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A week later, you have been asked to develop a solver for a single - player non - deterministic game called chumpkins - with -
A week later, you have been asked to develop a solver for a singleplayer nondeterministic game
called chumpkinswithdice. This time, you have a trick up your sleeve, and your heuristic
evalx now returns the exact value of a state under optimal play. However, now the gremlin
has changed the search algorithm but hasnt told you Every time the search algorithm runs
it also rolls a dice and chooses with equal probability to either add to every evaluation
value take the square of every evaluation value leave the evaluation values
unchanged. The search algorithm always outputs a policy that uses just one of these rules for
play on a given game tree. You test the algorithm empirically by running it repeatedly on many
game trees of your choosing! and averaging values of each policy that the gremlins algorithm
returns but with your evalx function. You compare the average values of the gremlins policies
to the true values computed by your evaluation function at each root state. When do you get
policies that play optimally and why?
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