ECE457 Applied Artificial Intelligence R. Khoury (2007)Page 1 Please pick up a copy of the course syllabus from the front desk.

Slides:



Advertisements
Similar presentations
Additional Topics ARTIFICIAL INTELLIGENCE
Advertisements

Intelligent Agents Russell and Norvig: 2
Artificial Intelligence: Chapter 2
ICS-171: 1 Intelligent Agents Chapter 2 ICS 171, Fall 2009.
Intelligent Agents Chapter 2. Outline Agents and environments Agents and environments Rationality Rationality PEAS (Performance measure, Environment,
Artificial Intelligence Lecture No. 5 Dr. Asad Safi ​ Assistant Professor, Department of Computer Science, COMSATS Institute of Information Technology.
January 11, 2006AI: Chapter 2: Intelligent Agents1 Artificial Intelligence Chapter 2: Intelligent Agents Michael Scherger Department of Computer Science.
CSE 471/598, CBS 598 Intelligent Agents TIP We’re intelligent agents, aren’t we? Fall 2004.
ICS-271: 1 Intelligent Agents Chapter 2 ICS 279 Fall 09.
Properties of task environments
ICS-171: Notes 2: 1 Intelligent Agents Chapter 2 ICS 171, Fall 2005.
Intelligent Agents Chapter 2 ICS 171, Fall 2005.
Plans for Today Chapter 2: Intelligent Agents (until break) Lisp: Some questions that came up in lab Resume intelligent agents after Lisp issues.
CSE 471/598 Intelligent Agents TIP We’re intelligent agents, aren’t we? Spring 2004.
Intelligent Agents Chapter 2.
Artificial Intelligence Overview John Paxton Montana State University August 14, 2003.
Agents & Environments. © Daniel S. Weld Topics Agents & Environments Problem Spaces Search & Constraint Satisfaction Knowledge Repr’n & Logical.
Rational Agents (Chapter 2)
Rational Agents (Chapter 2)
Leroy Garcia 1.  Artificial Intelligence is the branch of computer science that is concerned with the automation of intelligent behavior (Luger, 2008).
For Wednesday Read chapter 3, sections 1-4 Homework: –Chapter 2, exercise 4 –Explain your answers (Identify any assumptions you make. Where you think there’s.
CSE 573 Artificial Intelligence Dan Weld Peng Dai
Intelligent Agents Chapter 2. Outline Agents and environments Rationality PEAS (Performance measure, Environment, Actuators, Sensors) Environment types.
Intelligent Agents. Software agents O Monday: O Overview video (Introduction to software agents) O Agents and environments O Rationality O Wednesday:
1 AI and Agents CS 171/271 (Chapters 1 and 2) Some text and images in these slides were drawn from Russel & Norvig’s published material.
Introduction to AI. H.Feili, 1 Introduction to Artificial Intelligence LECTURE 2: Intelligent Agents What is an intelligent agent?
1 Intelligent Systems Q: Where to start? A: At the beginning (1940) by Denis Riordan Reference Modern Artificial Intelligence began in the middle of the.
Learning to Play Blackjack Thomas Boyett Presentation for CAP 4630 Teacher: Dr. Eggen.
How R&N define AI Systems that think like humans Systems that think rationally Systems that act like humans Systems that act rationally humanly vs. rationally.
CNS 4470 Artificial Intelligence. What is AI? No really what is it? No really what is it?
Intelligent Agents Chapter 2 Some slide credits to Hwee Tou Ng (Singapore)
Lection 3. Part 1 Chapter 2 of Russel S., Norvig P. Artificial Intelligence: Modern Approach.
Outline Agents and environments Rationality PEAS (Performance measure, Environment, Actuators, Sensors) Environment types Agent types Artificial Intelligence.
Chapter 2 Agents & Environments. © D. Weld, D. Fox 2 Outline Agents and environments Rationality PEAS specification Environment types Agent types.
Agents CPSC 386 Artificial Intelligence Ellen Walker Hiram College.
Chapter 2 Hande AKA. Outline Agents and Environments Rationality The Nature of Environments Agent Types.
CE An introduction to Artificial Intelligence CE Lecture 2: Intelligent Agents Ramin Halavati In which we discuss.
Artificial Intelligence Lecture 1. Objectives Definition Foundation of AI History of AI Agent Application of AI.
Intelligent Agents อาจารย์อุทัย เซี่ยงเจ็น สำนักเทคโนโลยีสารสนเทศและการ สื่อสาร มหาวิทยาลัยนเรศวร วิทยาเขต สารสนเทศพะเยา.
Rational Agents (Chapter 2)
A RTIFICIAL I NTELLIGENCE Intelligent Agents 30 November
For Friday No reading (other than handout) Homework: –Chapter 2, exercises 5 and 6 –Lisp handout 1.
Instructional Objective  Define an agent  Define an Intelligent agent  Define a Rational agent  Discuss different types of environment  Explain classes.
Agents and Uninformed Search ECE457 Applied Artificial Intelligence Spring 2007 Lecture #2.5.
Rational Agency CSMC Introduction to Artificial Intelligence January 8, 2007.
Rational Agency CSMC Introduction to Artificial Intelligence January 8, 2004.
Artificial Intelligence
Intelligent Agents Chapter 2. Outline Agents and environments Rationality PEAS (Performance measure, Environment, Actuators, Sensors) Environment types.
Intelligent Agents Chapter 2. Outline Agents and environments Rationality PEAS (Performance measure, Environment, Actuators, Sensors) Environment types.
1/23 Intelligent Agents Chapter 2 Modified by Vali Derhami.
Intelligent Agents Introduction Rationality Nature of the Environment Structure of Agents Summary.
CSE 471/598 Intelligent Agents TIP We’re intelligent agents, aren’t we?
Chapter 2 Agents & Environments
CS382 Introduction to Artificial Intelligence Lecture 1: The Foundations of AI and Intelligent Agents 24 January 2012 Instructor: Kostas Bekris Computer.
Lecture 2: Intelligent Agents Heshaam Faili University of Tehran What is an intelligent agent? Structure of intelligent agents Environments.
CPSC 420 – Artificial Intelligence Texas A & M University Lecture 2 Lecturer: Laurie webster II, M.S.S.E., M.S.E.e., M.S.BME, Ph.D., P.E.
Intelligent Agents Chapter 2. How do you design an intelligent agent? Definition: An intelligent agent perceives its environment via sensors and acts.
Intelligent Agents. Outline Agents and environments Rationality PEAS (Performance measure, Environment, Actuators, Sensors) Environment types Agent types.
ECE457 Applied Artificial Intelligence R. Khoury (2007)Page 1 Please pick up a copy of the course syllabus from the front desk.
Artificial Intelligence Programming Spring 2016
ECE 448 Lecture 3: Rational Agents
Rational Agents (Chapter 2)
Artificial Intelligence Lecture No. 5
Intelligent Agents Chapter 2.
Intelligent Agents Chapter 2.
Hong Cheng SEG4560 Computational Intelligence for Decision Making Chapter 2: Intelligent Agents Hong Cheng
© James D. Skrentny from notes by C. Dyer, et. al.
Introduction to Artificial Intelligence
AI and Agents CS 171/271 (Chapters 1 and 2)
Presentation transcript:

