Artificial Intelligence: Can Computers Think? |
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Page vi
... Assumptions Learning Formula- tion as a Multistage Decision Process Expected Gain Expected Gain Value of In- formation . Further Assumptions Functional Equations • Discus- sion of Assumptions • Computational Feasibility Levels of ...
... Assumptions Learning Formula- tion as a Multistage Decision Process Expected Gain Expected Gain Value of In- formation . Further Assumptions Functional Equations • Discus- sion of Assumptions • Computational Feasibility Levels of ...
Page 81
... assumptions that are made are the following : Here is an unknown machine and we are told that one lever has a fixed ... assumption that is made LEARNING 81.
... assumptions that are made are the following : Here is an unknown machine and we are told that one lever has a fixed ... assumption that is made LEARNING 81.
Page 92
... assumptions are required . Different assumptions will yield different results . Consequently , it is essential to make the assumptions explicit . The great value of the computer is that it forces us to make the assumptions explicit ...
... assumptions are required . Different assumptions will yield different results . Consequently , it is essential to make the assumptions explicit . The great value of the computer is that it forces us to make the assumptions explicit ...
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actual algorithm analog computer answer applications approach approximate policies arithmetic artificial intelligence assumptions average outcome behavior Bellman BIBLIOGRAPHY AND COMMENTS bility chapter chess COMMENTS Section complex concept consider Control Processes criterion function dealer determine deterministic device difficulty digital computer discussed draw a card drug Dynamic Programming effect example expected gain experience experimentation fast storage feasible fifteen puzzle foregoing functional equation fuzzy sets human idea important instinct interesting large number learning levels logic machine mathe mathematical analysis mathematical problems mathematical theory mathematician matical mean method minimize minimum Monte Carlo Method multistage decision process observe operations optimal play optimal policy particular path patient pattern recognition player possible precise proba probability distribution probability theory procedure puter puzzles question reasonable simple situations solve space stochastic approximation stochastic process structure talk tion transformation uncertainty unknown probability York Zadeh