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Toward Interactive and Intelligent Decision Support Systems: by Andrew P. Sage, Ambrose Goicoechea, Peter H. Aiken (auth.),

By Andrew P. Sage, Ambrose Goicoechea, Peter H. Aiken (auth.), Prof. Dr. Yoshikazu Sawaragi, Prof. Dr. Koichi Inoue, Prof. Dr. Hirotaka Nakayama (eds.)

In the prior, technological in addition to fiscal forces ruled the evolution of business buildings: those elements were handled greatly in different experiences. notwithstanding, one other significant component which has started to have a decisive influ­ ence at the functionality of the chemical is technological hazard and public and environmental wellbeing and fitness issues, specifically these regarding poisonous and unsafe ingredients utilized in commercial creation techniques. the problems of con­ trolling strategy danger, waste streams, and strength environmental effects of unintended or regimen liberate of dangerous chemical substances are swiftly gaining in impor­ tance vis CI vis slim financial concerns, and are more and more mirrored in nationwide and overseas laws. within the context of a number of ongoing R&D initiatives aiming on the improvement of a brand new iteration of instruments for "intelligent" selection aid, comparable areas of difficulty which have been pointed out are: (i) Structuring the or plant for the minimal price of construction in addition to least probability - e.g., toxicity of chemical substances concerned. during this multi-criteria framework, we search to unravel the clash among commercial constitution or plant layout confirmed via monetary issues and the only formed through environmental matters. this is often formulated as a layout challenge for nor­ mal construction stipulations. In part 3.1. and 3.2. an procedure on the right way to take care of this challenge on the and plant point is discussed.

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Extra resources for Toward Interactive and Intelligent Decision Support Systems: Volume 2 Proceedings of the Seventh International Conference on Multiple Criteria Decision Making Held at Kyoto, Japan August 18–22, 1986

Example text

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Specialized in all the component. ). he decision problem. elligent judgement. he major objectives of AI. way, 1986). ems are most. ems analysis. ation. e small amount. s power and usefulness and at. he same time make it. o use. ems are not. housands of rules. Applications containing only small knowledge bases of at. best. y. em (Fedra, 1985, 1986a; Fedra et. , 1986b,c) and provides direct. o a broad group of users. hmic model no longer apply. em.

L(c) and (b) show complex events and their component primitive events extracted from (a), respectively. As shown in this figure, complex events are not always consists of primitive events directly. For instance, the complex event "revenge" can be found by replacing its component primitive event "success" with another complex event "acceptedrequest," which represents that some actor's event is attained indirectly by making another actor execute its component event instead of being performed by himself.

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