Intelligent Agent: Maximizing Performance in Different Environments

1. Chapter 2 Intelligent Agent

2. 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 • any given instant can depend on the entire percept sequence observed to date, but not on anything it hasn't perceived.

3. 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 • The agent function is an abstract mathematical description; the agent program is a specify implementation, running within some physical system.

4. Vacuum-cleaner world • Percepts: location and state of the environment, e.g., [A,Dirty], [A,Clean], [B,Dirty] • Actions: Left, Right, Suck, NoOp • Simple agent function: • if the current square is dirty, then suck; otherwise, move to the other square.

5. what makes an agent good or bad, intelligent or stupid?

6. Rational agents • Rational agent: is one that does the right thing • Performance measure: • that evaluates any given sequence of environment states. • An objective criterion for success of an agent's behavior, e.g., • Robot driver? • Chess-playing program? • Spam email classifier? • RationalAgent: selects actions that is expected to maximize its performance measure, • given percept sequence • given agent’s built-in knowledge • sidepoint: how to maximize expected future performance, given only historical data

7. Rationality • What is rational at any given time depends on four things: • The performance measure that defines the criterion of success. • The agent's prior knowledge of the environment. • The actions that the agent can perform. • The agent's percept sequence to date. • 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.

8. Rational agents • Performance measure:An objective criterion for success of an agent's behavior, e.g., • Robot driver? • Chess-playing program? • Spam email classifier? • RationalAgent: selects actions that is expected to maximize its performance measure, • given percept sequence • given agent’s built-in knowledge • sidepoint: how to maximize expected future performance, given only historical data

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 percepts & experience (with ability to learn and adapt) without depending solely on built-in knowledge99

10. Task Environment • Before we design an intelligent agent, we must specify its “task environment”: PEAS: • Performance measure • Environment • Actuators • Sensors

11. PEAS • Example: Agent = robot driver in DARPA Challenge • Performance measure: • Time to complete course • Environment: • Roads, other traffic, obstacles • Actuators: • Steering wheel, accelerator, brake, signal, horn • Sensors: • Optical cameras, lasers, sonar, accelerometer, speedometer, GPS, odometer, engine sensors,

12. PEAS • Example: 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. Agent type Percepts Actions Goals Environment Medical diagnosis system Symptoms, findings, patient's answers Questions, tests, treatments Healthy patients, minimize costs Patient, hospital Satellite image analysis system Pixels of varying intensity, color Print a categorization of scene Correct categorization Images from orbiting satellite Part-picking robot Pixels of varying intensity Pick up parts and sort into bins Place parts in correct bins Conveyor belts with parts Refinery controller Temperature, pressure readings Open, close valves; adjust temperature Maximize purity, yield, safety Refinery Interactive English tutor Typed words Print exercises, suggestions, corrections Maximize student's score on test Set of students PEAS

14. 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. • Fully observable environments are convenient because the agent need not maintain any internal state to keep track of the world. • E.g.: Playing soccer : Partial observable, Perform high jump : Full observable. • Single agent (vs. multi-agent): • An agent operating by itself in an environment. Does the other agent interfere with my performance measure? • e,.g: crossword puzzle : Single agent Playing chess : mult-iagent (agent may be competitive or cooperation)

15. Environment types • 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 • Deterministic environments can appear stochastic to an agent (e.g., when only partially observable) • E.g: shopping for IT book online: deterministic, • Stochastic:generally implies that uncertainty about outcomes is quantified in terms of probabilities E.g: performing a high Jump :Stochastic

16. Episodic (vs. sequential): • An agent’s action is divided into atomic episodes. Decisions do not depend on previous decisions/actions. • Episodic : the next episode does not depend on the actions taken in previous episodes. • E.g.: an agent that has to spot defective parts on an assembly line bases each decision on the current part, regardless of previous decisions; • sequential the current decision could affect all future decisions e.g.: Chess and taxi driving are sequential

17. Environment types • Static (vs. dynamic): • Static : The environment is unchanged while an agent is deliberating. • E.g.: crossword puzzle :static • Dynamic: agent need to keep looking at the world while it is deciding on an action and The environment is changed while an agent is deliberating. Dynamic environment continuously asking agent what want to do. • E.g.: Taxi driving : Dynamic • 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 discrete set of distinct, clearly defined percepts and actions. • How we representorabstract ormodel the world Need to worry about time Dynamic Need to observe while deliberating

19. What is the environment for the DARPA Challenge? • Agent = robotic vehicle • Environment = 130-mile route through desert • Observable? • Deterministic? • Episodic? • Static? • Discrete? • Agents?

