1. Intelligent Agents Chapter 2

2. CIS 391 - Intro to AI - Fall 2008 2 Outline  Brief Review  Agents and environments  Rationality  PEAS (Performance measure, Environment, Actuators, Sensors)  Environment types  Agent types

3. CIS 391 - Intro to AI - Fall 2008 3  Rational behavior: doing whatever is expected to maximize goal achievement, given the available information  This course is about effective programming techniques for designing rational agents Review I: AI as Acting Rationally Thinking humanlyThinking rationally Acting humanlyActing rationally

4. CIS 391 - Intro to AI - Fall 2008 4 Review II: Rational agents  An agent is an entity that perceives and acts  Abstractly, an agent is a function from percept histories to actions: [f: P*  A ]  For any given class of environments and tasks, we seek the agent (or class of agents) with the best performance

5. CIS 391 - Intro to AI - Fall 2008 5 Agents  An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators  Human agent: Sensors: eyes, ears,... Actuators: hands, legs, mouth, …  Robotic agent: Sensors: cameras and infrared range finders Actuators: various motors  Agents include humans, robots, softbots, thermostats, …

6. CIS 391 - Intro to AI - Fall 2008 6 Agents and environments  An agent is specified by an agent function f that maps sequences of percepts Y to actions a from a set A : Y={y 0, y 1, …, y t } A={a 0, a 1, …, a k }

7. CIS 391 - Intro to AI - Fall 2008 7 Agent function & program  The agent program runs on the physical architecture to produce f agent = architecture + program  “Easy” solution: table that maps every possible sequence Y to an action a One small problem: exponential in length of Y

8. CIS 391 - Intro to AI - Fall 2008 8 Example: A Vacuum-cleaner agent  Percepts:location and contents, e.g., (A,dirty) (Idealization: locations are discrete)  Actions: move, clean, do nothing: LEFT, RIGHT SUCK NOP AB

9. CIS 391 - Intro to AI - Fall 2008 9 Vacuum-cleaner world: agent function

10. CIS 391 - Intro to AI - Fall 2008 10 Rational agents II  Rational Agent: For each possible percept sequence, a rational agent should select an action that is expected to maximize its performance measure.  Performance measure: An objective criterion for success of an agent's behavior, given the evidence provided by the percept sequence.  A performance measure for a vacuum-cleaner agent might include one or more of: +1 point for each clean square in time T +1 point for clean square, -1 for each move -1000 for more than k dirty squares

11. CIS 391 - Intro to AI - Fall 2008 11 Rationality is not omniscience  Ideal agent: maximizes actual performance, but needs to be omniscient. Usually impossible….. — But consider tic-tac-toe agent… Rationality  Success  Agents can perform actions in order to modify future percepts so as to obtain useful information (information gathering, exploration)  An agent is autonomous if its behavior is determined by its own experience (with ability to learn and adapt)

12. CIS 391 - Intro to AI - Fall 2008 12 Task environment  To design a rational agent we need to specify a task environment a problem specification for which the agent is a solution  PEAS : to specify a task environment P erformance measure E nvironment A ctuators S ensors

13. CIS 391 - Intro to AI - Fall 2008 13 PEAS: Specifying an automated taxi driver P erformance measure: ? E nvironment: ? A ctuators: ? S ensors: ?

14. CIS 391 - Intro to AI - Fall 2008 14 PEAS: Specifying an automated taxi driver P erformance measure: safe, fast, legal, comfortable, maximize profits E nvironment: ? A ctuators: ? S ensors: ?

15. CIS 391 - Intro to AI - Fall 2008 15 PEAS: Specifying an automated taxi driver P erformance measure: safe, fast, legal, comfortable, maximize profits E nvironment: roads, other traffic, pedestrians, customers A ctuators: ? S ensors: ?

16. CIS 391 - Intro to AI - Fall 2008 16 PEAS: Specifying an automated taxi driver P erformance measure: safe, fast, legal, comfortable, maximize profits E nvironment: roads, other traffic, pedestrians, customers A ctuators: steering, accelerator, brake, signal, horn S ensors: ?

17. CIS 391 - Intro to AI - Fall 2008 17 PEAS: Specifying an automated taxi driver P erformance measure: safe, fast, legal, comfortable, maximize profits E nvironment: roads, other traffic, pedestrians, customers A ctuators: steering, accelerator, brake, signal, horn S ensors: cameras, sonar, speedometer, GPS

18. CIS 391 - Intro to AI - Fall 2008 18 PEAS: Another example  Agent : Medical diagnosis system  P erformance measure: Healthy patient, minimize costs, lawsuits  E nvironment: Patient, hospital, staff  A ctuators: Screen display (form including: questions, tests, diagnoses, treatments, referrals)  S ensors: Keyboard (entry of symptoms, findings, patient's answers)

19. CIS 391 - Intro to AI - Fall 2008 19 Environment types: Definitions I  Fully observable (vs. partially observable): An agent's sensors give it access to the complete state of the environment at each point in time.  Deterministic (vs. stochastic): The next state of the environment is completely determined by the current state and the action executed by the agent. (If the environment is deterministic except for the actions of other agents, then the environment is strategic )  Episodic (vs. sequential): The agent's experience is divided into atomic "episodes" during which the agent perceives and then performs a single action, and the choice of action in each episode depends only on the episode itself.

20. CIS 391 - Intro to AI - Fall 2008 20 Environment types: Definitions II  Static (vs. dynamic): The environment is unchanged while an agent is deliberating. The environment is semidynamic if the environment itself does not change with the passage of time but the agent's performance score does.  Discrete (vs. continuous): A limited number of distinct, clearly defined percepts and actions.  Single agent (vs. multiagent): An agent operating by itself in an environment. (See examples in AIM, however I don’t agree with some of the judgments)

21. CIS 391 - Intro to AI - Fall 2008 21 Agent types  Goal of AI: given a PEAS task environment, construct agent function f, design an agent program that implements f on a particular architecture  Four basic agent types in order of increasing generality: Simple reflex Model-based reflex Goal-based Utility-based

22. CIS 391 - Intro to AI - Fall 2008 22 Simple reflex agent function REFLEX_VACUUM_AGENT( percept ) returns an action (location,status) = UPDATE_STATE( percept ) if status = DIRTY then return SUCK; else if location = A then return RIGHT; else if location = B then return LEFT;

23. CIS 391 - Intro to AI - Fall 2008 23 Model-based reflex agents New

24. CIS 391 - Intro to AI - Fall 2008 24 Goal-based agents New

25. CIS 391 - Intro to AI - Fall 2008 25 Utility-based agents New

26. CIS 391 - Intro to AI - Fall 2008 26 Learning agents incorporate others Any other agent!