Category: 3. Problem Solving

We have studied the strategies which can reason either in forward or backward, but a mixture of the two directions is appropriate for solving a complex and large problem. Such a mixed strategy, make it possible that first to solve the major part of a problem and then go back and solve the small problems… Continue reading Means-Ends Analysis in AI

Hill climbing algorithm is a local search algorithm which continuously moves in the direction of increasing elevation/value to find the peak of the mountain or best solution to the problem. It terminates when it reaches a peak value where no neighbor has a higher value. Hill climbing algorithm is a technique which is used for… Continue reading Hill Climbing Algorithm in AI

So far we have talked about the uninformed search algorithms which looked through search space for all possible solutions of the problem without having any additional knowledge about search space. But informed search algorithm contains an array of knowledge such as how far we are from the goal, path cost, how to reach to goal… Continue reading Informed Search Algorithms

Uninformed search is a class of general-purpose search algorithms which operates in brute force-way. Uninformed search algorithms do not have additional information about state or search space other than how to traverse the tree, so it is also called blind search. Following are the various types of uninformed search algorithms: Breadth-first Search Depth-first Search Depth-limited… Continue reading Uninformed Search Algorithms

Search algorithms are one of the most important areas of Artificial Intelligence. This topic will explain all about the search algorithms in AI. Problem-solving agents: In Artificial Intelligence, Search techniques are universal problem-solving methods. Rational agents or Problem-solving agents in AI mostly used these search strategies or algorithms to solve a specific problem and provide the best result. Problem-solving… Continue reading Search Algorithms in AI