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CHAPTER 2
OLIVER SCHULTE
SUMMER2011
Intelligent Agents
Outline
Artificial Intelligence a modern approach
2
⚫Agents and environments
⚫Rationality
⚫PEAS (Performance measure, Environment,
Actuators, Sensors)
⚫Environment types
⚫Agent types
Agents
Artificial Intelligence a modern approach
3
• 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
Agents and environments
Artificial Intelligence a modern approach
4
• 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
Vacuum-cleaner world
Artificial Intelligence a modern approach
5
⚫ Percepts: location and contents, e.g., [A,Dirty]
⚫ Actions: Left, Right, Suck, NoOp
⚫ Agent’s function 🡪 look-up table
⚪ For many agents this is a very large table
Demo:
http://www.ai.sri.com/~oreilly/aima3ejava/aima3ejavademos.h
tml
Rational agents
Artificial Intelligence a modern approach
6
• Rationality
– Performance measuring success
– Agents prior knowledge of environment
– Actions that agent can perform
– Agent’s percept sequence to date
• 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.
Examples of Rational Choice
⚫See File: intro-choice.doc
Artificial Intelligence a modern approach
7
Rationality
Artificial Intelligence a modern approach
8
⚫Rational is different from omniscience
⚪ Percepts may not supply all relevant information
⚪ E.g., in card game, don’t know cards of others.
⚫Rational is different from being perfect
⚪ Rationality maximizes expected outcome while perfection
maximizes actual outcome.
Autonomy in Agents
⚫Extremes
⚪ No autonomy – ignores environment/data
⚪ Complete autonomy – must act randomly/no program
⚫Example: baby learning to crawl
⚫Ideal: design agents to have some autonomy
⚪ Possibly become more autonomous with experience
The autonomy of an agent is the extent to which its
behaviour is determined by its own experience,
rather than knowledge of designer.
PEAS
Artificial Intelligence a modern approach
10
• 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: 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
PEAS
Artificial Intelligence a modern approach
11
⚫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
PEAS
Artificial Intelligence a modern approach
12
⚫Agent: Interactive English tutor
⚫Performance measure: Maximize student's score on
test
⚫Environment: Set of students
⚫Actuators: Screen display (exercises, suggestions,
corrections)
⚫Sensors: Keyboard
Environment types
Artificial Intelligence a modern approach
13
• Fully observable (vs. partially observable)
• Deterministic (vs. stochastic)
• Episodic (vs. sequential)
• Static (vs. dynamic)
• Discrete (vs. continuous)
• Single agent (vs. multiagent):
Fully observable (vs. partially observable)
Artificial Intelligence a modern approach
14
⚫Is everything an agent requires to choose its actions
available to it via its sensors? Perfect or Full
information.
⚪ If so, the environment is fully accessible
⚫If not, parts of the environment are inaccessible
⚪ Agent must make informed guesses about world.
⚫In decision theory: perfect information vs. imperfect
information.
Cross
Word
Backgammon Taxi
driver
Part picking robot
Poke
r
Image analysis
Fully Fully Fully
Partially
Partially Partially
Deterministic (vs. stochastic)
Artificial Intelligence a modern approach
15
⚫Does the change in world state
⚪ Depend only on current state and agent’s action?
⚫Non-deterministic environments
⚪ Have aspects beyond the control of the agent
⚪ Utility functions have to guess at changes in world
Cross
Word
Backgammon Taxi
driver
Part picking robot
Poke
r
Image analysis
Cross
Word
Backgammon Taxi
driver
Part
Poke
r
Image analysis
Deterministic Deterministic
Stochastic
Stochastic
Stochastic Stochastic
Episodic (vs. sequential):
Artificial Intelligence a modern approach
16
⚫ Is the choice of current action
⚪ Dependent on previous actions?
⚪ If not, then the environment is episodic
⚫ In non-episodic environments:
⚪ Agent has to plan ahead:
⯍ Current choice will affect future actions
Cross
Word
Backgammon Taxi
driver
Part picking robot
Poke
r
Image analysis
Sequential
Sequential
Sequential
Sequential Episodic Episodic
Static (vs. dynamic):
Artificial Intelligence a modern approach
17
⚫Static environments don’t change
⚪ While the agent is deliberating over what to do
⚫Dynamic environments do change
⚪ So agent should/could consult the world when choosing actions
⚪ Alternatively: anticipate the change during deliberation OR make
decision very fast
⚫ Semidynamic: If the environment itself does not change
with the passage of time but the agent's performance
score does.
