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Hierachical planning can be improved by explanation-based learning (EBL) of abstract plans from detailed, successfully solved planning problems. Abstract plans, expressed in well-established terms of the domain, serve as useful problem decompositions which can drastically reduce the planning complexity. The learned plan abstraction must be valid for a class of planning cases rather than for a single case, to ensure their successful application in a larger spectrum of new situations. A hierarchical organization of the newly learned knowledge must be archieved to overcome the utility problem in EBL. This paper presents a new formal model of shared plan abstraction and the closely related explanation-based procedure S-PABS. Unlike other apporaches to plan abstraction, our model allows a total different terminology to be introduced at the abstract level. Finally, an unsupervised incremental procedure for constructing a hierachy of shared abstract plans is proposed, as a kind of concept formation over explanations.
This paper presents a brief overview of the INRECA-II methodology for building and maintaining CBR applications. It is based on the experience factory and the software process modeling approach from software engineering. CBR development and maintenance experience is documented using software process models and stored in a three-layered experience packet.
Although several systematic analyses of existing approaches to adaptation have been published recently, a general formal adaptation framework is still missing. This paper presents a step into the direction of developing such a formal model of transformational adaptation. The model is based on the notion of the quality of a solution to a problem, while quality is meant in a more general sense and can also denote some kind of appropriateness, utility, or degree of correctness. Adaptation knowledge is then defined in terms of functions transforming one case into a successor case. The notion of quality provides us with a semantics for adaptation knowledge and allows us to define terms like soundness, correctness and completeness. In this view, adaptation (and even the whole CBR process) appears to be a special instance of an optimization problem.
For defining attribute types to be used in the case representation, taxonomies occur quite often. The symbolic values at any node of the taxonomy tree are used as attribute values in a case or a query. A taxonomy type represents a relationship between the symbols through their position within the taxonomy-tree which expresses knowledge about the similarity between the symbols. This paper analyzes several situations in which taxonomies are used in different ways and proposes a systematic way of specifying local similarity measures for taxonomy types. The proposed similarity measures have a clear semantics and are easy to compute at runtime.
This paper motivates the necessity for support for negotiation during Sales Support on the Internet within Case-Based Reasoning solutions. Different negotiation approaches are discussed and a general model of the sales process is presented. Further, the tradition al CBR-cycle is modified in such a way that iterative retrieval during a CBR consulting session is covered by the new model. Several gen eral characteristics of negotiation are described and a case study is shown where preliminary approaches are used to negotiate with a cu stomer about his demands and available products in a 'CBR-based' Electronic Commerce solution.
Der Trend zu einer immer stärkeren Kopplung von Systemen bei gleichzeitiger Dezentralisierung durch Vernetzung hat dazu geführt, daß Computernutzern auf Wunsch enorme Datenmengen zur Verfügung stehen, die sich einer sinnvollen Bearbeitung durch den Nutzer allein völlig entziehen. Unterschiedliche Repräsentationsformalismen für Informationen, Mehrdeutigkeiten, Redundanz sowie eingeschränkte Verfügbarkeit sowohl von Informationen als auch von Rechenleistung machen konventionelle Suchverfahren unanwendbar. Stattdessen werden Suchverfahren und Programme benötigt, die sich intelligent an unterschiedliche Formalismen anpassen, ihre Handlungen ständig evaluieren und fähig sind, ihre Benutzer individuell zu unterstützen. Schlagwörter wie Knowbots, Search-Engines oder Data-Miningsind deshalb zur Zeit in aller Munde. Ein umfassendes Buch, das die hinter diesen und ähnlichen Schlagwörtern verborgenen Ideen und Konzepte präsentiert, existiert jedoch zur Zeit noch nicht. Dies war für uns die Motivation, das Thema "Intelligente Suche im Internet mit Lernenden Systemen" in einem Seminar zu behandeln. Wir haben damit ein Forschungsgebiet aufgegriffen, das sowohl für alle am LSA beteiligten Gruppen von Interesse ist, aber darüber hinaus aktuell von vielen Seiten aufmerksam beobachtet wird. Daher haben wir uns entschlossen, die Ausarbeitungen, die im Rahmen dieses Seminars von den TeilmehmerInnen erstellt wurden, durch den vorliegenden Bericht einer breiteren Öffentlichkeit zugänglich zu machen.