2002 AAAI Spring Symposium on Information Refinement and Revision for Decision Making: Modeling for Diagnostics, Prognostics, and Prediction Palo Alto, California • March 25-27, 2002
Home Symposium Dates, times, and places  Format  List of Accepted Papers  Paper Submission  About AAAI  Decision Making in M&D Background  Examples  Artificial Intelligence Challenge problems  Software and Data Existing software  Useful datasets  References FAQ  Web sites  Bibliography  Contact Comments  Subscriptions	Many companies have discovered the value of preserving and maintaining their corporate knowledge as they are collecting large amount of process data and business information. This collection is accelerated by the use of advanced and less expensive sensors, massive information storage, and internet-facilitated access. As a result, diagnostic decision makers are faced with the daunting task of extracting relevant morsels from this information hodge-podge, dealing with conflicting information, repudiating stale and outdated information, and evaluating the merits of a found solution. Automated decision-making systems also need to heed the effect of degrees of redundancy in the information considered, which may skew the decision pursued. In addition, temporal effects play a major role in the decision making process not only because information integrity fades over time but also because new information needs to be factored in. Although this new information does not exist at the time of the system design, one must provide a system maintenance plan to account for it. Ways to judge the relevance of this new information and optimization issues need to be discussed in this context. Finally, the quality and uncertainty of the newly found system and its resulting decisions need to be evaluated. This workshop will explore some of the following topics within that context: Conflict resolution Information half-life Adaptive optimization Uncertainty management Distributed evolutionary agents Temporal information updating Link discovery in large databases Distributed resource management Aggregation of heterogeneous information Distributed multiple hypothesis management Automated updating of classification systems Maintenance of decision making units over time Postponement of commitments in design analysis Interactive tradeoff analysis between search and decision Multi-criteria decision making based on changing information Organizing Committee Alice Agogino Department of Mechanical Engineering, UC Berkeley Piero Bonissone GE Corporate Research & Development Kai Goebel GE Corporate Research & Development Soundar R.T. Kumara Dept. of Industrial and Manufacturing Engineering, Penn State Karl Reichard Applied Research Laboratory, Penn State George Vachtsevanos School of Electrical and Computer Engineering, Georgia Institute of Technology Xenofon Koutsoukos Xerox PARC
Version 1.0 Updated 9/19/01