This paper reviews Trace-Based Reasoning (TBR), a reasoning paradigm based on interaction traces left by users in digital environments. Because interaction traces record the user’s problem-solving experiences in context, TBR facilitates the reuse of such user’s experiences. Moreover, interaction traces can be used as a knowledge source to discover other knowledge useful for the reasoning process. This paper describes TBR principles and proposes a common framework for TBR applications. In particular, we focus on the articulation between Trace-Based Reasoning and Trace-Based Systems in the context of user assistance. For this purpose, we describe knowledge and knowledge models involved in reasoning tasks. We discuss the advantages of using traces as knowledge containers for reutilization of experience. Finally, we report a brief state of the art and a work agenda for TBR. We also show why TBR can take advantage of the current dynamic of the work about traces while globally improving trace-based applications.
[1]
Thomas Roth-Berghofer,et al.
Towards Goal Elicitation by User Observation
,
2003
.
[2]
Alain Mille,et al.
Extending Case-Based Reasoning with Traces
,
2009,
IJCAI 2009.
[3]
Pierre-Antoine Champin,et al.
Coping with noisy search experiences
,
2010,
Knowl. Based Syst..
[4]
David B. Leake,et al.
Four Heads Are Better than One: Combining Suggestions for Case Adaptation
,
2009,
ICCBR.
[5]
Brian Knight,et al.
A Framework for Historical Case-Based Reasoning
,
2003,
ICCBR.
[6]
Andreas Zimmermann,et al.
Context-Awareness in User Modelling: Requirements Analysis for a Case-Based Reasoning Application
,
2003,
ICCBR.
[7]
Alain Mille,et al.
Creating Cognitive Models from Activity Analysis: A Knowledge Engineering Approach to Car Driver Modeling
,
2007
.