David B. Leake – författare

Psychology and philosophy have long studied the nature and role of explanation. More recently, artificial intelligence research has developed promising theories of how explanation facilitates learning and generalization. By using explanations to guide learning, explanation-based methods allow reliable learning of new concepts in complex situations, often from observing a single example. The author of this volume, however, argues that explanation-based learning research has neglected key issues in explanation construction and evaluation. By examining the issues in the context of a story understanding system that explains novel events in news stories, the author shows that the standard assumptions do not apply to complex real-world domains. An alternative theory is presented, one that demonstrates that context -- involving both explainer beliefs and goals -- is crucial in deciding an explanation's goodness and that a theory of the possible contexts can be used to determine which explanations are appropriate. This important view is demonstrated with examples of the performance of ACCEPTER, a computer system for story understanding, anomaly detection, and explanation evaluation.

Psychology and philosophy have long studied the nature and role of explanation. More recently, artificial intelligence research has developed promising theories of how explanation facilitates learning and generalization. By using explanations to guide learning, explanation-based methods allow reliable learning of new concepts in complex situations, often from observing a single example. The author of this volume, however, argues that explanation-based learning research has neglected key issues in explanation construction and evaluation. By examining the issues in the context of a story understanding system that explains novel events in news stories, the author shows that the standard assumptions do not apply to complex real-world domains. An alternative theory is presented, one that demonstrates that context -- involving both explainer beliefs and goals -- is crucial in deciding an explanation's goodness and that a theory of the possible contexts can be used to determine which explanations are appropriate. This important view is demonstrated with examples of the performance of ACCEPTER, a computer system for story understanding, anomaly detection, and explanation evaluation.

Computing in context has become a necessity in modern and intelligent IT - plications. With the use of mobile devices and current research on ubiquitous computing, context-awareness has become a major issue. However, context and context-awareness are crucial not only for mobile and ubiquitous computing. They are also vital for spanning various application areas, such as collaborative softwareand Web engineering,personaldigital assistantsand peer-to-peer inf- mation sharing, health care work?ow and patient control, and adaptive games and e-learning solutions. In these areas, context serves as a major source for reasoning, decision making, and adaptation, as it covers not only application knowledge but also environmental knowledge.Likewise, modeling and retrieving context is an important part of modern knowledge management processes. In addition, context can play a role in determining what information a s- tem should provide. This is important for supporting the users of automated or intelligent systems, for tasks such as explaining how solutions are found, what the system is doing, and why it operates in a certain way. The methods applied and the advice given have to be explained, so that the user can understand the process and agree on decisions. Context is equally important for deciding when to provide uncertain or blurred information, e.g., when using a tracking system in situations for which either revealing the current position, or denying access to it, would have adverse e?ects. In this wide range of applications, context is now more than just location.