CSC 242 Lecture Notes - Lecture 5: Branching Factor, Simulated Annealing, Maxima And Minima
Document Summary
Systematic search: enumerates paths from initial state, records what alternatives have been explored at each point in the path. Steps taken does not matter, only final result obtained (e. g. 8-queens problem) Select an applicable action, apply, and update the state. Number of applicable actions is the branching factor (defines problem complexity) Local search: evaluates and modifies a small number of current states, does not record history of search (paths, explored set, etc ) Here, heuristic describes how bad or good options are. e. g. heuristic can tell the current cost of the configuration (i. e. Here, heuristic describes how bad or good options are. e. g. heuristic can tell the current cost of the configuration (i. e. how many conflicts we have or so). Therefore, our algorithm would aim at reducing the heuristic"s value. Being greedy, the algorithm can find itself stuck in a non-final state (14% success rate) All other search methods are built on this.