CALL FOR PAPERS International Conference Integration of Knowledge Intensive Multi-Agent Systems: KIMAS'03: Modeling, Exploration, and Engineering http://www.ieee-boston.org/kimas03.htm Sponsored by IEEE Boston Section in cooperation with IEEE NN Society and IEEE SMC Society, AAAI, INNS, US Air Force, US Army, US Navy, DARPA General Chair L. Perlovsky, US AF Program Chair A. Meystel, Drexel University Finance Chair J. Schindler, US AF, ret'd Tutorials Chair M. Rangaswami, US AF Publicity Chair M. Kokar, Northeastern University Publications Chair H. Hexmoor, University of Arkansas Local Arrangement Chair Robert Alongi, IEEE Boston Section Dates and places Royal Sonesta Hotel, Cambridge MA 1-3 October 2003 Proposals for tutorials and sessions 1 February 2002 Abstract due 1 February 2003 Notification of acceptance 1 March, 2003 Paper due 1 July, 2003 The Goals of the Conference 1. Integrated Knowledge Intensive Systems Integrated knowledge intensive systems emerge in all domains of business and engineering, when intelligent decision support, sensor operation, signal processing, and knowledge utilization requires knowing how this knowledge is produced, measured, and interpreted. We are used to conquer by decimating not integrating hence the present challenge of Integrated Knowledge Intensive Systems. Application domains include intelligent communication systems in the network-centric environment, sensors and sensor networks design and operation, analysis of situations in business and industry, battlefield awareness, automatic target recognition, signal and natural language processing, bioinformatics, gene profiling, drug discovery, control of autonomous robotics, financial prediction and management systems, search engines, text and language understanding, computational linguistics, data mining. 2. Multiagent Intelligent Systems. All knowledge intensive systems are multiagent ones. An agent is a concept of a device or a program that is significantly autonomous and goal-oriented; it performs its functions, and communicates with other agents. An agent might be a piece of hardware or software, it acts alone or as a part of multiagent system toward the goal, it is equipped with sensors, collects data, extracts information using existing knowledge and integrates this information into producing new knowledge; these functions of agents in multiagent systems embody the concept of Life and Intelligence. Agents use knowledge to develop behavior that produces actions and new knowledge. Between old and new knowledge there are data collection, information extraction, knowledge construction and behavior generation. Networks of knowledge, interwoven with intentional systems of goal oriented agents producing networks of actions, account for the available options in decision making based upon ontology (knowledge, phenomenology, models) and computational processes. Not only the knowledge repositories should contain the results of acquired data and information sets, they also must contain structures for integrating and understanding knowledge, or ontologies including models that might be detailed or approximate, reflecting precisely known physical laws, or uncertain intuitions about undiscovered phenomena or human nature: physical models of sensors or wave propagation and scattering; chemical models of molecular interactions; statistical models of object properties; dynamical models of motion; linguistic text models; informational models; semiotic models of meaning; cultural models of human behavior in the industry, or a society of interest; multi-factor models of performance evaluation 3. Computations Pertained to Intellect: Multiscale Search for Similarity The main challenge of Multiagent Intelligent Systems is in the development of integrated knowledge-intensive computation processes and structures capable of acquiring data, integrating it with knowledge, transforming knowledge, learning from experience, and creating new knowledge by incorporating arrivals from multiple disciplines and sensors. This formidable problem is known to be resolved by a consecutive process of maximizing similarity between models and incoming signals; similarity maximization could be a simple least mean squares or complex one, including multiscale hierarchies, linguistic or cultural behavior models, conceptual and emotional processing, tools of semiotics, symbolic systems, and generalization. Integration, discovery and disambiguation of knowledge in systems of interacting agents coordinated within the set of goals is the essence of successful decision making. 