ITCS 3153 Artificial Intelligence Lecture 2 Agents Agents

ITCS 3153Artificial Intelligence

Lecture 2Lecture 2

AgentsAgents

Lecture 2Lecture 2

AgentsAgents

Chess Article

Deep Blue (IBM)Deep Blue (IBM)

• 418 processors, 200 million positions per second418 processors, 200 million positions per second

Deep Junior (Israeli Co.)Deep Junior (Israeli Co.)


KasparovKasparov

• 100 billion neurons in brain, 2 moves per second100 billion neurons in brain, 2 moves per second

But there are 85 billion ways to play the first four movesBut there are 85 billion ways to play the first four moves

Deep Blue (IBM)Deep Blue (IBM)


Deep Junior (Israeli Co.)Deep Junior (Israeli Co.)


KasparovKasparov

• 100 billion neurons in brain, 2 moves per second100 billion neurons in brain, 2 moves per second

But there are 85 billion ways to play the first four movesBut there are 85 billion ways to play the first four moves

Chess Article

Cognitive psychologists report chess is a game of Cognitive psychologists report chess is a game of pattern matching for humanspattern matching for humans

• But what patterns do we see?But what patterns do we see?

• What rules do we use to evaluate perceived patterns?What rules do we use to evaluate perceived patterns?

Cognitive psychologists report chess is a game of Cognitive psychologists report chess is a game of pattern matching for humanspattern matching for humans

• But what patterns do we see?But what patterns do we see?

• What rules do we use to evaluate perceived patterns?What rules do we use to evaluate perceived patterns?

What is an agent?

PerceptionPerception• Sensors receive input from environmentSensors receive input from environment

– Keyboard clicksKeyboard clicks

– Camera dataCamera data

– Bump sensorBump sensor

ActionAction

• Actuators impact the environmentActuators impact the environment

– Move a robotic armMove a robotic arm

– Generate output for computer displayGenerate output for computer display

PerceptionPerception• Sensors receive input from environmentSensors receive input from environment

– Keyboard clicksKeyboard clicks

– Camera dataCamera data

– Bump sensorBump sensor

ActionAction

• Actuators impact the environmentActuators impact the environment

– Move a robotic armMove a robotic arm

– Generate output for computer displayGenerate output for computer display

Perception

PerceptPercept

• Perceptual inputs at an instantPerceptual inputs at an instant

• May include perception of internal stateMay include perception of internal state

Percept SequencePercept Sequence

• Complete history of all prior perceptsComplete history of all prior percepts

Do you need a Do you need a percept sequencepercept sequence to play Chess? to play Chess?

PerceptPercept

• Perceptual inputs at an instantPerceptual inputs at an instant

• May include perception of internal stateMay include perception of internal state

Percept SequencePercept Sequence

• Complete history of all prior perceptsComplete history of all prior percepts

Do you need a Do you need a percept sequencepercept sequence to play Chess? to play Chess?

An agent as a function

Agent maps percept sequence to actionAgent maps percept sequence to action

• Agent:Agent:

– Set of all inputs known as Set of all inputs known as state spacestate space

Agent FunctionAgent Function

• If inputs are finite, a table can store mappingIf inputs are finite, a table can store mapping

• Scalable?Scalable?

• Reverse Engineering?Reverse Engineering?

Agent maps percept sequence to actionAgent maps percept sequence to action

• Agent:Agent:

– Set of all inputs known as Set of all inputs known as state spacestate space

Agent FunctionAgent Function

• If inputs are finite, a table can store mappingIf inputs are finite, a table can store mapping

• Scalable?Scalable?

• Reverse Engineering?Reverse Engineering?

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Evaluating agent programs

We agree on what an agent must doWe agree on what an agent must do

Can we evaluate its quality?Can we evaluate its quality?

Performance MetricsPerformance Metrics

• Very ImportantVery Important

• Frequently the hardest part of the research problemFrequently the hardest part of the research problem

• Design these to suit what you really want to happenDesign these to suit what you really want to happen

We agree on what an agent must doWe agree on what an agent must do

Can we evaluate its quality?Can we evaluate its quality?

Performance MetricsPerformance Metrics

• Very ImportantVery Important

• Frequently the hardest part of the research problemFrequently the hardest part of the research problem

• Design these to suit what you really want to happenDesign these to suit what you really want to happen

Rational Agent

For each percept sequence, a rational agent For each percept sequence, a rational agent should select an action that maximizes its should select an action that maximizes its performance measureperformance measure

Example: autonomous vacuum cleanerExample: autonomous vacuum cleaner

• What is the performance measure?What is the performance measure?

