Problem solving, particularly in artificial intelligence, may be characterized as a systematic search through a range of possible actions in order to reach some predefined goal or solution. Problem-solving methods divide into special purpose and general purpose. A special-purpose method is tailor-made for a particular problem and often exploits very specific features of the situation in which the problem is embedded. In contrast, a general-purpose method is applicable to a wide variety of problems. One general-purpose technique used in AI is means-end analysis—a step-by-step, or incremental, reduction of the difference between the current state and the final goal. The program selects actions from a list of means—in the case of a simple robot this might consist of PICKUP, PUTDOWN, MOVEFORWARD, MOVEBACK, MOVELEFT, and MOVERIGHT—until the goal is reached.
Artificial Intelligence- in contrast with conventional problem solving techniques such as operation research, management science, or decision support systems-employ basically blind and heuristic approaches.
Blind Search: A blind search (also called an uninformed search) is a search that has no information about its domain. The only thing that a blind search can do is distinguish a non-goal state from a goal state.
Breadth-First Search: Using a breadth-first strategy we expand the root level first and then we expand all those nodes (i.e. those at level 1) before we expand any nodes at level 2.
Depth-First Search: Depth first search explores one branch of a tree before it starts to explore another branch. It can be implemented by adding newly expanded nodes at the front of the queue.
Heuristic Search:
A Heuristic is an operationally-effective nugget of information on how to direct search in a problem space. Heuristics are only approximately correct. Their purpose is to minimize search on average.Heuristic search is much faster and cheaper than a blind search. The results are considered good enough, and in the case of quantitative analysis they are very close to optimal solutions.
Characteristics:
Learns more by acting than analyzing the situation and places more emphasis on feedback
Uses trial and error and spontaneous action
Uses common sense, intuition and feelings
Looks for highly visible situational differences that vary with time
