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Chapter 2 Intelligent Agent

chapter 2 of artificial intelligence a modern approach book

Mahmoud Abdelsattar

on 12 March 2015

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Transcript of Chapter 2 Intelligent Agent

Chapter 2 Intelligent Agent
Agents and environments
PEAS (Performance measure, Environment, Actuators, Sensors)
Environment types
Agent types

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.
PEAS: Performance measure, Environment, Actuators, Sensors
Must first specify the setting for intelligent agent design
Environment types
The environment type largely determines the agent design

The real world is (of course) partially observable, stochastic, sequential, dynamic, continuous, multi-agent
An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators.
Agents and environments
The agent function maps from percept histories to actions:

[f: P* -----> A]
Another form f(P*)=A
The agent program runs on the physical architecture to produce f
Agent: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.
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)

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
Vacuum-cleaner world
Percepts: location and contents, e.g., [A,Dirty]

Actions: Left, Right, Suck, NoOp
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)
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.
Agent: Interactive English tutor

Performance measure: Maximize student's score on test

Environment: Set of students.

Actuators: Screen display (exercises, suggestions, corrections).

Sensors: Keyboard.

An agent's sensors give it access to the complete state of the environment at each point in time.
Fully observable (vs. partially observable):
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)
Deterministic (vs. stochastic):
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.
Episodic “”(vs. sequential):
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)
Static (vs. dynamic):
A limited number of distinct, clearly defined percepts and actions.
Discrete (vs. continuous):
An agent operating by itself in an environment.
Single agent (vs. multiagent):
ِAgent Types
Simple reflex agents
Model-based reflex agents
Goal-based agents
Utility-based agents
Full transcript