TAMU CSCE 420 - chapter02 (27 pages)

Previewing pages 1, 2, 3, 25, 26, 27 of 27 page document View the full content.
View Full Document

chapter02



Previewing pages 1, 2, 3, 25, 26, 27 of actual document.

View the full content.
View Full Document
View Full Document

chapter02

36 views


Pages:
27
School:
Texas A&M University
Course:
Csce 420 - Artificial Intelligence
Artificial Intelligence Documents
Unformatted text preview:

Intelligent Agents Chapter 2 Chapter 2 1 Reminders Assignment 0 lisp refresher due 1 28 Lisp emacs AIMA tutorial 11 1 today and Monday 271 Soda Chapter 2 2 Outline Agents and environments Rationality PEAS Performance measure Environment Actuators Sensors Environment types Agent types Chapter 2 3 Agents and environments sensors percepts environment actions agent actuators Agents include humans robots softbots thermostats etc The agent function maps from percept histories to actions f P A The agent program runs on the physical architecture to produce f Chapter 2 4 Vacuum cleaner world A B Percepts location and contents e g A Dirty Actions Lef t Right Suck N oOp Chapter 2 5 A vacuum cleaner agent Percept sequence A Clean A Dirty B Clean B Dirty A Clean A Clean A Clean A Dirty Action Right Suck Lef t Suck Right Suck 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 What is the right function Can it be implemented in a small agent program Chapter 2 6 Rationality Fixed performance measure evaluates the environment sequence one point per square cleaned up in time T one point per clean square per time step minus one per move penalize for k dirty squares A rational agent chooses whichever action maximizes the expected value of the performance measure given the percept sequence to date Rational 6 omniscient percepts may not supply all relevant information Rational 6 clairvoyant action outcomes may not be as expected Hence rational 6 successful Rational exploration learning autonomy Chapter 2 7 PEAS To design a rational agent we must specify the task environment Consider e g the task of designing an automated taxi Performance measure Environment Actuators Sensors Chapter 2 8 PEAS To design a rational agent we must specify the task environment Consider e g 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 video accelerometers gauges engine sensors keyboard GPS Chapter 2 9 Internet shopping agent Performance measure Environment Actuators Sensors Chapter 2 10 Internet shopping agent Performance measure price quality appropriateness efficiency Environment current and future WWW sites vendors shippers Actuators display to user follow URL fill in form Sensors HTML pages text graphics scripts Chapter 2 11 Environment types Solitaire Backgammon Internet shopping Taxi Observable Deterministic Episodic Static Discrete Single agent Chapter 2 12 Environment types Observable Deterministic Episodic Static Discrete Single agent Solitaire Yes Backgammon Yes Internet shopping No Taxi No Chapter 2 13 Environment types Observable Deterministic Episodic Static Discrete Single agent Solitaire Yes Yes Backgammon Yes No Internet shopping No Partly Taxi No No Chapter 2 14 Environment types Observable Deterministic Episodic Static Discrete Single agent Solitaire Yes Yes No Backgammon Yes No No Internet shopping No Partly No Taxi No No No Chapter 2 15 Environment types Observable Deterministic Episodic Static Discrete Single agent Solitaire Yes Yes No Yes Backgammon Yes No No Semi Internet shopping No Partly No Semi Taxi No No No No Chapter 2 16 Environment types Observable Deterministic Episodic Static Discrete Single agent Solitaire Yes Yes No Yes Yes Backgammon Yes No No Semi Yes Internet shopping No Partly No Semi Yes Taxi No No No No No Chapter 2 17 Environment types Observable Deterministic Episodic Static Discrete Single agent Solitaire Backgammon Internet shopping Taxi Yes Yes No No Yes No Partly No No No No No Yes Semi Semi No Yes Yes Yes No Yes No Yes except auctions No The environment type largely determines the agent design The real world is of course partially observable stochastic sequential dynamic continuous multi agent Chapter 2 18 Agent types Four basic types in order of increasing generality simple reflex agents reflex agents with state goal based agents utility based agents All these can be turned into learning agents Chapter 2 19 Simple reflex agents Agent Sensors Condition action rules What action I should do now Environment What the world is like now Actuators Chapter 2 20 Example 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 setq joe make agent name joe body make agent body program make reflex vacuum agent program defun make reflex vacuum agent program lambda percept let location first percept status second percept cond eq status dirty Suck eq location A Right eq location B Left Chapter 2 21 Reflex agents with state Sensors State How the world evolves What my actions do Condition action rules Agent What action I should do now Environment What the world is like now Actuators Chapter 2 22 Example function Reflex Vacuum Agent location status returns an action static last A last B numbers initially if status Dirty then defun make reflex vacuum agent with state program let last A infinity last B infinity lambda percept let location first percept status second percept incf last A incf last B cond eq status dirty if eq location A setq last A 0 setq last B 0 Suck eq location A if last B 3 Right NoOp eq location B if last A 3 Left NoOp Chapter 2 23 Goal based agents Sensors State What the world is like now What my actions do What it will be like if I do action A Goals What action I should do now Agent Environment How the world evolves Actuators Chapter 2 24 Utility based agents Sensors State What the world is like now What my actions do What it will be like if I do action A Utility How happy I will be in such a state What action I should do now Agent Environment How the world evolves Actuators Chapter 2 25 Learning agents Performance standard Sensors Critic changes Learning element knowledge Performance element learning goals Environment feedback Problem generator Agent Actuators Chapter 2 26 Summary Agents interact with environments through actuators and sensors The agent function describes what the agent does in all circumstances The performance measure evaluates the environment sequence A perfectly rational agent maximizes expected performance Agent programs implement some agent functions PEAS descriptions define task environments Environments are categorized along several


View Full Document

Access the best Study Guides, Lecture Notes and Practice Exams

Loading Unlocking...
Login

Join to view chapter02 and access 3M+ class-specific study document.

or
We will never post anything without your permission.
Don't have an account?
Sign Up

Join to view chapter02 and access 3M+ class-specific study document.

or

By creating an account you agree to our Privacy Policy and Terms Of Use

Already a member?