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I'm not sure I'm convinced by the thesis Prolog was killed by association with a Japanese project that was a failure. I thought Prolog died a natural death, for several reasons:

Firstly, Prolog is it was great at the deductive, expert system type of AI, but in 90's, a new generation of AI based on statistics (firstly, fuzzy logic, and then proper statistical reasoning, particularly Bayesian approaches) appeared and showed that truth and failure just don't cut it any more.

Secondly, I don't know if anyone has done the research, but I wouldn't be surprised if Blub programmers are just better at thinking imperatively; Prolog tends to kind of warp the brain away from that, which would put off Blub programmers, but it doesn't provide the kind of killer power that makes expert programmers want to use it either.

Thirdly, Prolog is hard to optimise well, and hard to predict the performance characteristics of. In the 80's and 90's, where apps were getting written in C, this was killer.



"Firstly, Prolog is it was great at the deductive, expert system type of AI, but in 90's, a new generation of AI based on statistics (firstly, fuzzy logic, and then proper statistical reasoning, particularly Bayesian approaches) appeared and showed that truth and failure just don't cut it any more."

There are attempts to reconcile the two approaches, such as Markov Logic.

http://en.wikipedia.org/wiki/Markov_logic_network

I believe Statistical Relational Learning is the more general term for this idea.

http://en.wikipedia.org/wiki/Statistical_relational_learning




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