20. 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 • Difference between agent program and agent function: • Agent program: takes the current percept as input because nothing more is available from the environment; • Agent function, which takes the entire percept history. • Aim: find a way to implement the rational agent function concisely

21. Examples of how the agent function can be implemented • Table-driven agent • Simple reflex agent • Reflex agent with internal state • Agent with explicit goals • Utility-based agent More sophisticated

22. Table-lookup agent • 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 • Agent programs type: • Simple reflex agents • Model-based reflex agents • Goal-based agents • Utility-based agents

23. Table-driven agent

24. Simple reflex agents

25. Simple reflex agent… • Table lookup of condition-action pairs defining all possible condition-action rules necessary to interact in an environment • e.g. if car-in-front-is-breaking then initiate breaking • select precepts based on the current percept • ignoring the rest of the precept history • The rules are like the form “if … then …” • efficient but have narrow range of applicability Because knowledge sometimes cannot be stated explicitly • Work only • if the environment is fully observable • Problems • Table is still too big to generate and to store (e.g. taxi) • Takes long time to build the table • No knowledge of non-perceptual parts of the current state • Not adaptive to changes in the environment; requires entire table to be updated if changes occur • Looping: Can’t make actions conditional

26. A Simple Reflex Agent in Nature percepts (size, motion) RULES: (1) If small moving object, then activate SNAP (2) If large moving object, then activate AVOID and inhibit SNAP ELSE (not moving) then NOOP needed for completeness Action: SNAP or AVOID or NOOP

27. Model-based reflex agents

28. Model-Based Reflex Agents • Most effective way to keep track of the part of the world it can’t see now. • Maintain some internal state that depends on percept history and thereby reflects at least some of the unobserved aspects of the current state (e.g. using some type of variable). • For the world that is partially observable • the agent has to keep track of an internal state • That depends on the percept history • Reflecting some of the unobserved aspects • E.g., driving a car and changing lane • Requiring two types of knowledge • How the world evolves independently of the agent • How the agent’s actions affect the world

29. Goal-based agents

30. Current state of the environment is always not enough • The goal is another issue to achieve • Judgment of rationality / correctness • Actions chosen  goals, based on • the current state • the current percept • Conclusion • Goal-based agents are less efficient • but more flexible • Agent  Different goals  different tasks • Search and planning • two other sub-fields in AI • to find out the action sequences to achieve its goal

31. Utility-based agents

32. Goals alone are not enough • to generate high-quality behavior • E.g. meals in Canteen, good or not ? • Many action sequences  the goals • some are better and some worse • If goal means success, • then utility means the degree of success (how successful it is) • State A has higher utility • If state A is more preferred than others • Utility is therefore a function • that maps a state onto a real number • the degree of success • Utility has several advantages: • When there are conflicting goals, • Only some of the goals but not all can be achieved • utility describes the appropriate trade-off • When there are several goals • None of them are achieved certainly • utility provides a way for the decision-making

33. Learning Agents • After an agent is programmed, can it work immediately? • No, it still need teaching • In AI, • Once an agent is done • We teach it by giving it a set of examples • Test it by using another set of examples • We then say the agent learns • A learning agent • Four conceptual components • Learning element • Making improvement • Performance element • Selecting external actions • Critic • Tells the Learning element how well the agent is doing with respect to fixed performance standard. (Feedback from user or examples, good or not?) • Problem generator • Suggest actions that will lead to new and informative experiences.

