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Adversarial search - alpha beta pruning & Probability and Markov…
Adversarial search - alpha beta pruning & Probability and Markov chains
Alpha Beta pruning
Problem with minimax - number of game states to search is exponential in depth of tree
Other strategies to optimise search
Search 1 play deep, record best moves, then 2 play deep etc
Uncertainty
Problem-solving and logical agents keep track of belief state
Make a plan
Logical agent can’t conclude with certainty that these things won’t happen
Rational decision
A rational decision is the “right” thing to do
Diagnosis involves uncertainty
Typical of judgemental domains
Decision theory
Decision theory =
probability theory + utility theory
Markov Chains
are similar to sequences proscribed by FSMs EXCEPT there is probability associated with the next sequence to be followed, rather than a definite input/output causation.
Stochastic process
collection of random variables
indeterminate outcome
represents evolution of system over time
applications - statistical models of real world
web links
https://www.javatpoint.com/mini-max-algorithm-in-ai
https://www.javatpoint.com/ai-adversarial-search