Computational Networks and Competition-Based Models

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.

Autorentext

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.