CSCE 580 Artificial Intelligence Ch.2: Intelligent Agents

1. CSCE 580Artificial IntelligenceCh.2: Intelligent Agents Fall 2008 Marco Valtorta mgv@cse.sc.edu

2. Acknowledgment • The slides are based on the textbook [AIMA] and other sources, including other fine textbooks • The other textbooks I considered are: • David Poole, Alan Mackworth, and Randy Goebel. Computational Intelligence: A Logical Approach. Oxford, 1998 • A second edition (by Poole and Mackworth) is under development. Dr. Poole allowed us to use a draft of it in this course • Ivan Bratko. Prolog Programming for Artificial Intelligence, Third Edition. Addison-Wesley, 2001 • The fourth edition is under development • George F. Luger. Artificial Intelligence: Structures and Strategies for Complex Problem Solving, Sixth Edition. Addison-Welsey, 2009

3. Agents • An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators • Actuators are sometimes called effectors • Human agent: • eyes, ears, and other organs for sensors; • hands, legs, mouth, and other body parts for actuators • Robotic agent: • cameras and infrared range finders for sensors; • various motors for actuators

4. Agents and Environments The agentfunction maps from percept histories to actions: [f: P* A] • The agentprogram runs on the physical architecture to produce f • agent = architecture + program

5. The Role of Representation • Choosing a representation involves balancing conflicting objectives • Different tasks require different representations • Representations should be expressive (epistemologically adequate) and efficient (heuristically adequate)

6. The Vacuum-Cleaner World • Environment: square A and B • Percepts: [location and content] e.g. [A, Dirty] • Actions: left, right, suck, and no-op

7. The Vacuum-Cleaner World

8. The Vacuum-Cleaner World function REFLEX-VACUUM-AGENT ([location, status]) return an action if status == Dirty then return Suck else if location == A then return Right else if location == B then return Left What is the right function? Can it be implemented in a small agent program?

9. Rational Agents • An agent should strive to "do the right thing," based on what it can perceive and the actions it can perform. The right action is the one that will cause the agent to be most successful • Performance measure: An objective criterion for success of an agent's behavior • E.g., performance measure of a vacuum-cleaner agent could be amount of dirt cleaned up, amount of time taken, amount of electricity consumed, amount of noise generated, etc.

10. Rational Agents • RationalAgent: For each possible percept sequence, a rational agent should select an action that is expected to maximize its performance measure, given the evidence provided by the percept sequence and whatever built-in knowledge the agent has.

11. Rational Agents • Rationality is distinct from omniscience (all-knowing with infinite knowledge) • 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. The Concept of Rationality • A rational agent is one that does the right thing • Every entry in the table is filled out correctly • What is the right thing? • Approximation: the most succesful agent • Measure of success? • Performance measure should be objective • E.g. the amount of dirt cleaned within a certain time • E.g. how clean the floor is • … • Better to design performance measures according to what is wanted in the environment instead of how the agents should behave.

13. Rationality • What is rational at a given time depends on four things: • Performance measure • Prior environment knowledge • Actions • Percept sequence to date (sensors) • DEF: A rational agent chooses whichever action maximizes the expected value of the performance measure given the percept sequence to date and prior environment knowledge

14. Rationality • Rationality  omniscience • An omniscient agent knows the actual outcome of its actions. • Rationality perfection • Rationality maximizes expected performance, while perfection maximizes actual performance.

15. Rationality • The proposed definition requires: • Information gathering/exploration • To maximize future rewards • Learn from percepts • Extending prior knowledge • Dung beetle and sphex wasp plans • Agent autonomy • Compensate for incorrect prior knowledge

16. PEAS • Task environment • a problem for which a rational agents is the solution • specified by the PEAS description: • Performance measure, Environment, Actuators, Sensors • Must first specify the setting for intelligent agent design • Consider, e.g., the task of designing an automated taxi: • Performance measure • Environment • Actuators • Sensors

17. PEAS for Automated Taxi • Performance measure: Safe, fast, legal, comfortable trip, maximize profits • Environment: Roads, other traffic, pedestrians, customers • Actuators: Steering wheel, accelerator, brake, signal, horn • Sensors: Cameras, sonar, speedometer, GPS, odometer, engine sensors, keyboard

18. PEAS for a Medical Diagnosis System • Performance measure: Healthy patient, minimize costs, lawsuits • Environment: Patient, hospital, staff • Actuators: Screen display (questions, tests, diagnoses, treatments, referrals) • Sensors: Keyboard (entry of symptoms, findings, patient's answers)

19. PEAS for a Part-Picking Robot • Performance measure: Percentage of parts in correct bins • Environment: Conveyor belt with parts, bins • Actuators: Jointed arm and hand • Sensors: Camera, joint angle sensors

20. PEAS for an Interactive English Tutor • Performance measure: Maximize student's score on test • Environment: Set of students • Actuators: Screen display (exercises, suggestions, corrections) • Sensors: Keyboard

21. Environment Types • 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" (each episode consists of the agent perceiving and then performing a single action), and the choice of action in each episode depends only on the episode itself

22. Environment Types • 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

23. Environment Types Fully vs. partially observable: an environment is full observable when the sensors can detect all aspects that are relevant to the choice of action.

