By Janet Kolodner (Auth.)
Case-based reasoning is without doubt one of the quickest transforming into components within the box of knowledge-based structures and this ebook, authored by way of a pace-setter within the box, is the 1st accomplished textual content at the topic. Case-based reasoning platforms are structures that shop information regarding events of their reminiscence. As new difficulties come up, related events are searched out to aid remedy those difficulties. difficulties are understood and inferences are made by means of discovering the nearest situations in reminiscence, evaluating and contrasting the matter with these circumstances, making inferences in keeping with these comparisons, and asking questions whilst inferences cannot be made.
This publication provides the cutting-edge in case-based reasoning. the writer synthesizes and analyzes a vast variety of ways, with precise emphasis on utilising case-based reasoning to complicated real-world problem-solving projects corresponding to clinical analysis, layout, clash answer, and making plans. The author's method combines cognitive technology and engineering, and is predicated on research of either professional and commonsense initiatives. directions for construction case-based specialist structures are supplied, comparable to the best way to signify wisdom in situations, the way to index instances for accessibility, tips on how to enforce retrieval tactics for potency, and the way to conform outdated ideas to slot new events.
This e-book is a wonderful textual content for classes and tutorials on case-based reasoning. it's also an invaluable source for machine pros and cognitive scientists drawn to studying extra approximately this fast-growing field.
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Extra resources for Case-Based Reasoning
It updates its understanding of the world so that it will be able to anticipate and not repeat the mistake it has just made. To do that, it figures out what the predictors of this type of failure are and uses those as indexes to a warning that a failure might happen. 1 CHEF's Functional Architecture derived explanation of this failure, extracting from it those parts that describe environmental features responsible for the failure. It uses this index to index both the case with the failed plan, which acts as a warning case, and the case with the repaired plan, which provides it a way to plan around that particular problem.
The requirements of the new problem are imposed on top of the framework that has been established, and their implications are propagated throughout the framework. In some places there are conflicts. JULIA adapts the framework to fit the new situation in those places. In other places, nothing specific is filled in, but the combination of constraints imposed by the prototype and constraints imposed by the new situation provide guidelines for filling it in. JULIA uses case-based reasoning to fill those in.
A casebased reasoner learns as a byproduct of its reasoning activity. It becomes more efficient and more competent as a result of storing its experiences and referring to them in later reasoning. In a departure from traditional methods of reasoning investigated by AI researchers and psychologists, case-based reasoning views reasoning as a process of remembering one or a small set of concrete instances or cases and basing decisions on comparisons between the new situation and the old one. Decomposition and recomposition are, as a result, deemphasized, as is the use of general knowledge.