For each percept sequence, a rational agent For each percept sequence, a rational agent should select an action that maximizes its should select an action that maximizes its performance measureperformance measure

Example: autonomous vacuum cleanerExample: autonomous vacuum cleaner

• What is the performance measure?What is the performance measure?• Penalty for eating the cat? How much?Penalty for eating the cat? How much?

• Penalty for missing a spot?Penalty for missing a spot?

• Reward for speed?Reward for speed?

• Reward for conserving power?Reward for conserving power?

• Penalty for eating the cat? How much?Penalty for eating the cat? How much?

• Penalty for missing a spot?Penalty for missing a spot?

• Reward for speed?Reward for speed?

• Reward for conserving power?Reward for conserving power?

Learning and Autonomy

LearningLearning

• To update the agent function in light of observed To update the agent function in light of observed performance of percept-sequence to action pairsperformance of percept-sequence to action pairs

– ExploreExplore new parts of state space new parts of state space

Learn from trial and errorLearn from trial and error

– Change internal variables that influence action selectionChange internal variables that influence action selection

LearningLearning

• To update the agent function in light of observed To update the agent function in light of observed performance of percept-sequence to action pairsperformance of percept-sequence to action pairs

– ExploreExplore new parts of state space new parts of state space

Learn from trial and errorLearn from trial and error

– Change internal variables that influence action selectionChange internal variables that influence action selection

Adding intelligence to agent function

At design timeAt design time• Some agents are designed with clear procedure to improve Some agents are designed with clear procedure to improve

performance over time. Really the engineer’s intelligence.performance over time. Really the engineer’s intelligence.

– Camera-based user identificationCamera-based user identification

At run-timeAt run-time• Agent executes complicated equation to map input to outputAgent executes complicated equation to map input to output

Between trialsBetween trials• With experience, agent changes its program (parameters)With experience, agent changes its program (parameters)

At design timeAt design time• Some agents are designed with clear procedure to improve Some agents are designed with clear procedure to improve

performance over time. Really the engineer’s intelligence.performance over time. Really the engineer’s intelligence.

– Camera-based user identificationCamera-based user identification

At run-timeAt run-time• Agent executes complicated equation to map input to outputAgent executes complicated equation to map input to output

Between trialsBetween trials• With experience, agent changes its program (parameters)With experience, agent changes its program (parameters)

How big is your percept?

Dung BeetleDung Beetle

• Largely feed forwardLargely feed forward

Sphex WaspSphex Wasp

• Reacts to environment (feedback) but not learningReacts to environment (feedback) but not learning

A DogA Dog

• Reacts to environment and can significantly alter behavior Reacts to environment and can significantly alter behavior

Dung BeetleDung Beetle

• Largely feed forwardLargely feed forward

Sphex WaspSphex Wasp

• Reacts to environment (feedback) but not learningReacts to environment (feedback) but not learning

A DogA Dog

• Reacts to environment and can significantly alter behavior Reacts to environment and can significantly alter behavior

Qualities of a task environment

Fully ObservableFully Observable• Agent need not store any aspects of stateAgent need not store any aspects of state

– The Brady Bunch as intelligent agentsThe Brady Bunch as intelligent agents

– Volume of observables may be overwhelmingVolume of observables may be overwhelming

Partially ObservablePartially Observable• Some data is unavailableSome data is unavailable

– MazeMaze

– Noisy sensorsNoisy sensors

Fully ObservableFully Observable• Agent need not store any aspects of stateAgent need not store any aspects of state

– The Brady Bunch as intelligent agentsThe Brady Bunch as intelligent agents

– Volume of observables may be overwhelmingVolume of observables may be overwhelming

Partially ObservablePartially Observable• Some data is unavailableSome data is unavailable

– MazeMaze

– Noisy sensorsNoisy sensors


DeterministicDeterministic

• Always the same outcome for state/action pairAlways the same outcome for state/action pair

StochasticStochastic

• Not always predictable – randomNot always predictable – random

Partially Observable vs. StochasticPartially Observable vs. Stochastic

• My cats think the world is stochasticMy cats think the world is stochastic

• Physicists think the world is deterministicPhysicists think the world is deterministic

DeterministicDeterministic

• Always the same outcome for state/action pairAlways the same outcome for state/action pair

StochasticStochastic

• Not always predictable – randomNot always predictable – random

Partially Observable vs. StochasticPartially Observable vs. Stochastic

• My cats think the world is stochasticMy cats think the world is stochastic

• Physicists think the world is deterministicPhysicists think the world is deterministic


MarkovianMarkovian

• Future state only depends on current state Future state only depends on current state

EpisodicEpisodic

• Percept sequence can be segmented into independent temporal Percept sequence can be segmented into independent temporal categoriescategories

– Behavior at traffic light independent of previous trafficBehavior at traffic light independent of previous traffic

SequentialSequential

• Current decision could affect all future decisionsCurrent decision could affect all future decisions

Which is easiest to program?Which is easiest to program?

