By Sergio Pajares, Eva Onaindia (auth.), Ioannis Hatzilygeroudis, Jim Prentzas (eds.)
The blend of alternative clever tools is a really lively learn region in man made Intelligence (AI). the purpose is to create built-in or hybrid equipment that make the most of every one in their parts. a few of the present efforts mix delicate computing tools both between themselves or with extra conventional AI tools corresponding to good judgment and principles. one other movement of efforts integrates laptop studying with soft-computing or conventional AI tools. another integrates agent-based techniques with common sense and in addition non-symbolic methods. a few of the mixtures were really very important and extra largely used, like neuro-symbolic equipment, neuro-fuzzy tools and techniques combining rule-based and case-based reasoning. although, there are different mixtures which are nonetheless less than research, comparable to these on the topic of the Semantic Web.
The 2nd Workshop on “Combinations of clever tools and functions” (CIMA 2010) used to be meant to turn into a discussion board for changing event and concepts between researchers and practitioners who're facing combining clever equipment both in accordance with first ideas or within the context of particular purposes. CIMA 2010 was once held at the side of the 22nd IEEE foreign convention on instruments with synthetic Intelligence (ICTAI 2010). additionally, a different song used to be equipped in ICTAI 2010, less than an analogous identify.
This quantity comprises revised models of the papers awarded in CIMA 2010 and one of many brief papers awarded within the corresponding ICTAI 2010 specified tune. additionally it is a paper of the editors as invited.
Read or Download Combinations of Intelligent Methods and Applications: Proceedings of the 2nd International Workshop, CIMA 2010, France, October 2010 PDF
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Additional info for Combinations of Intelligent Methods and Applications: Proceedings of the 2nd International Workshop, CIMA 2010, France, October 2010
To model these characters, the user defines character coefficients β1 , . . 4 I. Toulgoat, P. Siegel, and Y. Lacroix Weight Function Finally, we use a simple function to aggregate criteria: the weighted sum. For each extension Ei , we have: n P (Ei ) = βk μk (Ei ) (18) k=1 with the equation 17. The sum of the extension weight functions has the following property: p P (Ei ) = 1. (19) i=1 We obtain a general formula, which could be easily reused with other case study. The use of the weighted sum as aggregation function implies some limitations .
We define a method to select an extension, thanks to the weight function. 1 Extension Selection Principles First, we study some principles and requirements to which the extension selection must answer: 1. to choose the interesting extension and let the others aside. Example: NoMROD proposes two extensions: to make a random research trajectory or to rise to the surface and use the snorkel to take air. The random research trajectory is a rule of minimal change: it is applied while the submarine has no new information.
Adding priorities and specificity to default logic. J. ) JELIA 1994. LNCS (LNAI), vol. 838, pp. 247–260. Springer, Heidelberg (1994) 5. : A robust layered control system for a mobile robot. IEEE Journal of Robotics and Automation 2(1), 14–23 (1985) 6. : A temporal revision model for reasoning about world change. Journal of Intelligent Systems 9(1), 131–144 (1994) 7. Ferber: Les syst`emes multi-agents. Vers une intelligence collective. InterEditions (1995) 8. : Reasoning about action 1: a possible worlds approach.