24. Environment Types Fully vs. partially observable: an environment is full observable when the sensors can detect all aspects that are relevant to the choice of action.

25. Environment Types Deterministic vs. stochastic: if the next environment state is completely determined by the current state the executed action then the environment is deterministic.

26. Environment Types Deterministic vs. stochastic: if the next environment state is completely determined by the current state the executed action then the environment is deterministic.

27. Environment Types Episodic vs. sequential: In an episodic environment the agent’s experience can be divided into atomic steps where the agents perceives and then performs A single action. The choice of action depends only on the episode itself

28. Environment Types Episodic vs. sequential: In an episodic environment the agent’s experience can be divided into atomic steps where the agents perceives and then performs A single action. The choice of action depends only on the episode itself

29. Environment Types Static vs. dynamic: If the environment can change while the agent is choosing an action, the environment is dynamic. Semi-dynamic if the agent’s performance changes even when the environment remains the same.

30. Environment Types Static vs. dynamic: If the environment can change while the agent is choosing an action, the environment is dynamic. Semi-dynamic if the agent’s performance changes even when the environment remains the same.

31. Environment Types Discrete vs. continuous: This distinction can be applied to the state of the environment, the way time is handled and to the percepts/actions of the agent.

32. Environment Types Discrete vs. continuous: This distinction can be applied to the state of the environment, the way time is handled and to the percepts/actions of the agent.

33. Environment Types Single vs. multi-agent: Does the environment contain other agents who are also maximizing some performance measure that depends on the current agent’s actions?

34. Environment Types Single vs. multi-agent: Does the environment contain other agents who are also maximizing some performance measure that depends on the current agent’s actions?

35. Environment Types • The simplest environment is • Fully observable, deterministic, episodic, static, discrete and single-agent. • Most real situations are: • Partially observable, stochastic, sequential, dynamic, continuous and multi-agent.

36. Agent Types • Four basic types in order of increasing generality: • Simple reflex agents • Model-based reflex agents • Goal-based agents • Utility-based agents • All these can be turned into learning agents

37. Agent Types Function TABLE-DRIVEN_AGENT(percept) returns an action static: percepts, a sequence initially empty table, a table of actions, indexed by percept sequence append percept to the end of percepts action LOOKUP(percepts, table) returnaction This approach is doomed to failure

38. Simple Reflex Agents • Select action on the basis of only the current percept • E.g. the vacuum-agent • Large reduction in possible percept/action situations (example on next page) • Implemented through condition-action rules • If dirty then suck

39. The Vacuum-Cleaner World function REFLEX-VACUUM-AGENT ([location, status]) return an action if status == Dirty then return Suck else if location == A then return Right else if location == B then return Left Reduction from 4T to 4 entries

40. Simple Reflex Agent Function function SIMPLE-REFLEX-AGENT(percept) returns an action static: rules, a set of condition-action rules state INTERPRET-INPUT(percept) rule RULE-MATCH(state, rule) action RULE-ACTION[rule] return action Will only work if the environment is fully observable • E.g., determination of braking in cars without centrally mounted brake lights

41. Model-Based Reflex Agents • To tackle partially observable environments. • Maintain internal state • Over time update state using world knowledge • How does the world change. • How do actions affect world.  Model of World

42. Model-Based Agent Function function REFLEX-AGENT-WITH-STATE(percept) returns an action static: rules, a set of condition-action rules state, a description of the current world state action, the most recent action. state UPDATE-STATE(state, action, percept) rule RULE-MATCH(state, rule) action RULE-ACTION[rule] return action

43. Model-Based Goal-Based agents • The agent needs a goal to know which situations are desirable • Things become difficult when long sequences of actions are required to find the goal • Typically investigated in search and planning research • Major difference: future is taken into account • Is more flexible since knowledge is represented explicitly and can be manipulated

44. Utility-Based Agents • Certain goals can be reached in different ways • Some are better, have a higher utility • Utility function maps a (sequence of) state(s) onto a real number. • Improves on goals: • Selecting between conflicting goals • Select appropriately between several goals based on likelihood of success

45. Learning Agents • All previous agent programs describe methods for selecting actions • Yet the origin of these programs is not provided • Learning mechanisms can be used to perform this task • Teach them instead of instructing them • Advantage is the robustness of the program toward initially unknown environments

46. Learning Agents • Learning element: introduce improvements in performance element. • Critic provides feedback on agents performance based on fixed performance standard • Performance element: selecting actions based on percepts • Corresponds to the previous agent programs • Problem generator: suggests actions that will lead to new and informative experiences • Exploration