CSC 9010 Spring 2011. Paula Matuszek Intelligent Agents Overview Slides based in part on Hwee Tou...

CSC 9010 Spring 2011. Paula Matuszek

Intelligent Agents Overview

Slides based in part on Hwee Tou Ng, aima.eecs.berkeley.edu/slides-ppt, which are in turn based on Russell, aima.eecs.berkeley.edu/slides-pdf.

2CSC 9010 Spring 2011. Paula Matuszek

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

• Scooter: touch and rotation sensors; wheels


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


Vacuum-cleaner world

• Percepts: location and contents, e.g., [A,Dirty]

• Actions: Left, Right, Suck, NoOp


A vacuum-cleaner agentPercept sequence Action

[A,Clean] Right[A, Dirty] Suck[B, Clean] Left[B, Dirty] Suck

[A, Clean],[A, Clean] Right[A, Clean],[A, Dirty] Suck

… …


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.


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.

• Thus, rationality depends on– performance measure that defines success– prior knowledge of the environment– actions the agent can perform– percept sequence to date


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)


Task Environment• An agent operates within some task

environment, not in a blank world.• This environment includes:

– what the agent is trying to do– what resources it has to do it

• The nature of the environment affects how we design an appropriate agent.

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PEAS: Specifying an Agent's World• The task environment for an agent can be completely

specified by defining four things:– 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?

This PEAS specification gives us the information we need to design a rational agent.

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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

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PEAS: • Agent: Lego Scooter• Performance: number of seconds it keeps

moving without getting stuck• Environment: Flat surface with objects but

no dropoffs.• Actuators: Motors which turn two wheels• Sensors: touch sensors, rotation sensors

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PEAS• Agent: Medical diagnosis system

– Performance measure: – Environment:– Actuators:– Sensors:

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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)

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PEAS• Agent: Interactive English tutor• Performance measure: • Environment: • Actuators: • Sensors:

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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

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Agent functions and programs• An agent is completely specified by the

agent function mapping percept sequences to actions

• A rational agent function maximizes the average performance for a given environment class

• Aim: find a way to implement the rational agent function concisely

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Agent types• Four basic types in order of increasing

generality:• Simple reflex agents• Model-based reflex agents• Goal-based agents• Utility-based agents

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Simple reflex agents

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Simple reflex Vacuum Agentfunction REFLEX-VACUUM-AGENT ([location, status]) return an

actionif status == Dirty then return Suckelse if location == A then return Rightelse 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.• Even this simple agent can have a knowledge base, in

the form of condition-action rules.

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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.

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Reflex Agents with State

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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

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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• May use a KB for both condition-action rules and

what world/actions do.

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Goal-based agents

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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.

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Utility-based agents

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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.

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Learning agents

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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

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Summary• We can view most intelligent 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 and learning agents.

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