agent: senses the environment and acts upon the environment
rational agent selects actions that maximize its (expected) utility
State: something that the environment can be
State space: all possible state
Actions: finite set of executable actions from a given state to another. This forms a path
Successors: the state that results from taking a given action to a given state
Cost: resources related to a given action
Initial state: the state to start the search
Goal state: the state to stop the search
Solutions: the set of paths that leads from the initial to the final goal state
Node
Parent
Child
Root: nodes with no parent
leaf: nodes with parent
Depth ($d$, $m$ is the max depth)
number of node between the root and the current node +1
Branching factor ($b$)
average number of children nodes that each node have
Node expansion
generate a node in the successor