Wolfgang Nejdl

Judy Kay

Pearl Pu

Eelco Herder

Adaptive Hypermedia has emerged as an important area of both academic and deployed research. It encompasses a broad range of research that will enable personalized, adaptive hypermedia systems to play an even more e?ective role in people’s lives. The Web has enabled the widespread use of many person- ized systems, such as recommenders, personalized ?lters and retrieval systems, e-learning systems and various forms of collaborative systems. Such systems have been widely deployed in diverse domains such as e-Commerce, e-Health, e-Government, digital libraries, personalized travel planning as well as tourist and cultural heritage services. They are particularly promising for users with special needs. The exciting possibilities of such deployed adaptive hypermedia systems rely on research progress in a broad range of areas such as: user pro- ing and modeling; acquisition, updating and management of user models; group modeling and community-based pro?ling;recommender systems and recomm- dation strategies; data mining for personalization; the Semantic Web; adaptive multimedia content authoring and delivery; ubiquitous computing environments and Smart Spaces; personalization for the plethora of mobile devices, such as PDAs, mobile phones and other hand-held devices; and pragmatics such as p- vacy, trust and security. Empirical studies of adaptive hypermedia and Web systems are also critical to informing future directions. The AdaptiveHypermediaconferenceshavebecomethe majorforumsforthe scienti?c exchange and presentation of research results on adaptive hypermedia and adaptive Web-based systems.

5th International Conference, AH 2008, Hannover, Germany, July 29 - August 1, 2008, Proceedings

Adaptive Hypermedia and Adaptive Web-Based Systems

Mary Lou Maher

Design is believed to be one of the most interesting and challenging problem-solving activities ever facing artificial intelligence (AI) researchers. Knowledge-based systems using rule-based and model-based reasoning techniques have been applied to build design automation and/or design decision support systems. Although such systems have met with some success, difficulties have been encountered in terms of formalizing such generalized design experiences as rules, logic, and domain models. Recently, researchers have been exploring the idea of using case-based reasoning (CBR) techniques to complement or replace other approaches to design support. CBR can be considered as an alternative to paradigms such as rule-based and model-based reasoning. Rule-based expert systems capture knowledge in the form of if-then rules which are usually identified by a domain expert. Model-based reasoning aims at formulating knowledge in the form of principles to cover the various aspects of a problem domain. These principles, which are more general than if-then rules, comprise a model which an expert system may use to solve problems. Model-based reasoning (MBR) is sometimes called reasoning from first principles. Instead of generalizing knowledge into rules or models, CBR is an experience-based method. Thus, specific cases, corresponding to prior problem-solving experiences, comprise the main knowledge sources in a CBR system. This volume includes a collection of chapters that describe specific projects in which case-based reasoning is the focus for the representation and reasoning in a particular design domain. The chapters provide a broad spectrum of applications and issues in applying and extending the concept of CBR to design. Each chapter provides its own introduction to CBR concepts and principles.

Pearl Pu

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