34. Learning agents

35. Agent types • Five basic types in order of increasing generality: • Table Driven agent • Simple reflex agents • Model-based reflex agents • Goal-based agents • Problem-solving agents • Utility-based agents • Can distinguish between different goals • Learning agents

36. Problem-Solving Agents • Intelligent agents can solve problems by searching a state-space • State-space Model • the agent’s model of the world • usually a set of discrete states • e.g., in driving, the states in the model could be towns/cities • Goal State(s) • a goal is defined as a desirable state for an agent • there may be many states which satisfy the goal test • e.g., drive to a town with a ski-resort • or just one state which satisfies the goal • e.g., drive to Mammoth • Operators (actions, successor function) • operators are legal actions which the agent can take to move from one state to another

37. Initial Simplifying Assumptions • Environment is static • no changes in environment while problem is being solved • Environment is observable • Environment and actions are discrete • (typically assumed, but we will see some exceptions) • Environment is deterministic

38. Example: Traveling in Romania • On holiday in Romania; currently in Arad • Flight leaves tomorrow from Bucharest • Formulate goal: • be in Bucharest • Formulate problem: • states: various cities • actions/operators: drive between cities • Find solution • By searching through states to find a goal • sequence of cities, e.g., Arad, Sibiu, Fagaras, Bucharest • Execute states that lead to a solution

39. Example: Traveling in Romania

40. State-Space Problem Formulation A problem is defined by four items: • initial statee.g., "at Arad“ • actions or successor function S(x) = set of action–state pairs e.g., S(Arad) = {<Arad  Zerind, Zerind>, … } 3. goal test (or set of goal states) e.g., x = "at Bucharest”, 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

41. Example: Formulating the Navigation Problem • Set of States • individual cities • e.g., Irvine, SF, Las Vegas, Reno, Boise, Phoenix, Denver • Operators • freeway routes from one city to another • e.g., Irvine to SF via 5, SF to Seattle, etc • Start State • current city where we are, Irvine • Goal States • set of cities we would like to be in • e.g., cities which are closer than Irvine • Solution • a specific goal city, e.g., Boise • a sequence of operators which get us there, • e.g., Irvine to SF via 5, SF to Reno via 80, etc

42. Abstraction • Definition of Abstraction: Process of removing irrelevant detail to create an abstract representation: ``high-level”, ignores irrelevant details • Navigation Example: how do we define states and operators? • First step is to abstract “the big picture” • i.e., solve a map problem • nodes = cities, links = freeways/roads (a high-level description) • this description is an abstraction of the real problem • Can later worry about details like freeway onramps, refueling, etc • Abstraction is critical for automated problem solving • must create an approximate, simplified, model of the world for the computer to deal with: real-world is too detailed to model exactly • good abstractions retain all important details

43. The State-Space Graph • Graphs: • nodes, arcs, directed arcs, paths • Search graphs: • States are nodes • operators are directed arcs • solution is a path from start S to goal G • Problem formulation: • Give an abstract description of states, operators, initial state and goal state. • Problem solving: • Generate a part of the search space that contains a solution

44. C B A D F E The Traveling Salesperson Problem • Find the shortest tour that visits all cities without visiting any city twice and return to starting point. • State: sequence of cities visited • S0 = A • G = a complete tour

45. Example: 8-queens problem

46. State-Space problem formulation • states?-any arrangement of n<=8 queens -or arrangements of n<=8 queens in leftmost n columns, 1 per column, such that no queen attacks any other. • initial state? no queens on the board • actions?-add queen to any empty square -or add queen to leftmost emptysquare such that it is not attacked by other queens. • goal test?8 queens on the board, none attacked. • path cost? 1 per move

47. Example: Robot Assembly • States • Initial state • Actions • Goal test • Path Cost

48. Example: Robot Assembly • States: configuration of robot (angles, positions) and object parts • Initial state: any configuration of robot and object parts • Actions: continuous motion of robot joints • Goal test: object assembled? • Path Cost: time-taken or number of actions

49. Learning a spam email classifier • States • Initial state • Actions • Goal test • Path Cost

50. Learning a spam email classifier • States: settings of the parameters in our model • Initial state: random parameter settings • Actions: moving in parameter space • Goal test: optimal accuracy on the training data • Path Cost: time taken to find optimal parameters (Note: this is an optimization problem – many machine learning problems can be cast as optimization)