Contextual Knowledge Sharing And Cooperation In Intelligent Assistant Systems.

The role of contextual information in intelligent assistant systems is controversial. In this paper, we start from our experience of Intelligent Assistant System developers to clarify some notions about context and to study the question of context sharing. Moreover, we consider two important aspects of man-machine cooperation, namely explanation generation and incremental knowledge acquisition. Making context explicit in cooperative systems is the key factor for any implementation of these two concepts. Starting from our experience in the development of knowledge-based systems, especially of an interactive system for incident management in subway control, we explain our views about context for the development of intelligent assistant systems

JOINT COGNITIVE SYSTEMS, COOPERATIVE SYSTEMS AND DECISION SUPPORT SYSTEMS: A COOPERATION IN CONTEXT

We present the lessons drawn from a review of the main concepts put forward by the designers of decision support systems, joint cognitive systems and cooperative systems. A striking observation is that these systems stumble on user-system cooperation. The main idea of this paper is that interactive system must behave more as an intelligent assistant than as an expert. Key elements of an effective cooperation between a user and a system are making explicit cooperation context and extending consequently the notions of explanations, knowledge acquisition and machine learning

ABSTRACT DECISION MAKING AT A CROSSROAD: A NEGOTIATION OF CONTEXTS

The initial training generally puts the driver in a supervised learning context that is not the autonomous-driving context in which decision making and responsibility are assumed by the driver alone. As a consequence, the beginner is not able to contextualize the learned procedures and transform them in effective practices, to identify and manage pre-critical situations and to evaluate the degree of danger of situations. Starting from the studies and works presented in the literature, we propose a twofold approach based on the one hand on global methods and, on the other hand, on local methods. We first represent a driver in two spaces (situations and behaviors) related through scenarios. The situation space is an objective representation by a lattice and the other one is a subjective representation by contextual graphs. Our second goal is to provide the driver with a system for a self training and self evaluation of his behavior in different pre-critical situations. This paper discusses the cognitive part of the approach and objectives on a case study

Context sensitive decision support systems in road safety

International audienceEnterprises often embed decision-making processes in procedures in order to address issues in all cases. However, procedures often lead to sub-optimal solutions for any specific decision. As a consequence, each actor develops the practice of addressing decision making in a specific context. Actors contextualize decision making when enterprises are obliged to decontextualize decision making to limit the number of procedures and cover whole classes of decision-making processes by generalization. Practice modeling is not easy because there are as many practices as contexts of occurrence. This chapter proposes a way to deal effectively with practices. Based on a conceptual framework for dealing with context, we present a context-based representation formalism for modeling decision making and its realization by actors. This formalism is called contextual graphs and is discussed using the example of modeling car drivers’ behaviors