# Artificial Intelligence – A Modern Approach Chapter 3

Problem Solving Agent

decides what to do by finding sequences of actions that lead to desirable states

Uniformed

given no information about the problem other than its definition

Informed

search algorithms that have some idea of where to look for solutions

Goal Formulation

based on the current situation and the agent’s performance measure, is the first step in problem solving.

Problem Formulation

which actions and states to consider given a goal

Search Algorithm

takes a problem as input and returns a solution in the form of an action sequence (formulate, search, execute)

Initial State

the state that the agent start in

Successor Function

a description of possible actions available to the agent. The successor function, given a state, returns

State Space

The initial state and the successor function implicitly define the state space (all possible states from the initial state)

Goal Test

test which determines if a given state is the goal state

Path Cost

assigns a numeric cost to each path

Step Cost

the step cost of taking action a to go from state x to state y is denoted by c(x, a ,y)

Abstraction

process of removing detail from a representation is called abstraction

n-puzzle

object is to reach a specified goal state, such as the one shown on the right of the figure

Route Finding Problem

is defined in terms of specified locations and transitions along links between them

Touring Problem

visit every node (The Traveling Salesperson problem)

Measuring Problem Solving Performance

completeness, optimality, time complexity, space complexity

Branching Factor

Maximum number of successors to any node

Breadth-First Search

root node is expandd first, then all the successors of the root node are expanded next and so on. Expands the shallowest unexpanded node

Uniform Cost Search

Expands the node n with the lowest path cost (if all step costs are equal, this is identical to a breadth-first search)

Depth First Search

always expands the deepest node until the node has no successor

Depth-Limited Search

same as depth first search but limit the maximum depth allowed (not useful unless the maximum possible depth can be determined)

Iterative Deepening Depth First Search

depth first search but when all the nodes have been expanded and no solution found the depth limit is increased.

Bidirectional Search

two simultaneous searches, one from the initial state and from the goal state

Sensorless Problems

if the agent has no sensors at all, then it could be in one of several possible initial states and each action might therefore lead to one of several possible successor states

Contingency Problems

if the environment is partially observable or if actions are uncertain, then the agent’s percepts provide new information after each action. Each possible percept defines a contigency that must be planned for.

Exploration Problems

when the states and actions of the environment are unknown the agent must act to discover them.