CS 8520: Artificial Intelligence

1. CS 8520: Artificial Intelligence Intelligent Agents and Search Paula Matuszek Fall, 2005 Slides based on Hwee Tou Ng, aima.eecs.berkeley.edu/slides-ppt, which are in turn based on Russell, aima.eecs.berkeley.edu/slides-pdf.

2. Outline • Agents and environments • Rationality • PEAS (Performance measure, Environment, Actuators, Sensors) • Environment types • Agent types Paula Matuszek, CSC 8520, Fall 2005. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

3. Agents • An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators • 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 Paula Matuszek, CSC 8520, Fall 2005. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

4. Agents and environments • The agent function maps from percept histories to actions: [f: P* A] • The agent program runs on the physical architecture to produce f • agent = architecture + program Paula Matuszek, CSC 8520, Fall 2005. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

5. Vacuum-cleaner world • Percepts: location and contents, e.g., [A,Dirty] • Actions: Left, Right, Suck, NoOp Paula Matuszek, CSC 8520, Fall 2005. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

6. A vacuum-cleaner agent Paula Matuszek, CSC 8520, Fall 2005. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

7. 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. Paula Matuszek, CSC 8520, Fall 2005. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

8. 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. Paula Matuszek, CSC 8520, Fall 2005. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

9. 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) Paula Matuszek, CSC 8520, Fall 2005. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

10. PEAS: Description of an Agent's World • Performance measure: How do we assess whether we are doing the right thing? • Environment,: What is the world we are in? • Actuators: How do we affect the world we are in? • Sensors: How do we perceive the world we are in? • Together these specify the setting for intelligent agent design Paula Matuszek, CSC 8520, Fall 2005. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

11. PEAS: Taxi Driver • 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 Paula Matuszek, CSC 8520, Fall 2005. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

12. 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) Paula Matuszek, CSC 8520, Fall 2005. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

13. 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) Paula Matuszek, CSC 8520, Fall 2005. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

14. 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 Paula Matuszek, CSC 8520, Fall 2005. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

15. 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 Paula Matuszek, CSC 8520, Fall 2005. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

16. 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 Paula Matuszek, CSC 8520, Fall 2005. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

17. 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 Paula Matuszek, CSC 8520, Fall 2005. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

18. 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. Paula Matuszek, CSC 8520, Fall 2005. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

19. 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. Paula Matuszek, CSC 8520, Fall 2005. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

20. Environment types Chess with Chess without Taxi a clock a clock driving Fully observable Deterministic Episodic Static Discrete Single agent Paula Matuszek, CSC 8520, Fall 2005. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

21. Environment types Chess with Chess w/out Taxi a clock a clock driving Fully observable Yes Yes No Deterministic Strategic Strategic No Episodic No No No Static Semi Yes No Discrete Yes Yes No Single agent No No No • The environment type largely determines the agent design • The simplest environment is fully observable, deterministic, episodic, static, discrete and single-agent. • The real world is (of course) partially observable, stochastic, sequential, dynamic, continuous, multi-agent Paula Matuszek, CSC 8520, Fall 2005. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

22. 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 Paula Matuszek, CSC 8520, Fall 2005. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

23. Table-lookup agent Function TABLE-DRIVEN_AGENT(percept) returns an action append percept to the end of percepts action  LOOKUP(percepts, table) return action • Drawbacks: • Huge table • Take a long time to build the table • No autonomy • Even with learning, need a long time for table entries Paula Matuszek, CSC 8520, Fall 2005. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

24. Agent types • Four basic types in order of increasing generality: • Simple reflex agents • Model-based reflex agents • Goal-based agents • Utility-based agents Paula Matuszek, CSC 8520, Fall 2005. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

25. Simple reflex agents Paula Matuszek, CSC 8520, Fall 2005. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

26. Simple reflex Vacuum Agent 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 • Observe the world, choose an action, implement action, done. • Problems if environment is not fully-observable. • Depending on performance metric, may be inefficient. Paula Matuszek, CSC 8520, Fall 2005. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

27. Model-Based Agents • Suppose moving has a cost? • If a square stays clean once it is clean, then this algorithm will be extremely inefficient. • A very simple improvement would be • Record when we have cleaned a square • Don’t go back once we have cleaned both. • We have built a very simple model. Paula Matuszek, CSC 8520, Fall 2005. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

28. Reflex Agents with State Paula Matuszek, CSC 8520, Fall 2005. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

29. Reflex Agents with State • More complex agent with model: a square can get dirty again. Function REFLEX_VACUUM_AGENT_WITH_STATE ([location, status]) returns an action. last-cleaned-A and last-cleaned-B initially declared = 100. Increment last-cleaned-A and last-cleaned-B. if status == Dirty then return Suck if location == A then set last-cleaned-A to 0 if last-cleaned-B > 3 then return right else no-op else set last-cleaned-B to 0 if last-cleaned-A > 3 then return left else no-op • The value we check last-cleaned against could be modified. • Could track how often we find dirt to compute value Paula Matuszek, CSC 8520, Fall 2005. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