Cross
Word
Backgammon Taxi
driver
Part picking robot
Poke
r
Image analysis
Static Static Static Dynamic Dynamic Semi
Another example: off-line route planning vs. on-board navigation system
Discrete (vs. continuous)
Artificial Intelligence a modern approach
18
⚫ A limited number of distinct, clearly defined percepts and
actions vs. a range of values (continuous)
Cross
Word
Backgammon Taxi
driver
Part picking robot
Poke
r
Image analysis
Discrete Discrete Discrete Conti Conti Conti
Single agent (vs. multiagent):
Artificial Intelligence a modern approach
19
⚫ An agent operating by itself in an environment or there are
many agents working together
Cross
Word
Backgammon Taxi
driver
Part picking robot
Poke
r
Image analysis
Single Single Single
Multi
Multi
Multi
Artificial Intelligence a modern approach
Observable Deterministic Static
Episodic Agents
Discrete
Cross
Word
Backgammon
Taxi
driver
Part picking robot
Poke
r
Image analysis
Deterministic
Stochastic
Deterministic
Stochastic
Stochastic
Stochastic
Sequential
Sequential
Sequential
Sequential
Episodic
Episodic
Static
Static
Static
Dynamic
Dynamic
Semi
Discrete
Discrete
Discrete
Conti
Conti
Conti
Single
Single
Single
Multi
Multi
Multi
Summary.
Fully
Fully
Fully
Partially
Partially
Partially
Choice under (Un)certainty
Artificial Intelligence a modern approach
21
Fully
Observable
Deterministic
Certainty:
Search
Uncertainty
no
yes
yes
no
Agent types
Artificial Intelligence a modern approach
22
⚫Four basic types in order of increasing generality:
⚪ Simple reflex agents
⚪ Reflex agents with state/model
⚪ Goal-based agents
⚪ Utility-based agents
⚪ All these can be turned into learning agents
⚪ http://www.ai.sri.com/~oreilly/aima3ejava/aima3ejavad
emos.html
Simple reflex agents
Artificial Intelligence a modern approach
23
Simple reflex agents
Artificial Intelligence a modern approach
24
⚫ Simple but very limited intelligence.
⚫ Action does not depend on percept history, only on current
percept.
⚫ Therefore no memory requirements.
⚫ Infinite loops
⚪ Suppose vacuum cleaner does not observe location. What do you do
given location = clean? Left of A or right on B -> infinite loop.
⚪ Fly buzzing around window or light.
⚪ Possible Solution: Randomize action.
⚪ Thermostat.
⚫ Chess – openings, endings
⚪ Lookup table (not a good idea in general)
⯍ 35100 entries required for the entire game
States: Beyond Reflexes
• Recall the agent function that maps from percept histories
to actions:
[f: P* 🡪 A]
⚫ An agent program can implement an agent function by
maintaining an internal state.
⚫ The internal state can contain information about the state
of the external environment.
⚫ The state depends on the history of percepts and on the
history of actions taken:
[f: P*, A*🡪 S 🡪A] where S is the set of states.
⚫ If each internal state includes all information relevant to
information making, the state space is Markovian.
Artificial Intelligence a modern approach
25
States and Memory: Game Theory
⚫If each state includes the information about the
percepts and actions that led to it, the state space has
perfect recall.
⚫Perfect Information = Perfect Recall + Full
Observability + Deterministic Actions.
Artificial Intelligence a modern approach
26
Model-based reflex agents
Artificial Intelligence a modern approach
27
⚫ Know how world evolves
⚪ Overtaking car gets closer from
behind
⚫ How agents actions affect the
world
⚪ Wheel turned clockwise takes you
right
⚫ Model base agents update their
state
Goal-based agents
Artificial Intelligence a modern approach
28
• knowing state and environment? Enough?
– Taxi can go left, right, straight
• Have a goal
⚪ A destination to get to
⚫Uses knowledge about a goal to guide its actions
⚪ E.g., Search, planning
Goal-based agents
Artificial Intelligence a modern approach
29
• Reflex agent breaks when it sees brake lights. Goal based agent
reasons
– Brake light -> car in front is stopping -> I should stop -> I should use brake
Utility-based agents
Artificial Intelligence a modern approach
30
⚫Goals are not always enough
⚪ Many action sequences get taxi to destination
⚪ Consider other things. How fast, how safe…..
⚫A utility function maps a state onto a real number
which describes the associated degree of
“happiness”, “goodness”, “success”.
⚫Where does the utility measure come from?
⚪ Economics: money.