ECE457 Applied Artificial Intelligence R. Khoury (2007)Page 1 Please pick up a copy of the course syllabus from the front desk.

Introduction to AI ECE457 Applied Artificial Intelligence Fall 2007 Lecture #1

ECE457 Applied Artificial Intelligence R. Khoury (2007)Page 3 Outline What is an AI? Russell & Norvig, chapter 1 Agents Environments Russell & Norvig, chapter 2

ECE457 Applied Artificial Intelligence R. Khoury (2007)Page 4 Artificial Intelligence Computer players in video games Robotics Assembly-line robots, auto-pilot, Mars exploration robots, RoboCup, etc. Expert systems Medical diagnostics, business advice, technical help, etc. Natural language Spam filtering, translation, document summarization, etc. Artificial intelligence is all around us

ECE457 Applied Artificial Intelligence R. Khoury (2007)Page 5 What is an AI? Systems that… Rationality vs. Humans: emotions, instincts, etc. Thinking vs. acting: Turing test vs. Searle’s Chinese room Engineers (and this course) focus mostly on rational systems HumanlyRationally Think Neural networks Theorem proving Act ELIZADeep Blue

ECE457 Applied Artificial Intelligence R. Khoury (2007)Page 6 Act Rationally Perceive the environment, and act so as to achieve one’s goal Not necessary to do the best action There’s not always an absolutely best action There’s not always time to find the best action An action that’s good enough can be acceptable Example: Game playing Sample approach: Tree-searching strategies Problem: Choosing what to do given the constraints

ECE457 Applied Artificial Intelligence R. Khoury (2007)Page 7 Think Rationally Uses logic to reach a decision or goal via logical inferences Example: Theorem proving Sample approach: First-order logic Problems: Informal knowledge Uncertainty Search space

