Heuristic
TOPIC: HEURISTIC SEARCH
OVERVIEW OF HEURISTIC SEARCH
Search has been vital to artificial intelligence from the very beginning as a core technique in problem solving. Current developments in search such as pattern databases and search with efficient use of external memory and parallel processing units on main boards and graphics cards are still open problems. Heuristic search as a problem solving tool is demonstrated in applications for puzzle solving, game playing, constraint satisfaction and machine learning. Artificial intelligence (AI) research seeks to enable computers and machines to mimic human intelligence and sensory processing ability, and models human behavior with computers to improve our understanding of intelligence. The many branches of AI research include machine learning, inference, cognition, knowledge representation, problem solving, case-based reasoning, natural language understanding, speech recognition, computer vision, and artificial neural networks.
A key technique developed in the study of artificial intelligence is to specify a problem as a set of states, some of which are solutions, and then search for solution states. For example, in chess, each move creates a new state. If a computer searched the states resulting from all possible sequences of moves, it could identify those that win the game. However, the number of states associated with many problems (such as the possible number of moves needed to win a chess game) is so vast that exhaustively searching them is impractical. The search process can be improved through the use of heuristics—rules that are specific to a given problem and can therefore help guide the search. For example, a chess heuristic might indicate that when a move results in checkmate, there is no point in examining alternate moves.
In computer science, a heuristic is a technique designed to solve a problem that ignores whether the solution can be proven to be correct, but which usually produces a good solution or
OVERVIEW OF HEURISTIC SEARCH
Search has been vital to artificial intelligence from the very beginning as a core technique in problem solving. Current developments in search such as pattern databases and search with efficient use of external memory and parallel processing units on main boards and graphics cards are still open problems. Heuristic search as a problem solving tool is demonstrated in applications for puzzle solving, game playing, constraint satisfaction and machine learning. Artificial intelligence (AI) research seeks to enable computers and machines to mimic human intelligence and sensory processing ability, and models human behavior with computers to improve our understanding of intelligence. The many branches of AI research include machine learning, inference, cognition, knowledge representation, problem solving, case-based reasoning, natural language understanding, speech recognition, computer vision, and artificial neural networks.
A key technique developed in the study of artificial intelligence is to specify a problem as a set of states, some of which are solutions, and then search for solution states. For example, in chess, each move creates a new state. If a computer searched the states resulting from all possible sequences of moves, it could identify those that win the game. However, the number of states associated with many problems (such as the possible number of moves needed to win a chess game) is so vast that exhaustively searching them is impractical. The search process can be improved through the use of heuristics—rules that are specific to a given problem and can therefore help guide the search. For example, a chess heuristic might indicate that when a move results in checkmate, there is no point in examining alternate moves.
In computer science, a heuristic is a technique designed to solve a problem that ignores whether the solution can be proven to be correct, but which usually produces a good solution or
References: Daniel Kahneman, Amos Tversky and Paul Slovic, eds. (1982) Judgment under Uncertainty: Heuristics & Biases. Cambridge, UK, Cambridge University Press ISBN 0-521-28414-7 Gerd Gigerenzer, Peter M Epstein, S., Pacini, R., Denes-Raj, V. & Heier, H. (1996). Individual differences in intuitive-experiential and analytical-rational thinking styles. Journal of Personality and Social Psychology, 71, 390-405. Newell, A. & Simon, H. A. (1976). Computer science as empirical inquiry: symbols and search. Comm. Of the ACM. 19, 113-126. Dechter, R. & Pearl, J. (1985). Generalized Best-First Search Strategies and the Optimality of A*. Journal of the Association for Computing Machinery, 32, 505-536. Koenig, S. (2004). A Comparison of Fast Search Methods for Real-Time Situated Agents. Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2 (pp. 862-871). Washington, DC: IEEE Computer Society. Pearl, J. (1984). Heuristics: Intelligent Search Strategies for Computer Problem Solving. Reading, MA: Addison-Wesley.