COMP 4640Intelligent & Interactive Systems

Cheryl Seals, Ph.D.

Computer Science &

Software Engineering

Auburn University

Lecture 2: Intelligent Agents

2

Intelligent & Interactive Systems: Lecture2

Intelligent Agents An Agent is anything that perceives its environment via sensors and

acts upon that environment via effectors.

In this course, we will be interested in the design of rational agents, i.e. agents that do the right thing giving a sequence of percepts.

In order to design rational agents (and also to be able to judge whether they are actually rational!) we must know:

how to evaluate an agent’s success

when to evaluate an agent’s success The how part is accomplished through the development of a

performance measure. For the when part, it may be best to measure performance over an

extended period of time; however, at times it may be advantageous to have intermediate performance measurements along the way.

3

Agents

Chapter 2AI by Russell & Norvig

4

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

5

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

6

Vacuum-cleaner world

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

Actions: Left, Right, Suck, NoOp Robots examples (iRobot)

7

Intelligent & Interactive Systems: Lecture2

For an agent, one can determine rational behavior based on:

1. the performance measure of the agent,2. the percept sequence (this is the

collection of all of the percepts received by the agent),

3. what the agent currently knows about the environment, and

4. the set of actions that the agent can presently perform.

8

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.

9

Intelligent & Interactive Systems: Lecture2

Ideal Rational Agents An ideal rational agent is one that performs any

action that is expected to maximize its performance measure given its percept sequence and its built-in knowledge.

Ideal rational agents are formed by ideal mappings from percept sequences to actions

An ideal rational agent may have some sense of autonomy. That is, it may be able to learn, adapt, and operate successfully in a variety of environments.

10

Intelligent & Interactive Systems: Lecture2

Structure of Intelligent Agents The objective of AI is the design and application of agent programs

that implement mappings between percepts and actions An agent can be viewed as a program that is developed to run on a

particular architecture (some computing device). The architecture:

1. makes percepts available,2. runs the agent program,3. sends actions to the effectors

Types of Agent Programs Intelligent systems are typically composed of a number of intelligent

agents. Each agent interacts (directly or indirectly) with one or more aspects of an environment. This type of agent interaction is similar to what we see in sports, business, and other organizations that are composed of a number of different agents with different responsibilities working together for the common good.

11

Intelligent & Interactive Systems: Lecture2

There are a number of different agent programs; however, many can be classified as one of the following:1. Simple Reflex (Reactive) Agents

2. Goal-based Agents

3. Model-based Agents

4. Utility-based Agents

5. Communication-based Agents

12

Simple reflex agents

13

Vacuum-cleaner world

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

Actions: Left, Right, Suck, NoOp

14

Model-based reflex agents

15

Goal-based agents

16

Utility-based agents

17

Learning agents

18

Next Class

Chapter 2 (continued)

Agent ExamplesAgent Environments