This article discusses the Thirteenth International Distributed AI Workshop. An overview of the workshop is given as well as concerns and goals for the technology. This article discusses the Thirteenth International Distributed AI Workshop. An overview of the workshop is given as well as concerns and goals for the technology.

The Association for the Advancement of Artificial Intelligence (AAAI) held its 1995 Fall Symposia Series on 10 to 12 November in Cambridge, Massachusetts. This article contains summaries of the eight symposia that were conducted: (1) Active Learning; (2) Adaptation of Knowledge for Reuse; (3) AI Applications in Knowledge Navigation and Retrieval; (4) Computational Models for Integrating Language and Vision; (5) Embodied Language and Action Symposium; (6) Formalizing Context; (7) Genetic Programming; and (8) Rational Agency: Concepts, Theories, Models, and Applications.

The Financial Crimes Enforcement Network (FIN-CEN) AI system (FAIS) links and evaluates reports of large cash transactions to identify potential money laundering. The objective of FAIS is to discover previously unknown, potentially high-value leads for possible investigation. FAIS consists of an underlying database (that functions as a black-board), a graphic user interface, and several preprocessing and analysis modules. FAIS has been in operation at FINCEN since March 1993; a dedicated group of analysts process approximately 200,000 transactions a week, during which time over 400 investigative support reports corresponding to over $1 billion in potential laundered funds were developed.

This report stems from a workshop that was organized by the Association for the Advancement of Artificial Intelligence (AAAI) and cosponsored by the Information Technology and Organizations Program of the National Science Foundation. The purpose of the workshop was twofold: first, to increase awareness among the artificial intelligence (AI) community of opportunities presented by the National Information Infrastructure (NII) activities, in particular, the Information Infrastructure and Tech-nology Applications (IITA) component of the High Performance Computing and Communications Program; and second, to identify key contributions of research in AI to the NII and IITA.

The Association for the Advancement of Artificial Intelligence held its 1995 Spring Symposium Series on March 27 to 29 at Stanford University. This article contains summaries of the nine symposia that were conducted: (1) Empirical Methods in Discourse Interpretation and Generation; (2) Extending Theories of Action: Formal Theory and Practical Applications; (3) Information Gathering from Heterogeneous, Distributed Environments; (4) Integrated Planning Applications; (5) Interactive Story Systems: Plot and Character; (6) Lessons Learned from Implemented Software Architectures for Physical Agents; (7) Representation and Acquisition of Lexical Knowledge: Polysemy, Ambiguity, and Generativity; (8) Representing Mental States and Mechanisms; and (9) Systematic Methods of Scientific Discovery.

The annual Workshop on the Validation and Verification of Knowledge-Based Systems is the leading forum for presenting research on the validation and verification of knowledge-based systems (KBSs). The 1994 workshop was significant in that there was a definitive move in the philosophical position of the workshop from a testing- and tool-based approach to KBS evaluation to that of a formal specification-based approach. This workshop included 12 full papers and 5 short papers and was attended by 35 researchers from government, industry, and academia.

The Seventh International Workshop on Natural Language Generation was held from 21 to 24 June 1994 in Kennebunkport, Maine. Sixty-seven people from 13 countries attended this 4-day meeting on the study of natural language generation in computational linguistics and AI. The goal of the workshop was to introduce new, cutting-edge work to the community and provide an atmosphere in which discussion and exchange would flourish.

My collaborators and I have recently reported in domain science journals several human-computer discoveries in biology, chemistry, and physics. My conclusion is that each finding involves a new representation of the scientific task: The problem spaces searched were unlike previous task problem spaces. Such new representations need not be wholly new to the history of science; rather, they can draw on useful representational pieces from elsewhere in natural or computer science. My analysis also suggests a broader potential role for (AI) computer scientists in the practice of natural science.

The Georgia Institute of Technology won the Office Cleanup event at the 1994 AAAI Robot Competition and Exhibition with a multirobot cooperating team. This article describes the design and implementation of these reactive trash-collecting robots, including details of multiagent cooperation, color vision for the detection of perceptual object classes, temporal sequencing of behaviors for task completion, and a language for specifying motor schema-based robot behaviors.