Generic techniques in artificial intelligence, Basic Computer Science

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Generic Techniques Developed:

In  the  pursuit  of  solutions  to  many   problems  in  the  above  categories,  serval specific  techniques have sprung up which have been shown to be helpful for solving a range of problems (generally within the general problem category). These techniques are established sufficient now to have a name and provide at least a partial characterization of Artificial Intelligence. The following list is not intended to be complete, but rather to introduce some mechanism you will learn later in the course. Note that some of these overlap with the general techniques above.

        1) Forward/backward chaining (reasoning)

        2) Resolution theorem proving (reasoning)

        3) Alpha-Beta pruning (games)

        4)Case-based reasoning (expert systems)

        5)Knowledge elicitation (expert systems)

        6)Neural networks (learning)

        7)Bayesian methods (learning)

        8)Proof planning (reasoning)

        9)Constraint satisfaction (reasoning)
        10)Case-based reasoning (expert systems)

        11)Davis-Putnam method (reasoning)

12)Alpha-Beta pruning (games)

        13)Knowledge elicitation (expert systems)

        14)Minim ax search (games)

        '15)Neural networks (learning)

         16)Bayesian methods (learning)

         17) Explanation based (learning)

         18) Reinforcement (learning)

         19) Genetic programming (learning)

         20) Strips (planning)

         21) N-grams (NLP)

         22) Parsing (NLP)

         23) Behavior based (robotics)

         24) Cell decomposition (robotics)

         25) Genetic algorithms (learning)


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