MCTS is extended to games with hidden information and un- certainty through the introduction of the Information Set MCTS (ISMCTS) family of algorithms
he ICARUS framework generalises MCTS enhancements in terms of information capture (from MCTS simulations) and reuse (to improve MCTS tree and simulation policies).
There are several algorithms which extend minimax search to games with stochastic outcomes. Firstly there is expectimax search [78] where the value of a chance node is the expected value of a randomly chosen child (i.e. the sum of the values of its children weighted by the probabilities of the corresponding chance outcomes)
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