MarkovianMarkovian

• Future state only depends on current state Future state only depends on current state

EpisodicEpisodic

• Percept sequence can be segmented into independent temporal Percept sequence can be segmented into independent temporal categoriescategories

– Behavior at traffic light independent of previous trafficBehavior at traffic light independent of previous traffic

SequentialSequential

• Current decision could affect all future decisionsCurrent decision could affect all future decisions

Which is easiest to program?Which is easiest to program?


StaticStatic

• Environment doesn’t change over timeEnvironment doesn’t change over time

– Crossword puzzleCrossword puzzle

DynamicDynamic

• Environment changes over timeEnvironment changes over time

– Driving a carDriving a car

Semi-dynamicSemi-dynamic

• Environment is static, but performance metrics are dynamicEnvironment is static, but performance metrics are dynamic

– Drag racingDrag racing

StaticStatic

• Environment doesn’t change over timeEnvironment doesn’t change over time

– Crossword puzzleCrossword puzzle

DynamicDynamic

• Environment changes over timeEnvironment changes over time

– Driving a carDriving a car

Semi-dynamicSemi-dynamic

• Environment is static, but performance metrics are dynamicEnvironment is static, but performance metrics are dynamic

– Drag racingDrag racing


DiscreteDiscrete• Values of a state space feature (dimension) are constrained Values of a state space feature (dimension) are constrained

to distinct values from a finite setto distinct values from a finite set

– Blackjack: f(your cards, exposed cards) = actionBlackjack: f(your cards, exposed cards) = action

ContinuousContinuous• Variable has infinite variationVariable has infinite variation

– Antilock brakes: f (vehicle speed, wheel velocity) = unlockAntilock brakes: f (vehicle speed, wheel velocity) = unlock

– Are computers really continuous?Are computers really continuous?

DiscreteDiscrete• Values of a state space feature (dimension) are constrained Values of a state space feature (dimension) are constrained

to distinct values from a finite setto distinct values from a finite set

– Blackjack: f(your cards, exposed cards) = actionBlackjack: f(your cards, exposed cards) = action

ContinuousContinuous• Variable has infinite variationVariable has infinite variation

– Antilock brakes: f (vehicle speed, wheel velocity) = unlockAntilock brakes: f (vehicle speed, wheel velocity) = unlock

– Are computers really continuous?Are computers really continuous?


Towards a terse description of problem domainsTowards a terse description of problem domains• State space: features, dimensionality, degrees of freedomState space: features, dimensionality, degrees of freedom

• Observable?Observable?

• Predictable?Predictable?

• Dynamic?Dynamic?

• Continuous?Continuous?

• Performance metricPerformance metric

Towards a terse description of problem domainsTowards a terse description of problem domains• State space: features, dimensionality, degrees of freedomState space: features, dimensionality, degrees of freedom

• Observable?Observable?

• Predictable?Predictable?

• Dynamic?Dynamic?

• Continuous?Continuous?

• Performance metricPerformance metric

Building Agent Programs

The table approachThe table approach• Build a table mapping states to actionsBuild a table mapping states to actions

– Chess has 10Chess has 10150 150 entries (10entries (108080 atoms in the universe) atoms in the universe)

– I’ve said memory is free, but keep it within the confines of I’ve said memory is free, but keep it within the confines of the boundable universethe boundable universe

• Still, tables have their placeStill, tables have their place

Discuss four agent program principlesDiscuss four agent program principles

The table approachThe table approach• Build a table mapping states to actionsBuild a table mapping states to actions

– Chess has 10Chess has 10150 150 entries (10entries (108080 atoms in the universe) atoms in the universe)

– I’ve said memory is free, but keep it within the confines of I’ve said memory is free, but keep it within the confines of the boundable universethe boundable universe

• Still, tables have their placeStill, tables have their place

Discuss four agent program principlesDiscuss four agent program principles

Simple Reflex Agents

• Sense environmentSense environment

• Match sensations with rules in databaseMatch sensations with rules in database

• Rule prescribes an actionRule prescribes an action

Reflexes can be badReflexes can be bad

• Don’t put your hands down when falling backwards!Don’t put your hands down when falling backwards!

Inaccurate informationInaccurate information

• Misperception can trigger reflex when inappropriateMisperception can trigger reflex when inappropriate

But rules databases can be made large and complexBut rules databases can be made large and complex

• Sense environmentSense environment

• Match sensations with rules in databaseMatch sensations with rules in database

• Rule prescribes an actionRule prescribes an action

Reflexes can be badReflexes can be bad

• Don’t put your hands down when falling backwards!Don’t put your hands down when falling backwards!

