Refine
Has Fulltext
- yes (26)
Keywords
- Abstraction (5)
- case-based problem solving (5)
- Case Based Reasoning (4)
- Knowledge Acquisition (4)
- Case-Based Reasoning (3)
- explanation-based learning (3)
- learning system (2)
- Adaption (1)
- COMOKIT (1)
- Case-based problem solving (1)
Faculty / Organisational entity
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.
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.
Object-oriented case representations require approaches for similarity assessment that allow to compare two differently structured objects, in particular, objects belonging to different object classes. Currently, such similarity measures are developed more or less in an ad-hoc fashion. It is mostly unclear, how the structure of an object-oriented case model, e.g., the class hierarchy, influences similarity assessment. Intuitively, it is obvious that the class hierarchy contains knowledge about the similarity of the objects. However, how this knowledge relates to the knowledge that could be represented in similarity measures is not obvious at all. This paper analyzes several situations in which class hierarchies are used in different ways for case modeling and proposes a systematic way of specifying similarity measures for comparing arbitrary objects from the hierarchy. The proposed similarity measures have a clear semantics and are computationally inexpensive to compute at run-time.
Abstraction is one of the most promising approaches to improve the performance of problem solvers. Abstraction by dropping sentences of a domain description - as used in most hierarchical planners - is known to be very representation dependent. To overcome these drawbacks, we propose a more general view of abstraction involving the change of representation language. We have developed a new abstraction methodology and a related sound and complete learning algorithm that allows the complete change of representation language of planning cases from concrete to abstract.
Paris (Plan Abstraction and Refinement in an Integrated System) [4, 2] is a domain independent case-based planning system which allows the flexible reuse of planning cases by abstraction and refinement. This approach is mainly inspired by the observation that reuse of plans must not be restricted to a single description level. In domains with a high variation in the problems, the reuse of past solutions must be achieved at various levels of abstraction.
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 addresses the role of abstraction in case-based reasoning. We develop a general framework for reusing cases at several levels of abstraction, which is particularly suited for describing and analyzing existing and designing new approaches of this kind. We show that in synthetic tasks (e.g. configuration, design, and planning), abstraction can be successfully used to improve the efficiency of similarity assessment, retrieval, and adaptation. Furthermore, a case-based planning system, called Paris, is described and analyzed in detail using this framework. An empirical study done with Paris demonstrates significant advantages concerning retrieval and adaptation efficiency as well as flexibility of adaptation. Finally, we show how other approaches from the literature can be classified according to the developed framework.
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.
As the previous chapters of this book have shown, case-based reasoning is a technology that has been successfully applied to a large range of different tasks. Through all the different CBR projects, both basic research projects as well as industrial development projects, lots of knowledge and experience about how to build a CBR application has been collected. Today, there is already an increasing number of successful companies developing industrial CBR applications. In former days, these companies could develop their early pioneering CBR applications in an ad-hoc manner. The highly-skilled CBR expert of the company was able to manage these projects and to provide the developers with the required expertise.
Lernen von Abstraktionshierarchien zur Optimierung der Auswahl von maschinell abstrahierten Plänen
(1994)
Mit Hilfe von "Multistrategy" Ansätzen, die erklärungsbasiertes und induktives Lernen integrieren, ist es möglich, die Performanz von Planungssystemen signifikant zu verbessern. Dabei können gelöste Planungsprobleme zunächst mit einem wissensintensiven Verfahren abstrahiert und generalisiert werden. Durch den in diesem Beitrag im Vordergrund stehenden induktiven inkrementellen Lernalgorithmus ist es dann weiterhin möglich, die Gesamtheit des deduktiv generierten Wissens in einer Abstraktionshierarchie anzuordnen. Dabei wird die, im allgemeinen unentscheidbare, "spezieller-als-Relation" zwischen generalisierten Plänen, induktiv aus den gegebenen Planungsfällen gelernt. Diese Abstraktionshierarchie dient dann zur Klassifikation neuer Problemstellungen und damit zur Bestimmung einer speziellsten anwendbaren abstrakten Problemlösung.
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.
In this paper, we propose the PARIS approach for improving complex problem solving by learning from previous cases. In this approach, abstract planning cases are learned from given concrete cases. For this purpose, we have developed a new abstraction methodology that allows to completely change the representation language of a planning case, when the concrete and abstract languages are given by the user. Furthermore, we present a learning algorithm which is correct and complete with respect to the introduced model. An empirical study in the domain of process planning in mechanical engineering shows significant improvements in planning efficiency through learning abstract cases while an explanation-based learning method only causes a very slight improvement.
