Intelligent Agents

CPS 4801-01

Outline• Agents and environments• PEAS (Performance measure, Environment,

Actuators, Sensors)• Environment types• Agent types

Agents• An AI program = An intelligent Agent• An agent is anything that can be viewed as

perceiving its environment though sensors and acting upon that environment though actuators.

• Perception-Action Cycle

Application of Intelligent Agents

• AI has successfully been used ino Finance?o Robotics?o Games?o Medicine?o The Web?

AI in Finance

Stock Market

Bonds

Commodities Market

Trading Agent

Rates

News

Trades

AI in Robotics

• Physical actuators

Environment

RobotCamerasMicrophonesTouch

Motors: wheels, legs, arms, grippers Voice

AI in Games• Chess game

• Characters in games

You

Game Agent Yourmoves

Its own moves

AI in Medicine

YouDiagnostic Agent

Vital Signals: blood pressures, heart signals

Diagnostics

Doctor

• Simple Diagnostic Expert System:• http://familydoctor.org/familydoctor/en/health-tool

s/search-by-symptom.html

AI and the Web

World Wide Web

Crawler Web Pages

DB

YouQuery

• Percept: the agent’s perceptual inputs at any given instant

• Percept Sequence: the complete history of everything the agent has ever perceived

• The agent function maps from percept histories to actions: [f: P* A] (abstract)

• The agent program runs on the physical architecture to produce f. (implementation)

Vacuum-Cleaner World

• Percepts: location and contents, e.g., [A, Dirty]• Actions: Left, Right, Suck, NoOp

A Vacuum-Cleaner Agent

Percept sequence Action[A, Clean] Right[A, Dirty] Suck[B, Clean] Left[B, Dirty] Suck[A, Clean] [A, Clean] Right[A, Clean] [A, Dirty] Suck… …[A, Clean] [A, Clean] [A, Clean] Right[A, Clean] [A, Clean] [A, Dirty] Suck… …

A Vacuum-Cleaner Agent

• function REFLEX-VACUUM-AGENT ([location,status]) returns an action

if status = Dirty then return Suck else if location = A then return Right else if location = B then return Left

Task Environment• PEAS: Performance measure, Environment,

Actuators, Sensors• Consider the task of designing an automated taxi:• Performance measure: safety, destination,

profits, legality, comfort…• Environment: US streets/freeways, traffic,

pedestrians, weather…• Actuators: steering, accelerator, brake, horn,

speaker/display…• Sensors: camera, sonar, GPS, odometer, engine

sensor…

Internet Shopping Agent

• Performance measure?• Environment?• Actuators?• Sensors?

Internet Shopping Agent

• Performance measure? price, quality, appropriateness, efficiency

• Environment? WWW sites, vendors, shippers• Actuators? display to user, follow URL, fill in

form• Sensors? HTML pages (text, graphics, scripts)

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.o Card game vs. poker (needs internal memory)

• Deterministic (vs. stochastic): The next state of the environment is completely determined by the current state and the action executed by the agent.o Chess vs. game with dice (uncertainty,

unpredictable)• 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.o Chess and taxi driving

Environment Types• Static (vs. dynamic): The environment is unchanged

while an agent is deliberation. (The environment is semidynamic if the environment itself does not change with the passage of time but the agent’s performance score does.)o Taxi driving vs. chess (when played with a clock) vs.

crossword puzzles• Discrete (vs. continuous): A limited number of distinct,

clearly defined percepts and actions.o Chess vs. taxi driving (infinite)

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

Solitaire Chess with a clock

Internet Shopping

Taxi

Observable?Deterministic?Episodic?Static?Discrete?Single-agent?

Solitaire Chess with a clock

Internet Shopping

Taxi

Observable? Yes Yes Yes NoDeterministic?

Yes Yes No No

Episodic? No No No NoStatic? Yes Semi Semi NoDiscrete? Yes Yes Yes NoSingle-agent?

Yes No Yew No

The real world is (of course) partially observable, stochastic, sequential, dynamic, continuous, multi-agent.

Agent Types• Four basic types:• - simple reflex agents• - model-based reflex agents • - goal-based agents• - utility-based agents

• All these can be turned into learning agents.

Simple Reflex Agents

Model-Based Reflex Agents

Goal-Based Agents

Utility-Based Agents

Learning Agents

Summary• Agents interact with environments though

actuators and sensors.• The agent function describes what the agent does

in all circumstances.• Agent programs implement agent functions.• PEAS descriptions define task environments.• Environment are categorized along several

dimensions: o observable? deterministic? episodic? static? discrete?

single-agent?• Several basic agent architectures exist:

o reflex, reflex with state, goal-based, utility-based

Try out some intelligent agents!

• A Chatbot is a computer program designed to simulate an intelligent conversation with one or more human users via auditory or textual methods, primarily for engaging in small talk.

• ALICE: http://alice.pandorabots.com/o Won the Loebner Prize three times (in 2000, 2001 and

2004)• ELIZA: http://nlp-addiction.com/eliza/

o One of the classic chat bots, written at MIT by Joseph Weizenbaum between 1964 and 1966

• Automated online assistants