Inaccurate informationInaccurate information

• Misperception can trigger reflex when inappropriateMisperception can trigger reflex when inappropriate

But rules databases can be made large and complexBut rules databases can be made large and complex

Simple Reflex Agents

RandomizationRandomization

• The vacuum cleaner problemThe vacuum cleaner problem

RandomizationRandomization

• The vacuum cleaner problemThe vacuum cleaner problem

Left Right

DirtyDirty

Model-based Reflex Agents

So when you can’t see something, you model it!So when you can’t see something, you model it!

• Create an internal variable to store your expectation of Create an internal variable to store your expectation of variables you can’t observevariables you can’t observe

• If I throw a ball to you and it falls short, do I know why?If I throw a ball to you and it falls short, do I know why?

– Aerodynamics, mass, my energy levels…Aerodynamics, mass, my energy levels…

– I do have a modelI do have a model

Ball falls short, throw harderBall falls short, throw harder

So when you can’t see something, you model it!So when you can’t see something, you model it!

• Create an internal variable to store your expectation of Create an internal variable to store your expectation of variables you can’t observevariables you can’t observe

• If I throw a ball to you and it falls short, do I know why?If I throw a ball to you and it falls short, do I know why?

– Aerodynamics, mass, my energy levels…Aerodynamics, mass, my energy levels…

– I do have a modelI do have a model

Ball falls short, throw harderBall falls short, throw harder

Model-based Reflex Agents

Admit it, you can’t see and understand everythingAdmit it, you can’t see and understand everything

Models are very important!Models are very important!

• We all use models to get through our livesWe all use models to get through our lives

– Psychologists have many names for these context-Psychologists have many names for these context-sensitive modelssensitive models

• Agents need models tooAgents need models too

Admit it, you can’t see and understand everythingAdmit it, you can’t see and understand everything

Models are very important!Models are very important!

• We all use models to get through our livesWe all use models to get through our lives

– Psychologists have many names for these context-Psychologists have many names for these context-sensitive modelssensitive models

• Agents need models tooAgents need models too

Goal-based Agents

Lacking moment-to-moment performance measureLacking moment-to-moment performance measure

Overall goal is knownOverall goal is known

How to get from A to B?How to get from A to B?

• Current actions have future consequencesCurrent actions have future consequences

• SearchSearch and and PlanningPlanning are used to explore paths through state are used to explore paths through state space from A to Bspace from A to B

Lacking moment-to-moment performance measureLacking moment-to-moment performance measure

Overall goal is knownOverall goal is known

How to get from A to B?How to get from A to B?

• Current actions have future consequencesCurrent actions have future consequences

• SearchSearch and and PlanningPlanning are used to explore paths through state are used to explore paths through state space from A to Bspace from A to B

Utility-based Agents

Goal-directed agents that have a utility functionGoal-directed agents that have a utility function

• Function that maps internal and external states into a scalarFunction that maps internal and external states into a scalar

– A scalar is a numberA scalar is a number

Goal-directed agents that have a utility functionGoal-directed agents that have a utility function

• Function that maps internal and external states into a scalarFunction that maps internal and external states into a scalar

– A scalar is a numberA scalar is a number

Learning Agents

Learning ElementLearning Element

• Making improvementsMaking improvements

Performance ElementPerformance Element

• Selecting actionsSelecting actions

CriticCritic

• Provides learning element with feedback about progressProvides learning element with feedback about progress

Problem GeneratorProblem Generator

• Provides suggestions for new tasks to explore state spaceProvides suggestions for new tasks to explore state space


• Making improvementsMaking improvements


• Selecting actionsSelecting actions

CriticCritic

• Provides learning element with feedback about progressProvides learning element with feedback about progress


• Provides suggestions for new tasks to explore state spaceProvides suggestions for new tasks to explore state space

A taxi driver


• Knowledge of how to drive in trafficKnowledge of how to drive in traffic

CriticCritic

• Observes tips from customers and horn honking from other carsObserves tips from customers and horn honking from other cars


• Relates low tips to actions that may be the causeRelates low tips to actions that may be the cause


• Proposes new routes to try and improved driving skillsProposes new routes to try and improved driving skills


• Knowledge of how to drive in trafficKnowledge of how to drive in traffic

CriticCritic

• Observes tips from customers and horn honking from other carsObserves tips from customers and horn honking from other cars


• Relates low tips to actions that may be the causeRelates low tips to actions that may be the cause


• Proposes new routes to try and improved driving skillsProposes new routes to try and improved driving skills

Review

Outlined families of AI problems and solutionsOutlined families of AI problems and solutions

Next class we study search problemsNext class we study search problems

Outlined families of AI problems and solutionsOutlined families of AI problems and solutions

Next class we study search problemsNext class we study search problems

Documents

ITCS 3153 Artificial Intelligence Lecture 2 Agents Agents