lec 02 intelligent agents

7/31/2019 Lec 02 Intelligent Agents

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Intelligent Agents

What is an Agent?

The Concept of Rationality

The Nature of Environments

The Structure of Agents


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What agents are ?


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Agents

An agent is an biologic (people or animals), robotic, orcomputational (a computer program) entity/tool that isable to carry out some task, usually to help a humanuser.

For example, An agent might be set up to buy a particular stock

when its price falls below a particular level.

A simple Internet search agent might be designed to

send queries to a number of search engines andcollate the results.

In many ways, the field of Artificial Intelligence as awhole can be seen as the study of methods that can beused to build intelligent agents.


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Intelligent agents continuously perform threefunctions: perception of dynamic conditions inthe environment; action to affect conditions in

the environment; and reasoning to interpretperceptions, solve problems, draw inferences,and determine actions.

Agents


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Agent Properties

Rationale: Able to act in a rational (or intelligent) wayAutonomous: Able to act independently, not subject toexternal control The ability to act and make decisions independently

of the programmer or user of the agentPersistent: Able to run continuouslyCommunicative: Able to provide information, orcommand other agentsCooperative: Able to work with other agents to achieve

goalsMobile: Able to move (typically related to networkmobility)Adaptive: Able to learn and adaptGoal-oriented: Able to achieve some goal


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Agents

An agent is anything that can be viewed asperceiving its environment through sensorsand acting upon that environment throughactuators

Actuators are sometimes called effectors


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Agents interact with environmentsthrough sensors and actuators.

environmentpercepts

actions

?

agent

sensors

effectors


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External Characterization of an Agent

We use the term perceptto refer to the agent'sperceptual inputs at any given instant.

An agent's percept sequenceis the complete history ofeverything the agent has ever perceived.

An agent's choice of action at any given instant candepend on the entire percept sequence observed todate.

If we can specify the agent's choice of action for everypossible percept sequence, then we have said more orless everything there is to say about the agent

Mathematically speaking, we say that an agent'sbehavior is described by the agent function that mapsany given percept sequence to an action.


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Agents

Vacuum cleaner world

Two locations: squares A and B

Vacuum agents perceives which square it isin and whether there is dirt in the square

Vacuum agent can choose to move left, moveright, Suck dirt, do nothing

Agent function if current square is dirty, thensuck , otherwise move to the other square


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AgentsPartial tabulation (external characterization) of simpleagent functionPercept sequence action

================ =======[A, clean] right

[A, dirty] suck[B, clean] left[B, dirty] suck[A, clean], [A, clean] right[A, clean], [A, dirty] suck [A, clean], [A, clean], [A, clean] right[A, clean], [A, clean], [A, dirty] suck


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The Concept of Rationality

Rational agent does the right thing, that is,every entry in the table for the agent function isfilled out correctly

The right action is the one that will cause theagent to be most successful

Performance measure criterion for success ofan agents behavior

Imposed by the designer who is constructing theagent


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Rationality

Vacuum cleaner agent measure performanceby the amount of dirt cleaned up in a single eighthour shift

Clean, dump dirt, clean, dump dirt ?More suitable performance measure: rewardagent for having a clean floor

Design performance measures according to

what one wants in the environment - notaccording to how one thinks the agent shouldbehave


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Rationality

What is rational at any given time dependson

Performance measure that defines the

criterion of success

Agents prior knowledge of the environment

Actions that the agent can perform

Agents percept sequence to date


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Rationality

Definition

For each possible percept sequence, a rationalagent should select an action that is expected to

maximize its performance measure, given theevidence provided by the percept sequence andwhatever built-in knowledge the agent has

The simple vacuum-cleaner agent that cleans asquare if it is dirty and moves to the other squareif notis it a rational agent?


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Vacuumcleaner agent rational?

What is its performance measure

Performance measure awards one point foreach clean square at each time step over a

lifetime of 1000 times stepsWhat is known about the environment

Geography of environment is known a priori.Dirt distribution and initial location of agent

are not known. Clean squares stay clean.Sucking cleans the current square. Left andright move the agent


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Vacuumcleaner agent rational?

