Learning heuristics from experience

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 Learning heuristics from experience is a technique for automatically improving the performance of search algorithms by using data from previous search instances. The basic idea is to learn a heuristic function that accurately predicts the cost of reaching the goal state from a given state, based on the features of the state and the actions taken to reach it.

There are several approaches to learning heuristics from experience, including:

  1. Machine learning: This involves training a machine learning model, such as a decision tree, neural network, or support vector machine, on a dataset of state-action pairs and their associated costs. The learned model can then be used to predict the cost of reaching the goal state from new states.

  2. Reinforcement learning: This involves using a reinforcement learning algorithm, such as Q-learning or SARSA, to learn a policy that maximizes the expected cumulative reward over time. The policy can then be used to guide search by selecting actions that are likely to lead to the goal state.

  3. Evolutionary algorithms: This involves using an evolutionary algorithm, such as genetic programming or genetic algorithms, to evolve a heuristic function that performs well on a set of training instances. The evolved heuristic can then be used to guide search on new instances.

The main advantage of learning heuristics from experience is that it can lead to significant improvements in search performance, especially for problems with large state spaces or complex action spaces. However, it requires a large amount of training data and can be computationally expensive. It is also important to ensure that the learned heuristic is admissible and consistent, to guarantee optimality of the search algorithm

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