Although skeletal plan refinement is used in several planning systems, a procedure for the automatic acquisition of such high-level plans has not yet been developed. The proposed explanation- based knowledge acquisition procedure constructs a skeletal plan automatically from a sophisticated concrete planning case. The classification of that case into a well-described class of problems serves as an instrument for adjusting the applicability of the acquired skeletal plans to that class. The four phases of the proposed procedure are constituted as follows: In the first phase, the execution of the source plan is simulated, and explanations for the effects of the occurred operators are constructed. In the second phase, the generalization of these explanations is performed with respect to a criterion of operationality which specifies the vocabulary for defining abstract operators for the skeletal plan. The third phase, a dependency analysis of the resulting operator effects, unveils the interactions of the concrete plan which are substantial for the specified class. In the forth phase, the concept descriptions for the abstract operators of the skeletal plan are formed by collecting and normalizing the important constraints for each operation that were indicated by the dependencies. With this procedure sophisticated planning solutions from human experts can be generalized into skeletal plans and consequently be reused by a planning system in novel situations.
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.
When problems are solved through reasoning from cases, the primary kind of knowledge is contained in the specific cases which are stored in the case base. However, in many situations additional background-knowledge is required to cope with the requirements of an application. We describe an approach to integrate such general knowledge into the reasoning process in a way that it complements the knowledge contained in the cases. This general knowledge itself is not sufficient to perform any kind of model-based problem solving, but it is required to interpret the available cases appropriately. Background knowledge is expressed by two different kinds of rules that both must be formalized by the knowledge engineer: Completion rules describe how to infer additional features out of known features of an old case or the current query case. Adaptation rules describe how an old case can be adapted to fit the current query. This paper shows how these kinds of rules can be integrated into an object-oriented case representation.
Complex problem solving can be substantially improved by the reuse of experience from previously solved problems. This requires that case libraries of successful problem solutions are transformed into problem solving knowledge with high utility, i.e. knowledge which causes high savings in search time, high application probability and low matching costs in a respective performance component. 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. Abstractions which are valid for a class of planning cases rather than for a single case, ensure a successful application in a larger spectrum of new situations. The hierarchical organization of the learned shared abstractions causes low matching costs. The presented S-PABS procedure is an EBL-procedure in which abstraction, learning from multiple examples and hierarchical clustering are combined to automatically construct a hierarchy of shared abstract plans by analyzing concrete planning cases. A specific planning procedure has been designed to solve new planning problems guided by the knowledge learned by S-PABS. By allowing a feedback from this planning procedure to the learning component, the integrated system shows an increase in performance through past problem solving.
Bei der Erstellung komplexer Software spielt die Wiederverwendung vorhandener Programmbestandteile eine besonders grosse Rolle, da hierdurch sowohl die Software-Qualität gesteigert, als auch der gesamte Erstellungsund Wartungsaufwand erheblich reduziert werden kann. In jüngster Zeit gewinnen objektorientierte Programmiersprachen zunehmend an Bedeutung, da die Wiederverwendung hierbei bereits durch Sprachkonzepte wie z.B. Vererbung und Polymorphie unterstützt wird. Weiterhin besteht jedoch das Problem, zur Wiederverwendung geeignete Programmbestandteile aufzufinden. Ziel dieser Arbeit ist es herauszufinden, inwieweit fallbasiertes Schliessen nach dem aktuellen Stand der Kunst die Wiederverwendung objektorientierter Software unt erstützen kann. Hierzu wurde eine entsprechende Anwendung prototypisch auf der Basis des INRECA-Systems entwickelt. Durch ausgewählte Testsituationen wurden Erfahrungen mit diesem Prototyp gesammelt und systematisch ausgewertet.
In diesem Artikel diskutieren wir Anforderungen aus der Kreditwürdigkeitsprüfung und ihre Erfüllung mit Hilfe der Technik des fallbasierten Schliessens. Innerhalb eines allgemeinen Ansatzes zur fallbasierten Systementwicklung wird ein Lernverfahren zur Optimierung von Entscheidungskosten ausführlich beschrieben. Dieses Verfahren wird, auf der Basis realer Kundendaten, mit dem fallbasierten Entwicklungswerkzeug INRECA empirisch bewertet. Die Voraussetzungen für den Einsatz fallbasierter Systeme zur Kreditwürdigkeitsprüfung werden abschliessend dargestellt und ihre Nüt zlichkeit diskutiert.
Case-based problem solving can be significantly improved by applying domain knowledge (in opposition to problem solving knowledge), which can be acquired with reasonable effort, to derive explanations of the correctness of a case. Such explanations, constructed on several levels of abstraction, can be employed as the basis for similarity assessment as well as for adaptation by solution refinement. The general approach for explanation-based similarity can be applied to different real world problem solving tasks such as diagnosis and planning in technical areas. This paper presents the general idea as well as the two specific, completely implemented realizations for a diagnosis and a planning task.
Case-based problem solving can be significantly improved by applying domain knowledge (in opposition to problem solving knowledge), which can be acquired with reasonable effort, to derive explanations of the correctness of a case. Such explanations, constructed on several levels of abstraction, can be employed as the basis for similarity assessment as well as for adaptation by solution refinement. The general approach for explanation-based similarity can be applied to different real world problem solving tasks such as diagnosis and planning in technical areas. This paper presents the general idea as well as the two specific, completely implemented realizations for a diagnosis and a planning task.