4. A Formidable Resemblance: Similar Algorithmic Structures In all multidisciplinary areas employing Multiagent Intelligent Systems, the algorithms are surprisingly similar. This resemblance contains hints to answering many complex questions. The conference will focus primarily upon areas important in scientific research, business, and defense operations: integrated closed-loop operation of data acquisition ? information extraction ? knowledge construction ? action ? data acquisition. The conference will focus on tools and techniques of multiresolutional data, information, and knowledge analysis, entities discovery and recognition, exploratory large data arrays processing, signal and image analysis and understanding, objects, scenes, and situation identification, design of efficient sensor systems, multimodal data fusion, sensory and textual data fusion, analysis of text messages, natural language text interpretation and understanding, integrated closed-loop self-optimizing structures. 5. Focal themes. The following fundamental ideas will be the focal themes at this conference: 1. Models and Similarity Measures for Image Recognition, Natural Language Processing, Situation Analysis 2. Multiagent Calculus: Theoretical Fundamentals for Analysis of Knowledge Intensive Systems 3. Multiresolutional Systems for Knowledge Representation 4. Mechanisms of Integration: Algorithms and Processes of Generalization/Instantiation 5. Architectures of Intellect-like Computational Processes 6. Search for Exploring Bodies of Data, Information, and Knowledge 7. Hypotheses Generation and Disambiguation in Knowledge Intensive Systems Researchers from academia, commerce, and defense research centers will exchange ideas. Program managers will inform on the directions of research and development. The conference will include invited and contributed sessions, plenary lectures and discussions, and tutorials. Abstract Submission Procedure A 1 - 2 page abstract may be submitted. The deadline for submission is 1 February 2003. Electronic submissions may be sent to: alex@impact.drexel.edu Hard copy submissions may be sent to: Alex Meystel KIMAS '03 Technical Program Chair ECE Dept., Drexel University Philadelphia, PA, USA 19104 Exhibit Information. A small number of exhibits will be made available. If you are interested, please contact Bob Alongi,IEEE Boston Section at, sec.boston@ieee.org. Advisory Board: S. Adams, IBM Research, USA; A. E. Aktan, Drexel U. , USA; J. Albus, NIST, USA; L. Arata, Quinnipiac U. , USA; P. Borne, Ecole Centrale de Lille, France; N. Cassaigne, U. Manchester, UK; A. Chikrii, Ukrainian Academy Sciences, Ukraine; S. - K. Chin, SUNY, USA; R. Cottam, The Evolutionary Processing Group, Belgium; G. Cybenko, Dartmouth College, USA; F. Darema, NSF, USA; E. Dawidowicz, US Army, CECOM; S. DeLoach, Kansas State U. , USA; K. -F. Ding, AFRL, USA; E. Durfee, U. Michigan, USA; J. Fontanari, U. San Carlos, Brazil; T. Fukuda, Nagoya U. , Japan; V. Golovko, Brest Polytechnic Institute, Belarus; E. Grant, North Carolina State U. , USA; T. Henderson, U. Utah; H. Hexmoor, U. Arkansas; A. Jones, NIST, USA; D. Klose, US Army, CECOM, USA; S. Lee, Samsung Advanced Institute of Technology, S. Korea; L. Levitin, Boston U. , USA ; T. Luginbuhl, US Navy, NUWC, USA; R. Mayorga, Regina U. , Canada; R. Mehra, SSCI, USA; T. Mei, Chinese Academy of Sciences, China; R. Merkuryev, Riga Technical U. , Latvia; E. Messina, NIST, USA; O. Mikhailov, ChevronTexaco; A. Omicini, U. Bologna, Italy; S. Papert, MIT, USA; R. J. Patton, U. Hull, UK; E. S. Pyatnitskiy, Russian Academy of Sciences, Russia; S. Ramaswamy, Tennessee Technological U. , USA; D. Repperger, AFRL, USA; B. Rieger, Professor, U. Trier, Germany; L. Rocha, Los Alamos Nat'l Laboratory, USA; E. Ruspini, SRI, USA; E. Santos, Jr. , U. Connecticut, USA; R. Sanz, Politechnical U. Madrid, Spain; D. Schmorrow, DARPA, USA; A. Schultz, NRL, USA; J. Spall, John Hopkins U. , USA; L. Sterling, U. Melbourne, Australia ; B. Stilman, Advanced Strategies, Inc. , USA; H. Szu, George Washington U. , USA; T. Tan, Chinese Academy of Sciences, China; I. Ternovskyi, Intelligent Optical Systems, Inc. , USA; W. Truszkowski, NASA, USA; L. Tsoukalas, Purdue U. , USA; J. M. Vidal, U. South Carolina, USA; F. -Y. Wang, U. Arizona, USA; K. Weigand, AFRL, USA; W. van Wezel, U. Groningen, Netherlands; L. Zadeh, U. California, USA; Q. Zhu, U. Nebraska, USA; B. Zeigler, U. Arizona, USA; R. R. Yager, Iona College, USA; G. Yen, Oklahoma State U. , USA;