The Experts below are selected from a list of 294 Experts worldwide ranked by ideXlab platform
Bernard Moulin - One of the best experts on this subject based on the ideXlab platform.
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Trends in Distributed Artificial-Intelligence
Artificial Intelligence Review, 1992Co-Authors: Brahim Chaibdraa, Patrick Millot, René Mandiau, Bernard MoulinAbstract:Distributed Artificial Intelligence (DAI) is a subfield of Artificial Intelligence that deals with interactions of intelligent agents. Precisely, DAI attempts to construct intelligent agents that make decisions that allow them to achieve their goals in a world populated by other intelligent agents with their own goals. This paper discusses major concepts used in DAI today. To do this, a taxonomy of DAI is presented. based on the social abilities of an individual agent. the organization of agents, and the dynamics of this organization through time. Social abilities are characterized by the reasoning about other agents and the assessment of a Distributed situation. Organization depends on the degree of cooperation and on the paradigm of communication. Finally. the dynamics of organization is characterized by the global coherence of the group and the coordination between agents. A reasonably representative review of recent work done in DAI field is also supplied in order to provide a better appreciation of this vibrant AI field. The paper concludes with important issues in which further research in DAI is needed.
Szu Wei Yang - One of the best experts on this subject based on the ideXlab platform.
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ACIIDS (1) - Innovative semantic web services for next generation academic electronic library via web 3.0 via Distributed Artificial Intelligence
Intelligent Information and Database Systems, 2012Co-Authors: Szu Wei YangAbstract:The purpose of this article is to incubate the establishment of semantic web services for next generation academic electronic library via Web 3.0. The e-library users will be extremely beneficial from the forthcoming semantic social web mechanism. Web 3.0 will be the third generation of WWW and integrate semantics web, intelligent agent, and Distributed Artificial Intelligence into the ubiquitous networks. On top of current library 2.0 structures, we would be able to fulfill the Web 3.0 electronic library. We design the deployment of intelligent agents to form the semantic social web in order to interpret linguistic expressions of e-library users without ambiguity. This research is conducting the pioneering research to introduce the future and direction for the associate academic electronic library to follow the proposed guidelines to initiate the construction of future library system in terms of service-oriented architecture. This research article is the pioneering practice of future academic digital libraries under Web 3.0 structures.
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innovative semantic web services for next generation academic electronic library via web 3 0 via Distributed Artificial Intelligence
Asian Conference on Intelligent Information and Database Systems, 2012Co-Authors: Szu Wei YangAbstract:The purpose of this article is to incubate the establishment of semantic web services for next generation academic electronic library via Web 3.0. The e-library users will be extremely beneficial from the forthcoming semantic social web mechanism. Web 3.0 will be the third generation of WWW and integrate semantics web, intelligent agent, and Distributed Artificial Intelligence into the ubiquitous networks. On top of current library 2.0 structures, we would be able to fulfill the Web 3.0 electronic library. We design the deployment of intelligent agents to form the semantic social web in order to interpret linguistic expressions of e-library users without ambiguity. This research is conducting the pioneering research to introduce the future and direction for the associate academic electronic library to follow the proposed guidelines to initiate the construction of future library system in terms of service-oriented architecture. This research article is the pioneering practice of future academic digital libraries under Web 3.0 structures.
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Innovative semantic web services for next generation academic electronic library via Web 3.0 via Distributed Artificial Intelligence
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012Co-Authors: Hai Cheng Chu, Szu Wei YangAbstract:The purpose of this article is to incubate the establishment of semantic web services for next generation academic electronic library via Web 3.0. The e-library users will be extremely beneficial from the forthcoming semantic social web mechanism. Web 3.0 will be the third generation of WWW and integrate semantics web, intelligent agent, and Distributed Artificial Intelligence into the ubiquitous networks. On top of current library 2.0 structures, we would be able to fulfill the Web 3.0 electronic library. We design the deployment of intelligent agents to form the semantic social web in order to interpret linguistic expressions of e-library users without ambiguity. This research is conducting the pioneering research to introduce the future and direction for the associate academic electronic library to follow the proposed guidelines to initiate the construction of future library system in terms of service-oriented architecture. This research article is the pioneering practice of future academic digital libraries under Web 3.0 structures. © 2012 Springer-Verlag.
