intelligent agents chapter 2. cis 391 - intro to ai - fall 2008 2 outline brief review agents and...

Intelligent Agents

Chapter 2

CIS 391 - Intro to AI - Fall 2008 2

Outline

Brief Review Agents and environments Rationality PEAS (Performance measure, Environment,

Actuators, Sensors) Environment types Agent types

CIS 391 - Intro to AI - Fall 2008 3

Rational behavior: doing whatever is expected to maximize goal achievement, given the available information

This course is about effective programming techniques for designing rational agents

Review I: AI as Acting Rationally

Thinking humanly Thinking rationally

Acting humanly Acting rationally

CIS 391 - Intro to AI - Fall 2008 4

Review II: Rational agents An agent is an entity that perceives and acts

Abstractly, an agent is a function from percept histories to actions:

[f: P* A]

For any given class of environments and tasks, we seek the agent (or class of agents) with the best performance

CIS 391 - Intro to AI - Fall 2008 5

Agents

An agent is anything that can be viewed as • perceiving its environment through sensors and • acting upon that environment through actuators

Human agent: • Sensors: eyes, ears, ...• Actuators: hands, legs, mouth, …

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

Agents include humans, robots, softbots, thermostats, …

CIS 391 - Intro to AI - Fall 2008 6

Agents and environments

An agent is specified by an agent function f that maps sequences of percepts Y to actions a from a set A:

Y={y0, y1, … , yt}

A={a0, a1, … , ak}

CIS 391 - Intro to AI - Fall 2008 7

Agent function & program

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

“Easy” solution: table that maps every possible sequence Y to an action a• One small problem: exponential in length of Y

CIS 391 - Intro to AI - Fall 2008 8

Example: A Vacuum-cleaner agent

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

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

A B

CIS 391 - Intro to AI - Fall 2008 9

Vacuum-cleaner world: agent function

CIS 391 - Intro to AI - Fall 2008 10

Rational agents II

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.

A performance measure for a vacuum-cleaner agent might include one or more of:• +1 point for each clean square in time T• +1 point for clean square, -1 for each move• -1000 for more than k dirty squares

CIS 391 - Intro to AI - Fall 2008 11

Rationality is not omniscience Ideal agent: maximizes actual performance, but

needs to be omniscient. • Usually impossible…..

— But consider tic-tac-toe agent…

• Rationality Success

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)

CIS 391 - Intro to AI - Fall 2008 12

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

CIS 391 - Intro to AI - Fall 2008 13

PEAS: Specifying an automated taxi driver

Performance measure: • ?

Environment: • ?

Actuators: • ?

Sensors: • ?

CIS 391 - Intro to AI - Fall 2008 14

PEAS: Specifying an automated taxi driver

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

Environment: • ?

Actuators: • ?

Sensors: • ?

CIS 391 - Intro to AI - Fall 2008 15

PEAS: Specifying an automated taxi driver

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

Environment: • roads, other traffic, pedestrians, customers

Actuators: • ?

Sensors: • ?

CIS 391 - Intro to AI - Fall 2008 16

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: • ?

CIS 391 - Intro to AI - Fall 2008 17

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

CIS 391 - Intro to AI - Fall 2008 18

PEAS: Another example

Agent: Medical diagnosis system

Performance measure: Healthy patient, minimize costs, lawsuits

Environment: Patient, hospital, staff

Actuators: Screen display (form including: questions, tests, diagnoses, treatments, referrals)

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

CIS 391 - Intro to AI - Fall 2008 19

Environment types: Definitions I Fully observable (vs. partially observable): An agent's

sensors give it access to the complete state of the environment at each point in time.

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)

Episodic (vs. sequential): The agent's experience is divided into atomic "episodes" during which the agent perceives and then performs a single action, and the choice of action in each episode depends only on the episode itself.

CIS 391 - Intro to AI - Fall 2008 20

Environment types: Definitions II 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.

(See examples in AIM, however I don’t agree with some of the judgments)

CIS 391 - Intro to AI - Fall 2008 21

Agent types

Goal of AI: • given a PEAS task environment,

• construct agent function f, • design an agent program that implements f on a

particular architecture

Four basic agent types in order of increasing generality:• Simple reflex• Model-based reflex• Goal-based• Utility-based

CIS 391 - Intro to AI - Fall 2008 22

Simple reflex agent

function REFLEX_VACUUM_AGENT( percept )

returns an action

(location,status) = UPDATE_STATE( percept )

if status = DIRTY then return SUCK;

else if location = A then return RIGHT;

else if location = B then return LEFT;

CIS 391 - Intro to AI - Fall 2008 23

Model-based reflex agents

New

CIS 391 - Intro to AI - Fall 2008 24

Goal-based agents

New

CIS 391 - Intro to AI - Fall 2008 25

Utility-based agents

New

CIS 391 - Intro to AI - Fall 2008 26

Learning agents incorporate others

Any other agent!