⚪ Biology: number of offspring.
⚪ Your life?
Utility-based agents
Artificial Intelligence a modern approach
31
Learning agents
Artificial Intelligence a modern approach
32
⚫ Performance element is
what was previously the
whole agent
⚫ Input sensor
⚫ Output action
⚫ Learning element
⚫ Modifies performance
element.
Learning agents
Artificial Intelligence a modern approach
33
⚫ Critic: how the agent is
doing
⚫ Input: checkmate?
⚫ Fixed
⚫ Problem generator
⚫ Tries to solve the problem
differently instead of
optimizing.
⚫ Suggests exploring new
actions -> new problems.
Learning agents(Taxi driver)
⚪ Performance element
⯍ How it currently drives
⚪ Taxi driver Makes quick left turn across 3 lanes
⯍ Critics observe shocking language by passenger and other drivers
and informs bad action
⯍ Learning element tries to modify performance elements for future
⯍ Problem generator suggests experiment out something called
Brakes on different Road conditions
⚪ Exploration vs. Exploitation
⯍ Learning experience can be costly in the short run
⯍ shocking language from other drivers
⯍ Less tip
⯍ Fewer passengers
Artificial Intelligence a modern approach
34
The Big Picture: AI for Model-Based Agents
Artificial Intelligence a modern approach
35
Action
Learning
Knowledge
Logic
Probability
Heuristics
Inference
Planning
Decision Theory
Game Theory
Reinforcement
Learning
Machine Learning
Statistics
The Picture for Reflex-Based Agents
Artificial Intelligence a modern approach
36
Action
Learning
Reinforcement
Learning
• Studied in AI, Cybernetics, Control Theory, Biology,
Psychology.
Discussion Question
⚫Model-based behaviour has a large overhead.
⚫ Our large brains are very expensive from an
evolutionary point of view.
⚫Why would it be worthwhile to base behaviour on a
model rather than “hard-code” it?
⚫For what types of organisms in what type of
environments?
Artificial Intelligence a modern approach
37
Summary
⚫ Agents can be described by their PEAS.
⚫ Environments can be described by several key properties:
64 Environment Types.
⚫ A rational agent maximizes the performance measure for
their PEAS.
⚫ The performance measure depends on the agent
function.
⚫ The agent program implements the agent function.
⚫ 3 main architectures for agent programs.
⚫ In this course we will look at some of the common and
useful combinations of environment/agent architecture.
Artificial Intelligence a modern approach
38

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chapter2.pptx

  • 2. Outline Artificial Intelligence a modern approach 2 ⚫Agents and environments ⚫Rationality ⚫PEAS (Performance measure, Environment, Actuators, Sensors) ⚫Environment types ⚫Agent types
  • 3. Agents Artificial Intelligence a modern approach 3 • 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 Artificial Intelligence a modern approach 4 • 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 Artificial Intelligence a modern approach 5 ⚫ Percepts: location and contents, e.g., [A,Dirty] ⚫ Actions: Left, Right, Suck, NoOp ⚫ Agent’s function 🡪 look-up table ⚪ For many agents this is a very large table Demo: http://www.ai.sri.com/~oreilly/aima3ejava/aima3ejavademos.h tml
  • 6. Rational agents Artificial Intelligence a modern approach 6 • Rationality – Performance measuring success – Agents prior knowledge of environment – Actions that agent can perform – Agent’s percept sequence to date • 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.
  • 7. Examples of Rational Choice ⚫See File: intro-choice.doc Artificial Intelligence a modern approach 7
  • 8. Rationality Artificial Intelligence a modern approach 8 ⚫Rational is different from omniscience ⚪ Percepts may not supply all relevant information ⚪ E.g., in card game, don’t know cards of others. ⚫Rational is different from being perfect ⚪ Rationality maximizes expected outcome while perfection maximizes actual outcome.
  • 9. Autonomy in Agents ⚫Extremes ⚪ No autonomy – ignores environment/data ⚪ Complete autonomy – must act randomly/no program ⚫Example: baby learning to crawl ⚫Ideal: design agents to have some autonomy ⚪ Possibly become more autonomous with experience The autonomy of an agent is the extent to which its behaviour is determined by its own experience, rather than knowledge of designer.
