Reasoning is a cognitive task ubiquitous everywhere: diagnosis, planning, scientific theory formation, speech understanding, etc. Unfortunately, solving reasoning problems is still difficult for most advanced machines since it is NP-Complete. The use of artificial intelligence techniques, and especially neural networks, seems to be a promising direction which can solve these problems to a satisfactory level and in reasonable time scales. In this thesis, we distinguish two categories of causal reasoning; namely cause-to-effect and effect-to- cause. Then, we propose algorithms to solve both categories and compare their performance with already existing proposals in the scientific literature.
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Dr. Lotfi received the eng. degree from ENSI, Tunisia, in 1994; and the Ph.D. degree from the Un. of Sherbrooke, QC, Canada, in 2000, with excellent honors; both in computer sciences. He was awarded the CIDA Doctoral fellowship from 1995 to 2000. His areas of expertise include Reasoning, Data Mining Algorithms, and Image Indexing.
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Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Reasoning is a cognitive task ubiquitous everywhere: diagnosis, planning, scientific theory formation, speech understanding, etc. Unfortunately, solving reasoning problems is still difficult for most advanced machines since it is NP-Complete. The use of artificial intelligence techniques, and especially neural networks, seems to be a promising direction which can solve these problems to a satisfactory level and in reasonable time scales. In this thesis, we distinguish two categories of causal reasoning; namely cause-to-effect and effect-to- cause. Then, we propose algorithms to solve both categories and compare their performance with already existing proposals in the scientific literature. 208 pp. Englisch. Seller Inventory # 9783838311302
Seller: moluna, Greven, Germany
Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Ben Romdhane LotfiDr. Lotfi received the eng. degree from ENSI, Tunisia, in 1994 and the Ph.D. degree from the Un. of Sherbrooke, QC, Canada, in 2000, with excellent honors both in computer sciences. He was awarded the CIDA Doctora. Seller Inventory # 5411834
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Seller: preigu, Osnabrück, Germany
Taschenbuch. Condition: Neu. Computational Networks and Competition-Based Models | Solving Complex Causal Interactions | Lotfi Ben Romdhane | Taschenbuch | 208 S. | Englisch | 2010 | LAP LAMBERT Academic Publishing | EAN 9783838311302 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu. Seller Inventory # 101496538
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Reasoning is a cognitive task ubiquitous everywhere: diagnosis, planning, scientific theory formation, speech understanding, etc. Unfortunately, solving reasoning problems is still difficult for most advanced machines since it is NP-Complete. The use of artificial intelligence techniques, and especially neural networks, seems to be a promising direction which can solve these problems to a satisfactory level and in reasonable time scales. In this thesis, we distinguish two categories of causal reasoning; namely cause-to-effect and effect-to- cause. Then, we propose algorithms to solve both categories and compare their performance with already existing proposals in the scientific literature.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 208 pp. Englisch. Seller Inventory # 9783838311302
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Reasoning is a cognitive task ubiquitous everywhere: diagnosis, planning, scientific theory formation, speech understanding, etc. Unfortunately, solving reasoning problems is still difficult for most advanced machines since it is NP-Complete. The use of artificial intelligence techniques, and especially neural networks, seems to be a promising direction which can solve these problems to a satisfactory level and in reasonable time scales. In this thesis, we distinguish two categories of causal reasoning; namely cause-to-effect and effect-to- cause. Then, we propose algorithms to solve both categories and compare their performance with already existing proposals in the scientific literature. Seller Inventory # 9783838311302