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Building of a Corporate Memory for Traffic-Accident Analysis

AI Magazine

This article presents an experiment of expertise capitalization in road traffic-accident analysis. We study the integration of models of expertise from different members of an organization into a coherent corporate expertise model. We present our elicitation protocol and the generic models and tools we exploited for knowledge modeling in this context of multiple experts. We compare the knowledge models obtained for seven experts in accidentology and their representation through conceptual graphs. Finally, we discuss the results of our experiment from a knowledge capitalization viewpoint.


Systems Theoretic Techniques for Modeling, Control, and Decision Support in Complex Dynamic Systems

arXiv.org Artificial Intelligence

We discuss the problems of modeling, control, and decision support in complex dynamic systems from a general system theoretic point of view. The main characteristics of complex systems and of system approach to complex system study are considered. We provide an overview and analysis of known existing paradigms and methods of mathematical modeling and simulation of complex systems, which support the processes of control and decision making. Then we continue with the general dynamic modeling and simulation technique for complex hierarchical systems functioning in control loop. Architectural and structural models of computer information system intended for simulation and decision support in complex systems are presented.


Strategic Directions in Artificial Intelligence

AI Magazine

This report, written for the general computing and scientific audience and for students and others interested in artificial intelligence, summarizes the major directions in artificial intelligence research, sets them in context relative to other areas of computing research, and gives a glimpse of the vision, depth, research partnerships, successes, and excitement of the field. This article is reprinted with permission from ACM Computing Surveys 28(4), December 1996. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page.


Building of a Corporate Memory for Traffic-Accident Analysis

AI Magazine

This article presents an experiment of expertise capitalization in road traffic-accident analysis. We study the integration of models of expertise from different members of an organization into a coherent corporate expertise model. We present our elicitation protocol and the generic models and tools we exploited for knowledge modeling in this context of multiple experts. We compare the knowledge models obtained for seven experts in accidentology and their representation through conceptual graphs. Finally, we discuss the results of our experiment from a knowledge capitalization viewpoint.


Reports

AI Magazine

The third workshop on activity context-aware system architectures sought to explore architectures for intelligent context-aware systems delivering complex functionality, with direct access to information, simplifying business processes and activities while providing domain-specific and task-specific depth in interactive banking, insurance, wealth management, finance, clinical, legal, telecom customer service, operations, supply chain, connected living room, and personal assistants. Such systems understand not just words, but intentions and the context of the interaction. Such architectures are expected to dramatically improve the quality of proactive decision support provided by virtual agents by enabling them to seek explanations, make predictions, generate and test hypotheses, and perform what-if analyses. The systems can provide extreme personalization (N 1) by inferring user intent, making relevant suggestions, maintaining context, carrying out cost-benefit analysis from multiple perspectives, finding similar cases from organizational and personal episodic memory before such cases are searched for, finding relevant documents and answers, and finding issues resolved by experts in similar situations. The architectures are expected to enable meaningfully relating, finding, and connecting people and information sources through discovery of causal, temporal, and spatial relations.