1. COMP 4640 Intelligent and Interactive Systems Intelligent Agents Chapter 2

2. Rational Agents Rational Agent: 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.

3. 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)

4. PEAS PEAS: Performance measure, Environment, Actuators, Sensors Must first specify the setting for intelligent agent design Consider, e.g., the task of designing an automated taxi driver:  Performance measure  Environment  Actuators  Sensors

5. PEAS Must first specify the setting for intelligent agent design Consider, e.g., the task of designing an automated taxi driver:  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

6. PEAS Agent: 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)

7. PEAS Agent: 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

8. PEAS Agent: Interactive English tutor Performance measure: Maximize student's score on test Environment: Set of students Actuators: Screen display (exercises, suggestions, corrections) Sensors: Keyboard

9. Structure of Intelligent Agents The objective of AI is the design and application of agent programs that implement mappings between percepts and actions An agent can be viewed as a program that is developed to run on a particular architecture (some computing device). The architecture:  makes percepts available,  runs the agent program,  sends actions to the effectors

10. Types of Agent Programs Intelligent systems are typically composed of a number of intelligent agents. Each agent interacts (directly or indirectly) with one or more aspects of an environment. This type of agent interaction is similar to what we see in sports, business, and other organizations that are composed of a number of different agents with different responsibilities working together for the common good.

11. Environment types There are a number of different agent programs; however, many can be classified as one of the following: Agent Environments Fully vs. Partially Observable (Accessible vs. inaccessible) Deterministic vs. Stochastic (non-deterministic) Episodic vs. Sequential (non-episodic) Static vs. dynamic Discrete vs. continuous

12. 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.

13. 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.

14. Environment types Chess with Chess without Taxi drivinga clock Fully observableYesYesNo DeterministicStrategicStrategicNo Episodic NoNoNo Static SemiYes No DiscreteYes YesNo Single agentNoNoNo The environment type largely determines the agent design The real world is (of course) partially observable, stochastic, sequential, dynamic, continuous, multi-agent

15. Agent functions and programs An agent is completely specified by the agent function mapping percept sequences to actions One agent function (or a small equivalence class) is rational Aim: find a way to implement the rational agent function concisely

16. Table-lookup agent \input{algorithms/table-agent-algorithm} Drawbacks:  Huge table  Take a long time to build the table  No autonomy  Even with learning, need a long time to learn the table entries

17. Agent types Four basic types in order of increasing generality: Simple reflex agents Model-based reflex agents Goal-based agents Utility-based agents

18. Simple reflex agents

19. COMP-4640 Intelligent & Interactive Systems Simple Reflex Agent I Performance Measure: Get to the bowl Percept Sequence: (x,y) coordinates Agent’s Knowledge of the Environment: No Knowledge Set of Actions: 0 1 2 7 c 3 6 5 4 Rule Base R01: If at(5,0)  action(2) R02: If at(6,1)  action(2) R03: If at(7,2)  action(2) R04: If at(8,3)  action(1) R05: If at(8,4)  action(1) R06: If at(8,5)  action(1) R07: If at(8,6)  action(0) R08: If at(7,7)  action(0) R09: If at(6,8)  action(0) R10: If at(5,9)  action(0)

20. COMP-4640 Intelligent & Interactive Systems Simple Reflex Agent II Performance Measure: Get to the bowl Percept Sequence: (x,y) coordinates Agent’s Knowledge of the Environment: No Knowledge Set of Actions: 0 1 2 7 c 3 6 5 4, MTB (Move Towards Bowl)

21. COMP-4640 Intelligent & Interactive Systems Simple Reflex Agent II Rule Base R01: If MTB(x)  clear(x)  action(x)  remove(lastMove(y))  assert(lastMove(x)) R02: If MTB(x)   clear(x)  assert(escape_phase) R03: If MTB(x)  clear(x)  escape_phase  action(x)  remove(lastMove(y))  remove(escape_phase)  assert(lastMove(x)) R04: If escape_phase   lastMove(5)  clear(1)  action(1)  retract(lastMove(_))  assert(lastMove(1)) R05: If escape_phase   lastMove(7)  clear(3)  action(3)  retract(lastMove(_))  assert(lastMove(3)) R06: If escape_phase   lastMove(1)  clear(5)  action(5)  retract(lastMove(_))  assert(lastMove(5)) R07: If escape_phase   lastMove(3)  clear(7)  action(7)  retract(lastMove(_))  assert(lastMove(7))

22. COMP-4640 Intelligent & Interactive Systems Simple Reflex Agent II Rule Base R01: If MTB(x)  clear(x)  action(x)  remove(lastMove(y))  assert(lastMove(x)) R02: If MTB(x)   clear(x)  assert(escape_phase) R03: If MTB(x)  clear(x)  escape_phase  action(x)  remove(lastMove(y))  remove(escape_phase)  assert(lastMove(x)) R04: If escape_phase   lastMove(5)  clear(1)  action(1)  retract(lastMove(_))  assert(lastMove(1)) R05: If escape_phase   lastMove(7)  clear(3)  action(3)  retract(lastMove(_))  assert(lastMove(3)) R06: If escape_phase   lastMove(1)  clear(5)  action(5)  retract(lastMove(_))  assert(lastMove(5)) R07: If escape_phase   lastMove(3)  clear(7)  action(7)  retract(lastMove(_))  assert(lastMove(7))

23. Model-based reflex agents

24. Goal-based agents

25. COMP-4640 Intelligent & Interactive Systems Goal-Based Agent Performance Measure: Get to the Agent’s Knowledge bowl Percept Sequence: (x,y) coordinates Agent’s Knowledge of the Environment: No Knowledge Set of Actions: 0 1 2 7 c 3 6 5 4, GeneratePath Rule Base R01: If  path_ok  GeneratePath  assert(path_ok) R02: If path_ok  getMove(x,y,a)  clear(a)  action(a) R03: If path_ok  getMove(x,y,a)   clear(a)  retract(path_ok)  retract(getMove(c,d,e))

26. Utility-based agents

27. COMP-4640 Intelligent & Interactive Systems Utility-Based Agent Performance Measure: Get to the bowl Using the Shortest Path Percept Sequence: (x,y) coordinates Agent’s Knowledge of the Environment: No Knowledge Set of Actions: 0 1 2 7 c 3 6 5 4, GeneratePath Rule Base R01: If  path_ok  GeneratePath(UF)  assert(path_ok) R02: If path_ok  getMove(x,y,a)  clear(a)  action(a) R03: If path_ok  getMove(x,y,a)   clear(a)  retract(path_ok)  retract(getMove(c,d,e))

28. Learning agents

29. Learning Agent Examples Interactive Animated Pedagogical Agents Microsoft agent Why people hate clippy? Intelligent tutoring systems Agents on Websites with characters to guide users mySimon.com buy.com extempo.com ananova.com Ikea.comInteractive Animated Pedagogical Agents Why people hate clippy? Intelligent tutoring systems mySimon.com buy.com extempo.com ananova.com Ikea.com

30. Collaborative Agents 0

31. 5000 Collaborative Agents

32. 10000 Collaborative Agents

33. 0

34. 500 Collaborative Agents

35. 3000 Collaborative Agents

36. Collaborative Agent Examples Collaborative Agents CMU Advanced Mechantronics Lab

37. More Agent Examples Virtual Humans & Conversational Agents Conversational Agent Applications Virtual Patient Audio Virtual Pediatric Patient Video Video Just Talk Sample Animations Video