1. Solving Problems by Searching AIMA Chapter 3 Fall 2006 Messiah College Dr. Gene Chase

2. Problem-solving agents A type of goal-based agent Decide what to do by “finding sequences of actions that lead to desirable states.” (=search). problem Search algorithm Solution execution

3. Goals “A set of world states…in which the goal is satisfied.” Actions cause transitions between states. Agent has to find out which actions will get it to a goal state.

4. States & Actions…

5. Goal Formulation Based on current state Select desirable outcome states “…agent may wish to decide on some other factors that affect the desirability of different ways of achieving the goal” Fig. 2.14 utility based agent Fig. 2.15 performance standard

6. Problem Formulation Follows goal formulation “the process of deciding what actions and states to consider.” (p. 60, preliminary definition)

7. Environment types, p 41-42 Observable vs. inaccessible Deterministic vs. stochastic Episodic vs. sequential Static vs. dynamic Discrete vs. continuous Single agent vs. multiple agent

8. Adaptive Agents http://www.ai.mit.edu/research/proj ects/projects.shtml#adaptive http://www.ai.mit.edu/research/proj ects/projects.shtml#adaptive S IMPLE -P ROBLEM -S OLVING -A GENT only adapts when it has completed a goal.

9. Uninformed Search Strategies Means: No heuristic function Breadth first Uniform cost search Depth first Depth-limited Iterative-deepening Bidirectional

10. Problem with blind searches Breadth first: time & space Uniform cost search: time & space Depth first: time & not optimal Depth-limited: not optimal Iterative-deepening: optimal & complete, therefore best Bidirectional: better if can work backwards from goal

11. Acknowledgement Dr. Eugene Rohrbaugh created some of the slides in this presentation for the Fall 2001 Artificial Intelligence course at Messiah College.