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Intelligent Agents

Chapter 2

Modified by Vali Derhami

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Outline

• Agents and environments

• Rationality

• PEAS (Performance measure, Environment, Actuators, Sensors)

• Environment types

• Agent types

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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

• General assumption: agent can perceive its own action

•

•

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Agents (cont.)• Percept sequence is the complete history of

everything the agent has ever perceived.

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Agents and environments

• Agent's behavior is described by the agent function that maps any given percept sequence to an action.

• برنامه . حالیکه در است گونه انتزاع ریاضی شرح یک عامل تابعاجرا فیزیکی سیستم داخل در که است آن کامل سازی پیاده عامل

شود .می• The agent function maps from percept histories to actions:

Tabular representation: a large table for most agents

• The agent program runs on the physical architecture to produce f

• agent = architecture + program• [f: P* A]•

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Vacuum-cleaner world

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

• Actions: Left, Right, Suck, NoOp

•

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A vacuum-cleaner agent

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Rational agents

• An agent should "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.

• Note: Design performance measures according to what one actually wants in the environment, rather than according to how one thinks the agent should behave.

•

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What is rational

• The performance measure that defines the criterion of success.

• The agent's prior knowledge of the environment.

• The actions that the agent can perform.

• The agent's percept sequence to date.

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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.

•

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Omniscience, learning, and autonomy

•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)

•

• حالیکه در رود می امید که راندمانی میکند بیشینه عقالنیتبودن میکند (perfection) کامل بیشینه را واقعی .راندمان

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Successful agents split the task of computing the agent function into three different periods: During designing: some of the computation is

done by its designers; During deliberating (thinking) on its next

action, the agent does more computation; During learning from experience: it does even

more computation to decide how to modify its behavior.

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Task Environment (PEAS)

• PEAS: Performance measure, Environment, Actuators, Sensors

• Consider, e.g., the task of designing an automated taxi driver:– Performance measure– Actuators– Sensors–

– Environment•

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PEAS• Consider, e.g., the task of designing an

automated taxi driver:

•

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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: 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

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PEAS

• Agent: Interactive English tutor Performance measure: Maximize student's

score on test

Environment: Set of students, testing agency

Actuators: Screen display (exercises, suggestions, corrections)

Sensors: Keyboard entry

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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.• Episodic (vs. sequential): The agent's experience is divided into atomic

"episodes" (each episode consists of the agent perceiving and then performing a single action), and the choice of action in each episode depends only on the episode itself.

•

• 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)

• نایقین نباشد قطعی یا نباشد پذیر مشاهده کامال که محیطی(uncertaion) شود می گفته

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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.

• Single agent (vs. multiagent): An agent operating by itself in an environment.

•

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Environment typesChess with Chess without Taxi driving Image a clock a clock Analysis

Fully observable Yes Yes No YesDeterministic Strategic Strategic No YesEpisodic No No No YesStatic Semi Yes No SemiDiscrete Yes Yes No NoSingle agent No No No Yes

• 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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Structure of Agents

• The job of AI: Design the agent program

that implements the agent function

mapping percepts to actions.

• Agent= Architecture +Program

• Program has to be appropriate for the

architecture

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Agent functions and programs

• Agent programs: they take the current percept as input from the sensors and return an action to the actuators. Difference between agent program and agent

function:o Agent program: takes the current percept as input o Agent function: takes the entire percept history.

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

•

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Table-lookup agent

Drawbacks:Huge tableTake a long time to build the tableNo autonomyEven with learning, need a long time to learn the table entries

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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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Vacuum agent

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Simple reflex agents

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Model-based reflex agents

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Model-based reflex agents

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Goal-based agents

•

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Utility-based agents

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Learning agents

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Learning agentsFour conceptual components

• Learning element: making improvements.

• Performance element: selecting external actions.

• Critic: tells the learning element how well the

agent is doing with respect to a fixed performance

standard

• Problem generator: suggesting actions that will

lead to new and informative experiences.

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شود برداشته سایت وب از دوم .تمرین