Abstract
Context awareness in Case-Based Reasoning (CBR) systems has become
a topic of increased research of late. In CBR, context serves as
a major source for reasoning, decision-making, and adaptation. Achieving
context-awareness for context-sensitive CBR systems will depend on
their ability to represent and manipulate in formation about a rich
range of contextual factors. These factors may include not only physical
characteristics of the task environment, but many other aspects such
as the knowledge states (of both the application and user), and user
beliefs and emotions. There presentation and reasoning problem therein
presents research challenges to which numerous methods and techniques
derived from artificial intelligence and knowledge management (e.g.,
logical reasoning, object relationship models, ontologies, similarity
measures, and intelligent retrieval mechanisms) are now being brought
to bear.
This workshop served as a discussion platform to researchers and practitioners
exploring issues and approaches for context-sensitive systems involving
CBR to share their problems and techniques. The discussion extended
towards mechanisms and techniques for structured storage of contextual
information, effective ways to retrieve, reuse, and adapt it, as
well as methods for enabling integration of context and application
knowledge. The main question raised at the workshop is how to deal
with contextual and/or contextualized in formation, e.g., contextualized
cases for a CBR system. To kickstart this discussion, three selected
papers we represented, which opened the discussions with specific
questions about context-awareness, explanations, and context ontology
issues.
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