  • 10. PEAS Artificial Intelligence a modern approach 10 • 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: 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
  • 11. PEAS Artificial Intelligence a modern approach 11 ⚫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. PEAS Artificial Intelligence a modern approach 12 ⚫Agent: Interactive English tutor ⚫Performance measure: Maximize student's score on test ⚫Environment: Set of students ⚫Actuators: Screen display (exercises, suggestions, corrections) ⚫Sensors: Keyboard
  • 13. Environment types Artificial Intelligence a modern approach 13 • Fully observable (vs. partially observable) • Deterministic (vs. stochastic) • Episodic (vs. sequential) • Static (vs. dynamic) • Discrete (vs. continuous) • Single agent (vs. multiagent):
  • 14. Fully observable (vs. partially observable) Artificial Intelligence a modern approach 14 ⚫Is everything an agent requires to choose its actions available to it via its sensors? Perfect or Full information. ⚪ If so, the environment is fully accessible ⚫If not, parts of the environment are inaccessible ⚪ Agent must make informed guesses about world. ⚫In decision theory: perfect information vs. imperfect information. Cross Word Backgammon Taxi driver Part picking robot Poke r Image analysis Fully Fully Fully Partially Partially Partially
  • 15. Deterministic (vs. stochastic) Artificial Intelligence a modern approach 15 ⚫Does the change in world state ⚪ Depend only on current state and agent’s action? ⚫Non-deterministic environments ⚪ Have aspects beyond the control of the agent ⚪ Utility functions have to guess at changes in world Cross Word Backgammon Taxi driver Part picking robot Poke r Image analysis Cross Word Backgammon Taxi driver Part Poke r Image analysis Deterministic Deterministic Stochastic Stochastic Stochastic Stochastic
  • 16. Episodic (vs. sequential): Artificial Intelligence a modern approach 16 ⚫ Is the choice of current action ⚪ Dependent on previous actions? ⚪ If not, then the environment is episodic ⚫ In non-episodic environments: ⚪ Agent has to plan ahead: ⯍ Current choice will affect future actions Cross Word Backgammon Taxi driver Part picking robot Poke r Image analysis Sequential Sequential Sequential Sequential Episodic Episodic
  • 17. Static (vs. dynamic): Artificial Intelligence a modern approach 17 ⚫Static environments don’t change ⚪ While the agent is deliberating over what to do ⚫Dynamic environments do change ⚪ So agent should/could consult the world when choosing actions ⚪ Alternatively: anticipate the change during deliberation OR make decision very fast ⚫ Semidynamic: If the environment itself does not change with the passage of time but the agent's performance score does. Cross Word Backgammon Taxi driver Part picking robot Poke r Image analysis Static Static Static Dynamic Dynamic Semi Another example: off-line route planning vs. on-board navigation system
  • 18. Discrete (vs. continuous) Artificial Intelligence a modern approach 18 ⚫ A limited number of distinct, clearly defined percepts and actions vs. a range of values (continuous) Cross Word Backgammon Taxi driver Part picking robot Poke r Image analysis Discrete Discrete Discrete Conti Conti Conti
  • 19. Single agent (vs. multiagent): Artificial Intelligence a modern approach 19 ⚫ An agent operating by itself in an environment or there are many agents working together Cross Word Backgammon Taxi driver Part picking robot Poke r Image analysis Single Single Single Multi Multi Multi
  • 20. Artificial Intelligence a modern approach Observable Deterministic Static Episodic Agents Discrete Cross Word Backgammon Taxi driver Part picking robot Poke r Image analysis Deterministic Stochastic Deterministic Stochastic Stochastic Stochastic Sequential Sequential Sequential Sequential Episodic Episodic Static Static Static Dynamic Dynamic Semi Discrete Discrete Discrete Conti Conti Conti Single Single Single Multi Multi Multi Summary. Fully Fully Fully Partially Partially Partially
  • 21. Choice under (Un)certainty Artificial Intelligence a modern approach 21 Fully Observable Deterministic Certainty: Search Uncertainty no yes yes no
  • 22. Agent types Artificial Intelligence a modern approach 22 ⚫Four basic types in order of increasing generality: ⚪ Simple reflex agents ⚪ Reflex agents with state/model ⚪ Goal-based agents ⚪ Utility-based agents ⚪ All these can be turned into learning agents ⚪ http://www.ai.sri.com/~oreilly/aima3ejava/aima3ejavad emos.html
  • 23. Simple reflex agents Artificial Intelligence a modern approach 23
