limited inference. In order to detect plans in which the limited inference causes a faulty plan the system monitors goal achievement in plan execution. When a plan unexpectedly fails to achieve a goal (or unexpectedly achieves the goal) a refinement process is triggered in which the system constructs an explanation for the expectation violation. This explanation is then used to refine the plan. By using expectation failures to guide search the learner avoids a computationally intractable exhaustive search involved in constructing a complete proof of the plan. This approach has the theoretical property of convergence upon a sound plan.

The last few years have witnessed a remarkable expansion of research in machine learning. The field has gained an unprecedented popularity, several new areas have developed, and some previously established areas have gained new momentum. While symbolic methods, both empirical and knowledge-intensive, in particular, inductive concept learning and explanation-based methods, continued to be exceedingly active (Parts 2 and 3 of the book, respectively), sub-symbolic approaches, especially neural networks, have experienced tremendous growth (Part 5). Unlike past efforts that concentrated on single learning strategies, the new trend has been to integrate different strategies, and to develop cognitive learning architectures (Part 4). There has been an increasing interest in experimental comparisons of various methods, and in theoretical analyses of learning algorithms. Researchers have been sharing the same data sets, and have applied their techniques to the same problems in order to understand relative merits of different methods. Theoretical investigations have brought new insights into the complexity of learning processes (Part 6).

Explanation-based learning (EBL) systems have established their applicability to a wide variety of tasks. However, in despite intensive research, several problems relating to explanation-based learning have remained by and large open. This paper describes an approach to the problems of generalizing number and learning efficiently from multiple examples. The basic insight upon which the technique is based is that EBL can be thought of as learning control knowledge for a theorem-prover. By providing a richer representation for such control knowledge, more general rules can be learned: in particular, by providing looping constructs, rules which generalize number can be expressed; and by providing conditional branches, rules learned from different training examples can be combined. The technique described has been fully implemented, is domain-independent, and has been applied to a number of examples from the domain of VLSI circuit design.

This paper presents an approach to domain modeling and knowledge acquisition that consists of a gradual and goal-driven improvement of an incomplete domain model provided by a human expen. Our approach is based on a multistrategy learning method that allows a system with incomplete knowledge to learn general inference or problem solving rules from specific facts or problem solving episodes received from the human expen. The system will learn the general knowledge pieces by considering all their possible instances in the current domain model. trying to learn complete and consistent descriptions. Because of the incompleteness of the domain model the learned rules will have exceptions that are eliminated by refining the definitions of the existing concepts or by defining new concepts.

The goal of this research is to develop a systematic, integrated method of designing efficient search algorithms that solve optimization problems in real time. Search algorithms studied in this thesis comprise meta-control and primitive search. The class of optimization problems addressed are called combinatorial optimization problems, examples of which include many NP-hard scheduling and planning problems, and problems in operations research and artificial-intelligence applications. The problems we have addressed have a well-defined problem objective and a finite set of well-defined problem constraints. In this research, we use state-space trees as problem representations. The approach we have undertaken in designing efficient search algorithms is an engineering approach and consists of two phases: (a) designing generic search algorithms, and (b) improving by genetics-based machine learning methods parametric heuristics used in the search algorithms designed. Our approach is a systematic method that integrates domain knowledge, search techniques, and automated learning techniques for designing better search algorithms. Knowledge captured in designing one search algorithm can be carried over for designing new ones. iv ACKNOWLEDGEMENTS I express my sincere gratitude to all the people who have helped me in the course of my graduate study. My thesis advisor, Professor Benjamin W. Wah, was always available for discussions and encouraged me to explore new ideas. I am deeply grateful to the committee