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# CS188 Introduction to Artificial Intelligence - Project 4: Inference in Bayes Nets

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# Next look at computeExploreValue. This function computes the expected value of exploring all of the hidden cells, and then making a decision. We've provided a helper method, getExplorationProbsAndOutcomes, which returns a list of future observations Pacman might make, and the probability of each. To calculate the value of the extra information Pacman will gain, you can use the following formula: E[value of exploration]=∑p(evidence)maxactionsE[value of action|evidence]$E\left[\text{value of exploration}\right]=\sum p\left(\text{evidence}\right)\underset{\text{actions}}{max}E\left[\text{value of action}|\text{evidence}\right]$ Note that E[value of action|evidence]$E\left[\text{value of action}|\text{evidence}\right]$ is exactly the quantity computed by computeEnterVals, so to compute the value of exploration, you can call computeEnterValues again with the hypothetical evidence provided by getExplorationProbsAndOutcomes. Grading: To test and debug your code, run python autograder.py -q q8 Hint: After exploring, Pacman will again need to compute the expected value of entering the left and right houses. Fortunately, you've already written a function to do this! Your solution to computeExploreValue can rely on your solution to computeEnterValues to determine the value of future observations.

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