Intelligent Agents. Chapter 2 ICS 171, Fall 2009

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1 Intelligent Agents Chapter 2 ICS 171, Fall 2009

2 Discussion \\Why is the Chinese room argument impractical and how would we have to change the Turing test so that it is not subject to this criticism? Godel s theorem assures us that humans will always be superior to machines. A robot/agent can never be aware of itself (be self-conscious).

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 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 Vacuum-cleaner world Percepts: location and state of the environment, e.g., [A,Dirty], [B,Clean] Actions: Left, Right, Suck, NoOp

6 Rational agents Rational Agent: For each possible percept sequence, a rational agent should select an action that is expected to maximize its performance measure, based on the evidence provided by the percept sequence and whatever built-in knowledge the agent has. 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.

7 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 build-in knowledge

8 Task Environment Before we design an intelligent agent, we must specify its task environment : PEAS: Performance measure Environment Actuators Sensors

9 PEAS Example: Agent = 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

10 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)

11 PEAS Example: 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

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): An agent s action is divided into atomic episodes. Decisions do not depend on previous decisions/actions.

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. How do we represent or abstract or model the world? Single agent (vs. multi-agent): An agent operating by itself in an environment. Does the other agent interfere with my performance measure?

14 task environm. observable determ./ stochastic episodic/ sequential static/ dynamic discrete/ continuous agents crossword puzzle chess with clock fully determ. sequential static discrete single fully strategic sequential semi discrete multi taxi driving medical diagnosis image analysis partpicking robot refinery controller interact. Eng. tutor partial stochastic sequential dynamic continuous multi partial stochastic sequential dynamic continuous single fully determ. episodic semi continuous single partial stochastic episodic dynamic continuous single partial stochastic sequential dynamic continuous single partial stochastic sequential dynamic discrete multi

15 Agent types Five basic types in order of increasing generality: Table Driven agents Simple reflex agents Model-based reflex agents Goal-based agents Utility-based agents Learning agent

16 table lookup for entire history Table Driven Agent. current state of decision process

17 Simple reflex agents It uses just condition-action rules 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

18 Simple reflex agents

19 Simple reflex agents NO MEMORY Fails if environment is partially observable example: vacuum cleaner world

20 Model-based Reflex Agents 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

21 Model-based Reflex Agents The agent is with memory

22 description of current world state Model-based reflex agents Model the state of the world by: modeling how the world chances how it s actions change the world This can work even with partial information It s is unclear what to do without a clear goal

23 Goal-based agents 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

24 Goal-based agents 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

25 Goal-based agents Goals provide reason to prefer one action over the other. We need to predict the future: we need to plan & search

Many action sequences the goals some are better and some worse If

26 Utility-based agents 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)

27 Utility-based agents Some solutions to goal states are better than others. Which one is best is given by a utility function. Which combination of goals is preferred?

28 Utility-based agents it is said 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

29 Utility-based agents (3) 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

30 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

31 Learning Agents Four conceptual components Learning element Making improvement Performance element Selecting external actions Critic Feedback from user or examples, good or not? Problem generator New derived situation, try new solution or method

32 Learning agents How does an agent improve over time? By monitoring it s performance and suggesting better modeling, new action rules, etc. Evaluates current world state changes action rules suggests explorations old agent = model world and decide on actions to be taken

33 True or False?

Intelligent Agents. Outline. Agents. Agents and environments

Intelligent Agents. Outline. Agents. Agents and environments Outline Intelligent Agents Chapter 2 Source: AI: A Modern Approach, 2 nd Ed Stuart Russell and Peter Norvig Agents and environments Rationality (Performance measure, Environment, Actuators, Sensors) Environment