By Gheorghe Tecuci, Mihai Boicu, Kathryn Wright, Seok Won Lee, Dorin Marcu, Michael Bowman (auth.), Horia-Nicolai Teodorescu, Daniel Mlynek, Abraham Kandel, H.-J. Zimmermann (eds.)
The box of "intelligent interfaces and structures" has visible a quick development final decade. a magnificent variety of papers, convention tutorials, and volumes have been dedicated to the subject. Ten years in the past, clever structures constituted a slightly unique subject and lots of have been skeptic that such structures quantity to greater than a pleasant identify. these days, clever platforms characterize a strong software in lots of purposes, in all commercial fields. Their improvement advanced on either the horizontal measurement, with a continually expanding variety of functions, and at the vertical size, by way of together with extra services going from sensoric to neurofuzzy structures, clever brokers, speech and snapshot knowing, and choice making in complicated environments. The area constituted by means of the clever structures is now too huge to be coated in one quantity. as a result, the Editors thought of a stability among a number of chosen sub-fields, to insure the harmony of the quantity, but permitting a wide sufficient horizon and a constant realizing of the whole box, together with real-life functions. the quantity represents a entire insurance of the sector, together with primary features, software-, sensors-, and hardware-related concerns. furthermore, the members to this quantity supply, past a scientific evaluation of clever interfaces and platforms, deep, useful wisdom in development and utilizing clever platforms in a number of purposes. a distinct emphasis is on particular facets and requisites in purposes. furthermore, the second one Editor proposes chapters addressing the administration of tasks facing clever systems.
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It may learn from a repository of information (such as a database) or it may learn from its own experience. Building a practical autonomous learning agent that can acquire and update its knowledge by itself is not yet practical; we do not yet understand enough about the cognitive process of learning. Therefore, a more practical approach is to develop an interactive learning agent that can interact with an expert. Such an agent can perform most of the functions of the knowledge engineer. It allows the expert to communicate expertise in a way familiar to himlher and is responsible for building, updating and reorganizing the knowledge base.
Also, importing concepts and features from previously developed ontologies, although very appealing is actually quite hard to accomplish. We are therefore planning to develop methods where the modeling process and the agent provide more guidance in identifying the knowledge pieces to import. Finally, we need to develop a more powerful and natural approach to hint specification by the expert. The current types of allowable hints do not constrain the search for explanations enough, and some of them are not very intuitive for the expert.
Problem-solving models for generation of task-specific knowledge acquisition tools. In Cuena J. ) Knowledge-Oriented Software Design, Elsevier, Amsterdam. , and Hendler, J. 1997. Efficient Management of Very Large Ontologies. In Proc. , AAAI Press. Tecuci, G. 1998. Building Intelligent Agents: An Apprenticeship Multistrategy Learning Theory, Methodology, Tool and Case Studies. London, England, Academic Press. , Marcu, D. and Bowman, M. 1999. An Integrated Shell and Methodology for Rapid Development of Knowledge-Based Agents.