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Searching is the most important tools in the study of Artificial Intelligence. Searching algorithms provide the initial concept of building an artificial intelligence-based system.

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Searching is the most important tools in the study of Artificial Intelligence. Searching algorithms
provide the initial concept of building an artificial intelligence-based system. Searching
algorithms are divided into two kinds:
- Uniformed or also known as blind search
- Informed or also known as heuristic search. assisted with the search process information that
can improve search efficiency.
Another approach of Searching algorithms are:
- Any path searching: finding the path to the destination can not pay attention to optimality.
- Optimal Searching: find a path toward a goal that the most optimal

The search process or search method using a support device. Typically use a tree diagram or
graph. Tree constructed from the nodes and links that are connected and there are no loops or
cycles. Nodes are also termed as Vertice and links as edges in the graph modeling uses. Nodes
denoted by circles and links represented by lines. For example, a tree is as follows:

           link/                       A





A tree has a root node as the start / initial / start. Each node except the root has a single parent
(called the direct ancestor). More generally, an ancestor node is a node that can be achieved over
time by heading to the parent node. Each node except the terminal / leaf has one or more children
(direct Descendants) / more generally, an Descendants node is a node that can be achieved
repeatedly on the way to a children node.
. A graph is also constructed from a collection of nodes connected by links, but the condition of a
loop or cycle is allowed. A node can have many parents. There are two kinds of graph, namely:

- Directed graph (with directions): the directed graph that has a link.
- Undirected graph (without direction) the graph that has a link can have alternating directions.

Directed graph has a link that can only move in one direction, while Undirected graph has a link
that can move in both directions.
Graph is everywhere, for example, the road network, or computer network. In some cases,
searching will do a search path that can satisfy or fulfill several objectives and constraints. An
example is a path which is achieved with the shortest path, the cost / low cost and most others.




                                         C                        C

                                         A         B              A

           A      B       C                                       A

                                                   C              C

                                         A         B              B

One common approach to menyelesaiakan problem in AI is to do modeling with graph so that
the searching process can run well. To do this approach it is necessary to determine the state,
action and goal test. Next is a connecting state, action and goal into a tree.
Tree is part of a directed graph, the tree has no cycles and every node has a single parent. Cycle
is not good for searching, or something that should be avoided because it is not possible to search
by constantly traveling. Similarly, if found to be the case with the undirected graph is a necessary
adjustment prior to a tree and avoid a cycle or loop.
As an example of the change or conversion into a tree graph. S is assumed to be the initial state
or start doing searches and trying to find a route to state G. Paths can be selected by making
connections between nodes and avoid having to make a cycle and stop if it gets in G. Avoided by
stopping the cycle if it reached the state has ever visited. The process can be described as





                                      A                        B

                          D                C           D               C

                C                 G            C                   G

Of the tree image can be seen that some of the leaf node / terminal is not a goal state that the tree
permit a cycle or loop. So that the process of searching hard enough having to avoid previously
visited node.
One important difference to note is the state with the search node. State is a depiction of a real
world or a model of something that exists. Can be assumed that the same state can be achieved
with many pathways or different action sequences. Search node is a data structure in the
searching algorithm that builds a tree while doing searching. Each node represents a state but not
unique. So that the searching algorithm is to build a tree.

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