Problem solving 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. In AI problem solving by search algorithms is quite common technique. In the coming age of AI it will have big impact on the technologies of the robotics and path finding. It is also widely used in travel planning. This chapter contains the different search algorithms of AI used in various applications. Let us look the concepts for visualizing the algorithms.
A search algorithm takes a problem as input and returns the solution in the form of an action sequence. Once the solution is found, the actions it recommends can be carried out. This phase is called as the execution phase. After formulating a goal and problem to solve the agent cells a search procedure to solve it. A problem can be defined by 5 components.
a) The initial state: The state from which agent will start.
b) The goal state: The state to be finally reached.
c) The current state: The state at which the agent is present after starting from the initial state.
d) Successor function: It is the description of possible actions and their outcomes.
e) Path cost: It is a function that assigns a numeric cost to each path.
DIFFERENT TYPES OF SEARCHING
the searching algorithms can be various types. When any type of searching is performed, there may some information about the searching or mayn’t be. Also it is possible that the searching procedure may depend upon any constraints or rules. However, generally searching can be classified into two types i.e. uninformed searching and informed searching. Also some other classifications of these searches are given below in the figure .
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