ECE457 Applied Artificial Intelligence R. Khoury (2007)Page 8 1. X = Y/Z  XZ = Y 2. X = Y  X + Z = Y + Z 3. X * Y + X * Z  X * (Y + Z) 4. b/c = AH/b 5. a/c = BH/a 6. AH + BH = c Think Rationally a. b² = AH * c b. a² = BH * c c. a² + b² = BH * c + AH * c d. a² + b² = c * (AH + BH) e. a² + b² = c²

ECE457 Applied Artificial Intelligence R. Khoury (2007)Page 9 Act Humanly “Turing-test” AI Improve human-machine interactions up to human-human level Drawbacks: In some cases, requires dumbing down the AI Lots of man-made devices work well because they don’t imitate nature

ECE457 Applied Artificial Intelligence R. Khoury (2007)Page 10 Think Humanly Cognitive science Neural networks Helps in other fields Computer vision Natural language processing

ECE457 Applied Artificial Intelligence R. Khoury (2007)Page 11 Rational Agents An agent has Sensors to perceive its environment Actuators to act upon its environment A rational agent has an agent program that allows it to do the right action given its precepts Environment Percepts Actions SensorsActuators Agent Program

ECE457 Applied Artificial Intelligence R. Khoury (2007)Page 12 Types of Agents Simple reflex agent Selects action based only on current perception of the environment Model-based agent Keeps track of perception history Goal-based agent Considers what will happen given its actions Utility-based agent Adds the ability to choose between conflicting/uncertain goals Learning agent Adds the ability to learn from its experiences

ECE457 Applied Artificial Intelligence R. Khoury (2007)Page 13 Simple Reflex Agent Environment PerceptsActions Sensors Actuators Selected Action Current State If-then Rules

ECE457 Applied Artificial Intelligence R. Khoury (2007)Page 14 Simple Reflex Agent Dune II (1992) units were simple reflex agents Harvester rules: IF at refinery AND not empty THEN empty IF at refinery AND empty THEN go harvest IF harvesting AND not full THEN continue harvesting IF harvesting AND full THEN go to refinery IF under attack by infantry THEN squash them

ECE457 Applied Artificial Intelligence R. Khoury (2007)Page 15 Model-Based Agent Environment PerceptsActions Sensors Actuators Selected Action Current State Previous perceptions Impact of actions World changes If-then Rules

ECE457 Applied Artificial Intelligence R. Khoury (2007)Page 16 Goal-Based Agent Environment PerceptsActions Sensors Actuators Selected Action Current State Goal Previous perceptions Impact of actions World changes State if I do action X

ECE457 Applied Artificial Intelligence R. Khoury (2007)Page 17 Utility-Based Agent Environment PerceptsActions Sensors Actuators Selected Action Current State Utility Previous perceptions Impact of actions World changes State if I do action X Happiness in that state

ECE457 Applied Artificial Intelligence R. Khoury (2007)Page 18 Learning Agent Environment PerceptsActions Sensors Actuators Problem Generator Learning Element Feedback Performance standard Changes Knowledge Learning Goals Performance Element Critic

ECE457 Applied Artificial Intelligence R. Khoury (2007)Page 19 Properties of the Environment Fully observable vs. partially observable See everything vs. hidden information Chess vs. Stratego Deterministic vs. stochastic vs. strategic Controlled by agent vs. randomness vs. multiagents Sudoku vs. Yahtzee vs. chess Episodic vs. sequential Independent episodes vs. series of events Face recognition vs. chess

ECE457 Applied Artificial Intelligence R. Khoury (2007)Page 20 Properties of the Environment Static vs. dynamic vs. semi-dynamic World waits for agent vs. world goes on without agent vs. world waits but agent timed Translation vs. driving vs. chess with timer Discrete vs. continuous Finite distinct states vs. uninterrupted sequence Chess vs. driving Single agent vs. cooperative vs. competitive Alone vs. team-mates vs. opponents Sudoku vs. sport team vs. chess

ECE457 Applied Artificial Intelligence R. Khoury (2007)Page 21 Crossword Puzzle Fully observable, deterministic, sequential, static, discrete, single-agent Monopoly Fully observable, stochastic, sequential, static, discrete, competitive multi-agent Driving a car Partially observable, stochastic, sequential, dynamic, continuous, cooperative multi-agent Assembly-line inspection robot Fully observable, deterministic, episodic, dynamic, continuous, single-agent Properties of the Environment