Computational Networks and Competition-Based Models
Solving Complex Causal Interactions
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.
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.