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AIMA gives detailed information about the working of algorithms in AI. The book's chapters span from classical AI topics like searching algorithms and first-order logic, propositional logic and probabilistic reasoning to advanced topics such as multi-agent systems, constraint satisfaction problems, optimization problems, artificial neural networks, deep learning, reinforcement learning, and ...
The regulation of artificial intelligence is the development of public sector policies and laws for promoting and regulating AI; it is therefore related to the broader regulation of algorithms. [322] The regulatory and policy landscape for AI is an emerging issue in jurisdictions globally. [ 323 ]
Automated planning and scheduling, sometimes denoted as simply AI planning, [1] is a branch of artificial intelligence that concerns the realization of strategies or action sequences, typically for execution by intelligent agents, autonomous robots and unmanned vehicles.
Intelligent control is a class of control techniques that use various artificial intelligence computing approaches like neural networks, Bayesian probability, fuzzy logic, machine learning, reinforcement learning, evolutionary computation and genetic algorithms. [1]
He has also authored multiple books on artificial intelligence and its potential promise and peril. In December 2012 Kurzweil was hired by Google in a full-time director of engineering position to "work on new projects involving machine learning and language processing". [ 54 ]
The view of the first of the above three points goes back to Zadeh, the founder of the fuzzy set theory, who differentiated machine intelligence into hard and soft computing techniques, which are used in artificial intelligence on the one hand and computational intelligence on the other.
Allen Newell's research group in artificial intelligence had been working on production systems for some time, but Forgy's implementation, based on his Rete algorithm, was especially efficient, sufficiently so that it was possible to scale up to larger problems involving hundreds or thousands of rules.
A particle swarm searching for the global minimum of a function. In computational science, particle swarm optimization (PSO) [1] is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality.