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

3. 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

4. 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

5. 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

6. Paula Matuszek, CSC 8520, Fall 2005. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt 6 A vacuum-cleaner agent Percept sequenceAction [A,Clean]Right [A, Dirty]Suck [B, Clean]Left [B, Dirty]Suck [A, Clean],[A, Clean]Right [A, Clean],[A, Dirty]Suck ……

7. 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.

8. 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.

9. 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)

10. 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

11. 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

12. 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)

13. 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)

14. 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

15. 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

16. 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

17. 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

18. 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.

19. 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.

20. 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 clocka clockdriving Fully observable Deterministic Episodic Static Discrete Single agent

21. 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 clocka clockdriving Fully observableYesYesNo DeterministicStrategicStrategicNo Episodic NoNoNo Static SemiYes No DiscreteYes YesNo Single agentNoNoNo 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

22. 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

23. 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

24. 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

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

26. 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.

27. 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.

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

29. 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

30. 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

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

32. 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.

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

34. 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.

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

36. 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

37. Solving problems by searching Chapter 3

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

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

40. 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

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

42. 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

43. 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?

44. 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?

45. 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 #7 Solution?

46. 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 #7 Solution? [Right, if dirt then Suck]

47. 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: 1.initial state e.g., "at Arad" 2.actions or successor function S(x) = set of action–state pairs e.g., S(Arad) = {, … } 3.goal test, can be explicit, e.g., x = "at Bucharest" implicit, e.g., Checkmate(x) 4.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

48. 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

49. 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?

50. 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

51. Paula Matuszek, CSC 8520, Fall 2005. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt 51 Example: The 8-puzzle states? actions? goal test? path cost?

52. Paula Matuszek, CSC 8520, Fall 2005. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt 52 Example: The 8-puzzle states? locations of tiles actions? move blank left, right, up, down goal test? = goal state (given) path cost? 1 per move [Note: optimal solution of n-Puzzle family is NP-hard]

53. Paula Matuszek, CSC 8520, Fall 2005. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt 53 Example: robotic assembly states? actions? goal test? path cost?

54. Paula Matuszek, CSC 8520, Fall 2005. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt 54 Example: robotic assembly states?: real-valued coordinates of robot joint angles parts of the object to be assembled actions?: continuous motions of robot joints goal test?: complete assembly path cost?: time to execute

55. Paula Matuszek, CSC 8520, Fall 2005. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt 55 Tree search algorithms Basic idea: –offline, simulated exploration of state space by generating successors of already-explored states (a.k.a.~expanding states)

56. Paula Matuszek, CSC 8520, Fall 2005. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt 56 Tree search example

57. Paula Matuszek, CSC 8520, Fall 2005. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt 57 Tree search example

58. Paula Matuszek, CSC 8520, Fall 2005. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt 58 Tree search example

59. Paula Matuszek, CSC 8520, Fall 2005. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt 59 Implementation: general tree search

60. Paula Matuszek, CSC 8520, Fall 2005. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt 60 Implementation: states vs. nodes A state is a (representation of) a physical configuration A node is a data structure constituting part of a search tree includes state, parent node, action, path cost g(x), depth The Expand function creates new nodes, filling in the various fields and using the SuccessorFn of the problem to create the corresponding states.

61. Paula Matuszek, CSC 8520, Fall 2005. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt 61 Search strategies A search strategy is defined by picking the order of node expansion. (e.g., breadth-first, depth-first) Strategies are evaluated along the following dimensions: –completeness: does it always find a solution if one exists? –time complexity: number of nodes generated –space complexity: maximum number of nodes in memory –optimality: does it always find a least-cost solution? Time and space complexity are measured in terms of –b: maximum branching factor of the search tree –d: depth of the least-cost solution –m: maximum depth of the state space (may be infinite)

62. Paula Matuszek, CSC 8520, Fall 2005. Based on aima.eecs.berkeley.edu/slides-ppt/m2-agents.ppt 62 Summary We will view our systems as agents. An agent operates in a world which can be described by its Performance measure, Environment, Actuators, and Sensors. A rational agent chooses actions which maximize its performance measure, given the information it has. Agents range in complexity from simple reflex agents to complex utility-based agents. Problem-solving agents search through a problem or state space for an acceptable solution. The formalization of a good state space is hard, and critical to success. It must abstract the essence of the problem so that –It is easier than the real-world problem. –A solution can be found. –The solution maps back to the real-world problem and solves it.