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Proceedings of the 34th Annual Hawaii International Conference on System Sciences (2001)
Maui, Hawaii
Jan. 3, 2001 to Jan. 6, 2001
ISSN: 1530-1605
ISBN: 0-7695-0981-9
pp: 3021
Intelligent systems and soft computing are part of the movement towards developing effective intelligent systems for problem solving and decision making, and systems that can deal with complex and ill-structured situations, i.e. contexts for which discovery and learning can positively impact the outcome of the problem solving process. In the cutting-edge practice of intelligent systems design there are modeling techniques that govern the design of KBS systems, and there are several choices of computational models, that can be used to implement those KBS architectures.In the minitrack, we want to explore intelligent systems designs and computational models and to identify emerging paradigms underlying the design and development of intelligent systems of the future. In recent years, a number of innovative applications have been presented and published; in the minitrack, we want to explore and understand both successes and failures with new systems constructs. The next generation of modeling tools and support systems will include (but is not limited to) the use of intelligent technologies (machine intelligence, neural nets, genetic algorithms), soft computing (fuzzy logic, approximate reasoning, probabilistic modeling) and advanced mathematical modeling.There is an increasing demand for smart systems (standard software tools enhanced with intelligent modules) for interactive planning, problem solving and decision making, by individuals or by groups of users. The resulting systems will be more robust, more adaptive and easier to use than conventional tools. The optimization models (most of the time multiple criteria models) will be more easily incorporated in support systems. The expected end result is a generation of support systems which give the users knowledge-based support which is adapted both to the problems they need to solve and the decision making expected of them and, furthermore, to the internal logic of the context in which they will have to carry out their activities.There is a growing interest in soft computing tools, which are used to handle imprecision and uncertainty, and to build flexibility and context adaptability into intelligent systems. The application of soft computing to decision problems is focused on the “new economy” decision context, where fast and correct decision making is becoming instrumental as the context is becoming increasingly complex, and will change more and more rapidly. There is no great consensus on what exactly will form the “new economy” context, but some of the key elements will most probably be, (i) virtual teamwork in different places and in different time zones, (ii) decision support systems on mobile devices, with (iii) access to and the use of multilayer networks (Internet(s), intranets), through which (iv) access to and the use of a multitude of data sources (databases, data warehouses, text files, multimedia sources, etc.), and with support from (v) intelligent technologies for filtering, sifting and summarizing (software agents, machine intelligence, evolutionary computing, neural nets, etc.) and (vi) multiple criteria (crisp, soft) algorithms for problem solving. In the minitrack on Intelligent Systems and Soft Computing we aim to aim to explore the issues raised by the introduction of new technology to handle decision problems. The papers accepted for the minitrack include: Prototype Matching-Finding Meaning in the Books of Bible, A. Visa, J. Toivonen, H. Vanharanta and B. Back Operational Knowledge Representation for Practical Decision Making, L. Pasquier, P. Brezillon and J.C. Pomerol An Approach to Multiple Attribute Decision Making Based on Preference Information on Alternatives, J. Ma, Q. Zhang, Z. P. Fan and J. Liang A Language for the Rapid Prototyping of Mobile Evolving Agents, W. Muller, A. Meyer and H. Zabel Effects of Symbiotic Evolution in Genetic Algorithms for Job-Shop Scheduling, Y. Tsujimura, Y. Mafune and M. Gen Reducing the Bullwhip Effect by Means of Intelligent, Soft Computing Methods, C. Carlsson and R. Full?r A Fingerprint Recognizer Using Fuzzy Evolutionary Programming, T.V. Le, K.Y. Cheung and M.H. Nguyen

P. Walden and C. Carlsson, "Intelligent Systems and Soft Computing," Proceedings of the 34th Annual Hawaii International Conference on System Sciences(HICSS), Maui, Hawaii, 2001, pp. 3021.
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