 C Context-Based Explanations for E-Collaboration Patrick Brezillon University Paris 6, France Copyright © 2008, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited. IntroductIon E-collaboration is generally deined in reference to ICT used by people in a common task (Kock, 2005; Kock, Davison, Ocker, & Wazlawick, 2001). However, when speaking of e-collaboration, people seems to put more the emphasis on “e-” than on “collaboration”; that is, on the ICT dimension of the concept that on the human dimension. Along the human dimension, e-collabora- tion requires to revisit previous concept of cooperation, conlict, negotiation, justiication, explanation, etc. to account for the sharing of knowledge and information in the ICT dimension. We discuss in this chapter of explanation generation in this framework. Any collaboration supposes that each participant understands how others make a decision and follows the series of steps of their reasoning to reach the decision. In a face-to-face collaboration, participants use a large part of contextual information to translate, interpret and understand others’ utterances use contextual cues like mimics, voice modulation, movement of a hand, etc. In e-collaboration, it is necessary to retrieve this contextual information in other ways. Explanation generation relies heavily on contextual cues (Karsenty & Brézillon, 1995) and thus would play a role in e-collaboration more important than in face-to-face collaboration. Fifteen years ago, Artiicial Intelligence was consid- ered as the science of explanation (Kodratoff, 1987). However, there are few concrete results to reuse now from that time. There are several reasons for that. The irst point concerns expert systems themselves and their past failures (Brézillon & Pomerol, 1997): • There was an exclusion of the human expert providing the knowledge for feeding the expert systems. When an expert generally provided something like “Well, in the context A, I will use this solution,” the knowledge engineer retained the pair {problem, solution} and forgot the initial triple {problem, context, solution} provides by the expert. The reason was to generalize in order to cover a large class of similar problems when the expert was giving a local solution. Now we know that a system needs to acquire knowledge within its context of use. • On the opposite side, the user was excluded from the noble part of the problem solving because all the expert knowledge was supposed to be in the machine: the machine was considered as the oracle and the user as a novice (Karsenty & Brézillon, 1995). Thus, explanations aimed to convince the user of the rationale used by the machine without respect to what the user knew or wanted to know. Now, we know that we need to develop a user- centered approach (Brézillon, 2003). - Capturing the knowledge from the expert, it was supposed to put all the needed knowledge in the machine, prior the use of the system. However, one knows that the exception is rather the norm in expert diagnosis. Thus, the system was able to solve 80% of the most common problems, on which users did not need explanations. Now, we know that systems must be able to acquire incre- mentally knowledge with its context of use. • Systems were unable to generate relevant ex- planations because they did not pay attention to what the user’s question was really, in which context the question was asked. The request for an explanation was analyzed on the basis of the available information to the system. Thus, the three key lessons learned are (1) KM stands for management of the knowledge in its context; (2) any collaboration (including e-collaboration) needs a user-centered approach; and (3) an intelligent system must incrementally acquires new knowledge and learns corresponding new practices. Focusing on explanation generation, it appears that a context-based formalism for representing knowledge and reasoning allows to introduce the end-user in the