Brahim Chaibdraa - One of the best experts on this subject based on the ideXlab platform.
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Trends in Distributed Artificial-Intelligence
Artificial Intelligence Review, 1992Co-Authors: Brahim Chaibdraa, Patrick Millot, René Mandiau, Bernard MoulinAbstract:Distributed Artificial Intelligence (DAI) is a subfield of Artificial Intelligence that deals with interactions of intelligent agents. Precisely, DAI attempts to construct intelligent agents that make decisions that allow them to achieve their goals in a world populated by other intelligent agents with their own goals. This paper discusses major concepts used in DAI today. To do this, a taxonomy of DAI is presented. based on the social abilities of an individual agent. the organization of agents, and the dynamics of this organization through time. Social abilities are characterized by the reasoning about other agents and the assessment of a Distributed situation. Organization depends on the degree of cooperation and on the paradigm of communication. Finally. the dynamics of organization is characterized by the global coherence of the group and the coordination between agents. A reasonably representative review of recent work done in DAI field is also supplied in order to provide a better appreciation of this vibrant AI field. The paper concludes with important issues in which further research in DAI is needed.
Y. Dote - One of the best experts on this subject based on the ideXlab platform.
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SMC - Soft computing (immune networks) in Artificial Intelligence
SMC'98 Conference Proceedings. 1998 IEEE International Conference on Systems Man and Cybernetics (Cat. No.98CH36218), 1998Co-Authors: Y. DoteAbstract:Proposes a reactive Distributed Artificial Intelligence (dynamic) using immune networks and other soft computing methods. Firstly, extended soft computing is defined by adding immune networks and chaos theory including fractals and wavelets to conventional soft computing which is the fusion or combination of fuzzy systems, neural networks and genetic algorithms and is suited to cognitive Distributed Artificial Intelligence (static). Next, a novel fuzzy neural net (general parameter radial based function neural network) is developed in order to use it for communication among agents in immune networks. The general parameter method is extended to an adaptive structured general algorithm to obtain much faster convergence rate. An unbiasedness criterion using distorter (a radial based function network) in order to optimize parameters resulting in the reactive Distributed Artificial Intelligence kind of GMDH is applied to obtain better generalization properties. Then, this developed neural net is extended to obtain high performance.
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Soft computing (immune networks) in Artificial Intelligence
IEEE International Symposium on Industrial Electronics. Proceedings. ISIE'98 (Cat. No.98TH8357), 1998Co-Authors: Y. DoteAbstract:This paper proposes a novel reactive Distributed Artificial Intelligence (dynamic) using immune networks and other soft computing methods. Firstly, extended soft computing is defined by adding immune networks and chaos theory including fractal and wavelet to conventional soft computing which is the fusion or combination of fuzzy systems, neural networks and algorithms and is suitable for Distributed Artificial Intelligence (static). Next, a novel fuzzy neural net (general parameter radial based function neural network) is developed in order to use it for communication among agents in immune networks. The general parameter method is extended to an adaptive structured genetic algorithm to obtain much faster convergence rate. An unbiased criterion using distorter (a kind of GMDH) is applied to better generalization properties. Then, this developed fuzzy neural net is extended to a high performance radial based function network in order to optimize parameters resulting in the reactive Distributed Artificial Intelligence.