  • 24. Simple reflex agents Artificial Intelligence a modern approach 24 ⚫ Simple but very limited intelligence. ⚫ Action does not depend on percept history, only on current percept. ⚫ Therefore no memory requirements. ⚫ Infinite loops ⚪ Suppose vacuum cleaner does not observe location. What do you do given location = clean? Left of A or right on B -> infinite loop. ⚪ Fly buzzing around window or light. ⚪ Possible Solution: Randomize action. ⚪ Thermostat. ⚫ Chess – openings, endings ⚪ Lookup table (not a good idea in general) ⯍ 35100 entries required for the entire game
  • 25. States: Beyond Reflexes • Recall the agent function that maps from percept histories to actions: [f: P* 🡪 A] ⚫ An agent program can implement an agent function by maintaining an internal state. ⚫ The internal state can contain information about the state of the external environment. ⚫ The state depends on the history of percepts and on the history of actions taken: [f: P*, A*🡪 S 🡪A] where S is the set of states. ⚫ If each internal state includes all information relevant to information making, the state space is Markovian. Artificial Intelligence a modern approach 25
  • 26. States and Memory: Game Theory ⚫If each state includes the information about the percepts and actions that led to it, the state space has perfect recall. ⚫Perfect Information = Perfect Recall + Full Observability + Deterministic Actions. Artificial Intelligence a modern approach 26
  • 27. Model-based reflex agents Artificial Intelligence a modern approach 27 ⚫ Know how world evolves ⚪ Overtaking car gets closer from behind ⚫ How agents actions affect the world ⚪ Wheel turned clockwise takes you right ⚫ Model base agents update their state
  • 28. Goal-based agents Artificial Intelligence a modern approach 28 • knowing state and environment? Enough? – Taxi can go left, right, straight • Have a goal ⚪ A destination to get to ⚫Uses knowledge about a goal to guide its actions ⚪ E.g., Search, planning
  • 29. Goal-based agents Artificial Intelligence a modern approach 29 • Reflex agent breaks when it sees brake lights. Goal based agent reasons – Brake light -> car in front is stopping -> I should stop -> I should use brake
  • 30. Utility-based agents Artificial Intelligence a modern approach 30 ⚫Goals are not always enough ⚪ Many action sequences get taxi to destination ⚪ Consider other things. How fast, how safe….. ⚫A utility function maps a state onto a real number which describes the associated degree of “happiness”, “goodness”, “success”. ⚫Where does the utility measure come from? ⚪ Economics: money. ⚪ Biology: number of offspring. ⚪ Your life?
  • 32. Learning agents Artificial Intelligence a modern approach 32 ⚫ Performance element is what was previously the whole agent ⚫ Input sensor ⚫ Output action ⚫ Learning element ⚫ Modifies performance element.
  • 33. Learning agents Artificial Intelligence a modern approach 33 ⚫ Critic: how the agent is doing ⚫ Input: checkmate? ⚫ Fixed ⚫ Problem generator ⚫ Tries to solve the problem differently instead of optimizing. ⚫ Suggests exploring new actions -> new problems.
  • 34. Learning agents(Taxi driver) ⚪ Performance element ⯍ How it currently drives ⚪ Taxi driver Makes quick left turn across 3 lanes ⯍ Critics observe shocking language by passenger and other drivers and informs bad action ⯍ Learning element tries to modify performance elements for future ⯍ Problem generator suggests experiment out something called Brakes on different Road conditions ⚪ Exploration vs. Exploitation ⯍ Learning experience can be costly in the short run ⯍ shocking language from other drivers ⯍ Less tip ⯍ Fewer passengers Artificial Intelligence a modern approach 34
  • 35. The Big Picture: AI for Model-Based Agents Artificial Intelligence a modern approach 35 Action Learning Knowledge Logic Probability Heuristics Inference Planning Decision Theory Game Theory Reinforcement Learning Machine Learning Statistics
  • 36. The Picture for Reflex-Based Agents Artificial Intelligence a modern approach 36 Action Learning Reinforcement Learning • Studied in AI, Cybernetics, Control Theory, Biology, Psychology.
  • 37. Discussion Question ⚫Model-based behaviour has a large overhead. ⚫ Our large brains are very expensive from an evolutionary point of view. ⚫Why would it be worthwhile to base behaviour on a model rather than “hard-code” it? ⚫For what types of organisms in what type of environments? Artificial Intelligence a modern approach 37
  • 38. Summary ⚫ Agents can be described by their PEAS. ⚫ Environments can be described by several key properties: 64 Environment Types. ⚫ A rational agent maximizes the performance measure for their PEAS. ⚫ The performance measure depends on the agent function. ⚫ The agent program implements the agent function. ⚫ 3 main architectures for agent programs. ⚫ In this course we will look at some of the common and useful combinations of environment/agent architecture. Artificial Intelligence a modern approach 38