AI News, Artificial Intelligence/Search/Iterative Improvement/Hill Climbing

Artificial Intelligence/Search/Iterative Improvement/Hill Climbing

Hill climbing is an optimization technique for solving computationally hard problems.

It is best used in problems with “the property that the state description itself contains all the information needed for a solution” (Russell &

Local maxima can be avoided by a variety of methods: Simulated annealing tackles this issue by allowing some steps to be taken which decrease the immediate optimality of the current state.

“Random-restart hill-climbing conducts a series of hill-climbing searches from randomly generated initial states, running each until it halts or makes no discernible progress” (Russell &

This enables comparison of many optimization trials, and finding a most optimal solution thus becomes a question of using sufficient iterations on the data.

To address these problems some researchers have looked at using probability theory and local sampling to direct the restarting of hill-climbing algorithms.

The group of researchers designed “a team of robots [that] must coordinate their actions so as to guarantee location of a skilled evader.” (Gerkey, Thrun, &

Robots executing Parish are therefore “collectively hill-climbing according to local progress gradients, but stochastically make lateral or downward moves to help the system escape from local maxima.” (Gerkey, Thrun, &

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