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Soft computing (immune networks) in Artificial Intelligence
IECON '98. Proceedings of the 24th Annual Conference of the IEEE Industrial Electronics Society (Cat. No.98CH36200), 1998Co-Authors: Y. DoteAbstract:This paper proposes a novel reactive Distributed Artificial Intelligence (dynamic) using immune networks and other soft computing methods. Firstly, extended soft computing is defined by adding immune networks and chaos theory including fractal and wavelet to conventional soft computing which is the fusion or combination of fuzzy systems, neural networks and genetic algorithms and is suitable for cognitive Distributed Artificial Intelligence (static). Next, a novel fuzzy neural net (general parameter radial based function neural network) is developed in order to use it for communication among agents in immune networks. The general parameter method is extended to an adaptive structured genetic algorithm to obtain a much faster convergence rate. An unbiasedness criterion using distorter (a kind of GMDH) is applied to better generalization properties. Then, this developed fuzzy neural net is extended to a high performance radial based function network in order to optimize parameters resulting in the reactive Distributed Artificial Intelligence.
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Soft computing (immune networks) in Artificial Intelligence
SMC'98 Conference Proceedings. 1998 IEEE International Conference on Systems Man and Cybernetics (Cat. No.98CH36218), 1998Co-Authors: Y. DoteAbstract:Proposes a reactive Distributed Artificial Intelligence (dynamic) using immune networks and other soft computing methods. Firstly, extended soft computing is defined by adding immune networks and chaos theory including fractals and wavelets to conventional soft computing which is the fusion or combination of fuzzy systems, neural networks and genetic algorithms and is suited to cognitive Distributed Artificial Intelligence (static). Next, a novel fuzzy neural net (general parameter radial based function neural network) is developed in order to use it for communication among agents in immune networks. The general parameter method is extended to an adaptive structured general algorithm to obtain much faster convergence rate. An unbiasedness criterion using distorter (a radial based function network) in order to optimize parameters resulting in the reactive Distributed Artificial Intelligence kind of GMDH is applied to obtain better generalization properties. Then, this developed neural net is extended to obtain high performance.
René Mandiau - One of the best experts on this subject based on the ideXlab platform.
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Trends in Distributed Artificial-Intelligence
Artificial Intelligence Review, 1992Co-Authors: Brahim Chaibdraa, Patrick Millot, René Mandiau, Bernard MoulinAbstract:Distributed Artificial Intelligence (DAI) is a subfield of Artificial Intelligence that deals with interactions of intelligent agents. Precisely, DAI attempts to construct intelligent agents that make decisions that allow them to achieve their goals in a world populated by other intelligent agents with their own goals. This paper discusses major concepts used in DAI today. To do this, a taxonomy of DAI is presented. based on the social abilities of an individual agent. the organization of agents, and the dynamics of this organization through time. Social abilities are characterized by the reasoning about other agents and the assessment of a Distributed situation. Organization depends on the degree of cooperation and on the paradigm of communication. Finally. the dynamics of organization is characterized by the global coherence of the group and the coordination between agents. A reasonably representative review of recent work done in DAI field is also supplied in order to provide a better appreciation of this vibrant AI field. The paper concludes with important issues in which further research in DAI is needed.
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Distributed Artificial Intelligence
ACM SIGART Bulletin, 1992Co-Authors: Brahim Chaib-draa, René Mandiau, Patrick MillotAbstract:A PLATFORM TO MODEL AND BUILD INTELLIGENT SYSTEMS BY SIMULATING THE PROCESSES THAT OCCUR IN NATURE. The cooperation of specialists with Distributed knowledge is examined, in the context of knowledge-based support for collaboration among different engineering departments in carrying out large design tasks. It is concluded that1each specialist department has its own private justification language, but interacts with another department using a common shared language.2there may be little shared knowledge, and the shared language may involve an abstracted subset of the private languages of the collaborators.3collaborative reasoning can be limited because of•expression problems, not being able to ask the right questions.•rules of interaction and protocol, arising from legalistic procedures or from proof strategy.•performance problems, not having sufficient resources, due to the complexity of resolving the distribution. A simple model for collaborative reasoning is proposed that defines a collaboration strategy as a dialogue game.