What sensors and actuators does it have

Available actions left, right, suck,NoOp

Agent perceives Its location andwhether that location contains dirt

Under these conditions, the agent isrational


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The Nature Of Environments

We have a definition of rationality

We can now think of building rationalagents


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Specifying the task environment

Task environment a problem for which a rational agents is the

solution specified by the PEAS description:Performance measure, Environment, Actuators,SensorsMust first specify the setting for intelligent agentdesignConsider, e.g., the task of designing an automated

taxi: Performance measure Environment Actuators Sensors


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PEAS for Automated Taxi

Performance measure: getting to correct destination;minimizing fuel consumption , wear and tear, trip time,cost, traffic law violations; maximizing safety, comfort,profitsEnvironment: Variety of roads , other traffic, pedestrians,customers,, traffic, road works, Interact with thepassengers, snow

Actuators: Steering wheel, brake, signal, hornaccelerator, steering, output to a display screen or voicesynthesizer; communicate with other vehiclesSensors: controllable cameras, speedometer odometer,accelerometer, engine and other system sensors , GPS,sensors to detect distances to other cars and obstacles,keyboard or microphone for the passenger to request a

destination


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PEAS for a Medical DiagnosisSystem

Performance measure: Healthy patient, minimizecosts

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 for an Interactive English Tutor

Performance measure: Maximize student'sscore on test

Environment: Set of students

Actuators: Screen display (exercises,suggestions, corrections)

Sensors: Keyboard


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Properties of task environments

Fully observable vs. partially observable

Deterministic vs. stochastic

Episodic vs. sequential

Static vs. dynamic

Discrete vs. continuous

Single agent vs. multiagent


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Fully observable vs. partially

observable

If an agent's sensors give it access to the complete stateof the environment at each point in time, then we saythat the task environment is fully observable a task environment is effectively fully observable if the sensors

detect all aspects that are relevantto the choice of action

Fully observable environments are convenient becausethe agent need not maintain any internal state to keeptrack of the world

An environment might be partially observable because of

noisy and inaccurate sensors or because parts of thestate are simply missing from the sensor data a vacuum agent with only a local dirt sensor cannot tell whether

there is dirt in other squares

automated taxi cannot see what other drivers are thinking


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Deterministic vs. stochastic

A deterministic environment is one in which any actionhas a single guaranteed effect there is no uncertaintyabout the state of the environment that will result fromperforming an action

If the next state of the environment is completelydetermined by the current state and the action executedby the agent, then we say the environment isdeterministic; otherwise, it is stochastic Taxi driving is clearly stochastic in this sense, because one can

never predict the behavior of traffic exactly The vacuum world as we described it is deterministic, but

variations can include stochastic elements such as randomlyappearing dirt and an unreliable suction mechanism

Non-deterministic environments present greater

problems for the agent designer


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Episodic vs. sequential

In an episodic task environment, the agent's experienceis divided into atomic episodes. Each episode consists ofthe agent perceiving and then performing a single action the next episode does not depend on the actions

taken in previous episodes

an agent that has to spot defective parts on anassembly line bases each decision on the currentpart, regardless of previous decisions

In sequential environments, on the other hand, thecurrent decision could affect all future decisions Chess and taxi driving are sequentialEpisodic environments are much simpler than sequentialenvironments because the agent does not need to thinkahead


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Static vs. dynamic

If the environment can change while an agent isdeliberating, then we say the environment is dynamic forthat agent; otherwise, it is static. Static environments are easy to deal with because

the agent need not keep looking at the world while it

is deciding on an action Crossword puzzles are staticDynamic environments, are continuously asking theagent what it wants to do. If the agent it hasn't decidedyet, that counts as deciding to do nothing Taxi driving is dynamicIf the environment itself does not change with thepassage of time but the agent's performance score does,then we say the environment is semidynamic Chess, when played with a clock, is semidynamic.


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Discrete vs. continuous

The discrete/continuous distinction can beapplied to the stateof the environment, to theway timeis handled

For example, a discrete-state environment such as achess game has a finite number of distinct states.

Chess also has a discrete set of percepts andactions.

Taxi driving is a continuous state and continuous-timeproblem: the speed and location of the taxi and of theother vehicles sweep through a range of continuousvalues and do so smoothly over time


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Single agent vs. multiagent

Does the environment contain a single agent, or possiblymultiple agents acting in a cooperative or competitivefashion

In chess, the opponent entity B is trying to maximize its

performance measure, which, by the rules of chess,minimizes agent A's performance measure. Thus, chessis a competitivemultiagent environment.

Avoiding collisions maximizes the performance measure

of all agents, so it is a cooperativemultiagentenvironment.


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Examples

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The environment type largely determines the agent design The real world is (of course) partially observable, stochastic,sequential, dynamic, continuous, multi-agent


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Examples of task environments andtheir characteristics.


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Environment Types

The simplest environment is

Fully observable, deterministic, episodic,static, discrete and single-agent.

Most real situations are: Partially observable, stochastic, sequential,

dynamic, continuous and multi-agent.

lec 02 intelligent agents

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