Agents and Intelligent AgentsAn agent is anything that can be viewed as

perceiving its environment through sensors and acting upon that environment through actuators

An intelligent agent acts further for its own interests.

Artificial Intelligence, Lecturer #8

Example of AgentsHuman agent:

Sensors: eyes, ears, nose…. Actuators: hands, legs, mouth, …

Robotic agent: Sensors: cameras and infrared range finders Actuators: various motors

Agents include humans, robots, thermostats, etcPerceptions: Vision, speech reorganization, etc.

Agent Function & programAn agent is specified by an agent function f that maps

sequences of percepts Y to actions A:

The agent program runs on the physical architecture to produce fagent = architecture + program

“Easy” solution: table that maps every possible sequence Y to an action A

0 1

0 1

{ , ,..., }

{ , ,..., }

:

T

T

Y y y y

A a a a

f Y A

Agents and Environments

The agent function maps from percept histories (sequences of percepts) to actions:

[f: P* A]

Example: A Vacuum-Cleaner Agent

A B

Percepts: location and contents, e.g., (A,dust)• (Idealization: locations are discrete)

Actions: move, clean, do nothing: LEFT, RIGHT, SUCK, NOP

Example: A Vacuum-Cleaner Agent

Properties of AgentMobility: the ability of an agent to move around in an environment.

Veracity: an agent will not knowingly communicate false

information

Benevolence: agents do not have conflicting goals, and that every

agent will therefore always try to do what is asked of it

Rationality: agent will act in order to achieve its goals, and will not

act in such a way as to prevent its goals being achieved.

Learning/adoption: agents improve performance over time

Agents Vs. Objects Agents are autonomous

agents embody stronger notion of autonomy than objects, and in particular,

they decide for themselves whether or not to perform an action on request

from another agent

Agents are smart

capable of flexible (reactive, pro-active, social) behavior, and the standard

object model has nothing to say about such types of behavior

Agents are active

a multi-agent system is inherently multi-threaded, in that each agent is

assumed to have at least one thread of active control

The Concept of Rationality

What is rational at any given time depends on four

things:

The performance measure that defines the criterion of

success.

The agent’s prior knowledge of the environment.

The actions the agent can perform.

The agent’s percept sequence to date.

Rational Agents Rational Agent:

For each possible percept sequence, a rational agent should

select an action that is expected to maximize its performance

measure.

Performance measure:

An objective criterion for success of an agent's behavior, given

the evidence provided by the percept sequence.

Nature of Task Environment

To design a rational agent we need to specify a task environment

a problem specification for which the agent is a solution

PEAS: to specify a task environment

Performance measure

Environment

Actuators

Sensors

PEAS Specifying an Automated Taxi Driver

Performance measure: safe, fast, legal, comfortable, maximize profits

Environment: roads, other traffic, pedestrians, customers

Actuators: steering, accelerator, brake, signal, horn

Sensors: cameras, sonar, speedometer, GPS

PEAS: Another Example Agent: Medical diagnosis system

Performance measure:

Healthy patient, minimize costs.

Environment:

Patient, hospital, staff

Actuators:

Screen display (questions, tests, diagnoses, treatments, referrals)

Sensors:

Keyboard (entry of symptoms, findings, patient's answers)

Recommended Textbooks [Negnevitsky, 2001] M. Negnevitsky “ Artificial Intelligence: A guide to

Intelligent Systems”, Pearson Education Limited, England, 2002. [Russel, 2003] S. Russell and P. Norvig Artificial Intelligence: A Modern

Approach Prentice Hall, 2003, Second Edition [Patterson, 1990] D. W. Patterson, “Introduction to Artificial Intelligence

and Expert Systems”, Prentice-Hall Inc., Englewood Cliffs, N.J, USA, 1990.

[Minsky, 1974] M. Minsky “A Framework for Representing Knowledge”, MIT-AI Laboratory Memo 306, 1974.

[Hubel, 1995] David H. Hubel, “Eye, Brain, and Vision” [Ballard, 1982] D. H. Ballard and C. M. Brown, “Computer Vision”,

Prentice Hall, 1982.