30. Model-Based = Reflex Plus State • Maintain an internal model of the state of the environment • Over time update state using world knowledge • How the world changes • How actions affect the world • Agent can operate more efficiently • More effective than a simple reflex agent for partially observable environments Paula Matuszek, CSC 8520, Fall 2005. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

31. Goal-based agents Paula Matuszek, CSC 8520, Fall 2005. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

32. Goal-Based Agent • Agent has some information about desirable situations • Needed when a single action cannot reach desired outcome • Therefore performance measure needs to take into account "the future". • Typical model for search and planning. Paula Matuszek, CSC 8520, Fall 2005. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

33. Utility-based agents Paula Matuszek, CSC 8520, Fall 2005. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

34. Utility-Based Agents • Possibly more than one goal, or more than one way to reach it • Some are better, more desirable than others • There is a utility function which captures this notion of "better". • Utility function maps a state or sequence of states onto a metric. Paula Matuszek, CSC 8520, Fall 2005. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

35. Learning agents Paula Matuszek, CSC 8520, Fall 2005. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

36. Learning Agents • All agents have methods for selection actions. • Learning agents can modify these methods. • Performance element: any of the previously described agents • Learning element: makes changes to actions • Critic: evaluates actions, gives feedback to learning element • Problem generator: suggests actions Paula Matuszek, CSC 8520, Fall 2005. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

37. Solving problems by searching Chapter 3

38. Outline • Problem-solving agents • Problem formulation • Example problems Paula Matuszek, CSC 8520, Fall 2005. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

39. Problem-solving agents Paula Matuszek, CSC 8520, Fall 2005. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

40. Example: Romania • On holiday in Romania; currently in Arad. • Flight leaves tomorrow from Bucharest • Formulate goal: • be in Bucharest • Formulate problem: • states: various cities • actions: drive between cities • Find solution: • sequence of cities, e.g., Arad, Sibiu, Fagaras, Bucharest Paula Matuszek, CSC 8520, Fall 2005. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

41. Example: Romania Paula Matuszek, CSC 8520, Fall 2005. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

42. Problem types • Deterministic, fully observable single-state problem • Agent knows exactly which state it will be in; solution is a sequence • Non-observable sensorless problem (conformant problem) • Agent may have no idea where it is; solution is a sequence • Nondeterministic and/or partially observable contingency problem • percepts provide new information about current state • often interleave} search, execution • Unknown state space exploration problem Paula Matuszek, CSC 8520, Fall 2005. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

43. Example: vacuum world • Single-state, start in #5. Solution? Paula Matuszek, CSC 8520, Fall 2005. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

44. Example: vacuum world • Single-state, start in #5. Solution?[Right, Suck] • Sensorless, start in {1,2,3,4,5,6,7,8}e.g., Right goes to {2,4,6,8} Solution? Paula Matuszek, CSC 8520, Fall 2005. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

45. Example: vacuum world • Sensorless, start in {1,2,3,4,5,6,7,8}e.g., Right goes to {2,4,6,8} Solution?[Right,Suck,Left,Suck] • Contingency • Nondeterministic: Suck may dirty a clean carpet • Partially observable: location, dirt at current location. • Percept: [L, Clean], i.e., start in #5 or #7Solution? Paula Matuszek, CSC 8520, Fall 2005. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

46. Example: vacuum world • Sensorless, start in {1,2,3,4,5,6,7,8}e.g., Right goes to {2,4,6,8} Solution?[Right,Suck,Left,Suck] • Contingency • Nondeterministic: Suck may dirty a clean carpet • Partially observable: location, dirt at current location. • Percept: [L, Clean], i.e., start in #5 or #7Solution?[Right, if dirt then Suck] Paula Matuszek, CSC 8520, Fall 2005. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

47. Single-state problem formulation A problem is defined by four items: • initial state e.g., "at Arad" • actions or successor function S(x) = set of action–state pairs • e.g., S(Arad) = {<Arad  Zerind, Zerind>, … } • goal test, can be • explicit, e.g., x = "at Bucharest" • implicit, e.g., Checkmate(x) • path cost (additive) • e.g., sum of distances, number of actions executed, etc. • c(x,a,y) is the step cost, assumed to be ≥ 0 • A solution is a sequence of actions leading from the initial state to a goal state Paula Matuszek, CSC 8520, Fall 2005. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

48. Selecting a state space • Real world is absurdly complex  state space must be abstracted for problem solving • (Abstract) state = set of real states • (Abstract) action = complex combination of real actions • e.g., "Arad  Zerind" represents a complex set of possible routes, detours, rest stops, etc. • For guaranteed realizability, any real state "in Arad“ must get to some real state "in Zerind" • (Abstract) solution = • set of real paths that are solutions in the real world • Each abstract action should be "easier" than the original problem Paula Matuszek, CSC 8520, Fall 2005. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

49. Vacuum world state space graph • States? Actions? Goal Test? Path Cost? Paula Matuszek, CSC 8520, Fall 2005. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt

50. Vacuum world state space graph • states? integer dirt and robot location • actions? Left, Right, Suck • goal test? no dirt at all locations • path cost? 1 per action Paula Matuszek, CSC 8520, Fall 2005. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt