How To Create SEARCH And Algorithm Using Java Programming

Search:

Early AI research emphasized the optimization of search algorithms. This approach made a lot of

 sense because many AI tasks can be solved effectively by defining state spaces and using search

algorithms to define and explore search trees in this state space. Search programs were frequently

made tractable by using heuristics to limit areas of search in these search trees. This use of heuristics

converts intractable problems to solvable problems by compromising the quality of solutions; this

trade off of less computational complexity for less than optimal solutions has become a standard

design pattern for AI programming. We will see in this chapter that we trade off memory for faster

computation time and better results; often, by storing extra data we can make search time faster, and

make future searches in the same search space even more efficient.


What are the limitations of search? Early on, search applied to problems like checkers and chess

misled early researchers into underestimating the extreme difficulty of writing software that performs

 tasks in domains that require general world knowledge or deal with complex and changing

environments. These types of problems usually require the understanding and then the

implementation of domain specific knowledge. In this chapter, we will use three search problem

domains for studying search algorithms: pat

In this chapter, we will use three search problem domains for studying search algorithms: path

finding in a maze, path finding in a graph, and alpha-beta search in the games tic-tac-toe and chess.