CSL452 Artificial Intelligence Spring 2016 NARAYANAN C KRISHNAN

Transcription

1 CSL452 Artificial Intelligence Spring 2016 NARAYANAN C KRISHNAN CKN@IITRPR.AC.IN

2 General Information q Course Structure o (4 credits) q Class Timings o Monday am o Tuesday am o Wednesday am q Lab hours o Thursday am q Teaching Assistant o TBA q Office hours o Instructor only through prior appointment by q Course google group o csl452s16@iitrpr.ac.in q Pre-registered students have already been added. q Pseudonym o 5 character o Jan 8 th, 5.00pm 06/01/16 CSL452 - ARTIFICIAL INTELLIGENCE 2

3 Reference Material q Course Textbook o Artificial Intelligence A Modern Approach, Stuart Russell and Peter Norvig, 3 rd edition o Low price edition will suffice q Other reference materials o o AI Rich and Knight q Pre-requisites o CSL 201 Data Structures 06/01/16 CSL452 - ARTIFICIAL INTELLIGENCE 3

4 Tentative Course Schedule 06/01/16 CSL452 - ARTIFICIAL INTELLIGENCE 4

5 Learning Every Week 06/01/16 CSL452 - ARTIFICIAL INTELLIGENCE 5

6 Quizzes 30% q Almost every Thursday o am o L2 q Covers material discussed from the previous quiz till the current week q Duration 30-45m q Top 6 out of 8 will be considered towards the final grade Quiz Date Q1 14/1 Q2 21/1 Q3 4/2 Q4 11/2 Q5 17/3 Q6 23/3 Q7 8/4 Q8 15/4 06/01/16 CSL452 - ARTIFICIAL INTELLIGENCE 6

7 Labs 20% q Due every third Friday 11.55pm q Programming Assignments o start early heavy programming component q TA is available for any assistance o students are encouraged to contact the TA for clarifications regarding the labs q Programming language o python/c/c++/java Lab Date L1 29/1 L2 19/2 L3 11/3 L4 1/4 L5 22/4 06/01/16 CSL452 - ARTIFICIAL INTELLIGENCE 7

8 Grading Scheme q Tentative Breakup o Quizzes (6-8) 30% o Labs (5) 20% o Mid-semester exam 25% o End-semester exam 25% o Attendance Bonus - 1% Ø Attendance is not mandatory, however attendance will be taken for every class and will count towards the bonus points A student must secure an overall score of 40(out of 100) and a combined score of 60(out of 200) in the exams to pass the course. 06/01/16 CSL452 - ARTIFICIAL INTELLIGENCE 8

9 Honor Code q Unless explicitly stated otherwise, for all assignments: o Strictly individual effort o Group discussions at a high level are encouraged o You are forbidden from trawling the web for answers/code etc. q Any infraction will be dealt with in severest terms allowed. q I reserve the right to question you with regards to your submission, if I suspect any misconduct. 06/01/16 CSL452 - ARTIFICIAL INTELLIGENCE 9

10 Course Website q csl452/csl452.html q All class related material will be accessible from the webpage q Labs will be uploaded incrementally and will be notified through o Labs will be submitted only by moodle q I will not be giving any separate handouts q The pdf version of the lecture slides will be available on the class website. 06/01/16 CSL452 - ARTIFICIAL INTELLIGENCE 10

11 Introduction

12 Motivation Why study AI? What comes to your mind when you hear AI? Introduction CSL452 - ARTIFICIAL INTELLIGENCE 12

13 Introduction CSL452 - ARTIFICIAL INTELLIGENCE 13

14 Kasparov said that he sometimes saw deep intelligence and creativity in the machine's moves Introduction CSL452 - ARTIFICIAL INTELLIGENCE 14

15 HAL - Heuristic Algorithmic, capable of Speech Recognition Facial Recognition Natural Language Processing Lip Reading Art Appreciation Reproducing emotional behavior Reasoning Playing chess Introduction CSL452 - ARTIFICIAL INTELLIGENCE 15

16 Introduction CSL452 - ARTIFICIAL INTELLIGENCE 16

17 Introduction CSL452 - ARTIFICIAL INTELLIGENCE 17

18 Introduction CSL452 - ARTIFICIAL INTELLIGENCE 18

19 What is AI? q What do you think? Introduction CSL452 - ARTIFICIAL INTELLIGENCE 19

20 Definition of AI Thinking humanly Acting humanly Thinking rationally Acting rationally Introduction CSL452 - ARTIFICIAL INTELLIGENCE 20

21 Definition of AI Thinking humanly Acting humanly Thinking rationally Acting rationally q Acting Humanly o Turing test o Is it sufficient to imitate a human (living being)? Introduction CSL452 - ARTIFICIAL INTELLIGENCE 21

22 Definition of AI Thinking humanly Acting humanly Thinking rationally Acting rationally q Thinking humanly o Model human thinking process o Requires scientific theories of internal activities of the human brain o Cognitive Science, Cognitive Neuroscience q A machine that thinks like human while solving a problem correctly. Introduction CSL452 - ARTIFICIAL INTELLIGENCE 22

23 Definition of AI Thinking humanly Acting humanly q Thinking Rationally Thinking rationally Acting rationally q Laws of Thought o Aristotle right thinking o Belief that logic governs the human thought process q Knowledge is not always 100% certain q What is the goal? What is purpose of thinking? Introduction CSL452 - ARTIFICIAL INTELLIGENCE 23

24 Definition of AI Thinking humanly Acting humanly q Acting Rationally q rational behavior = doing the right thing q Encompasses the other lines of thought. o Thinking rationally will help to act rationally, but is not the only means; Eg: Reflex q Agent: an entity that perceives and acts q Goal: building rational agents Thinking rationally Acting rationally Introduction CSL452 - ARTIFICIAL INTELLIGENCE 24

25 Intelligent Agents

26 Definition of AI Thinking humanly Acting humanly Thinking rationally Acting rationally q Acting Rationally q rational behavior = doing the right thing q Encompasses the other lines of thought. o Thinking rationally will help to act rationally, but is not the only means; Eg: Reflex q Goal: building rational agents Intelligent Agents CSL452 - ARTIFICIAL INTELLIGENCE 26

27 Agent Environment Perception Action Agent What should I do next? Intelligent Agents CSL452 - ARTIFICIAL INTELLIGENCE 27

28 Agent Functions and Program q Agent behavior is described by the agent function that maps percept sequences to actions. q Lookup Table An action for every possible percept sequence. q Agent Program: realization/concrete implementation of the agent function within some physical system. Intelligent Agents CSL452 - ARTIFICIAL INTELLIGENCE 28

29 Vacuum World Intelligent Agents CSL452 - ARTIFICIAL INTELLIGENCE 29

30 Rational Agents q A rational agent does the right thing(action) q Without loss of generality, goals specifiable by performance measure defining a numerical value for any environment history q Rational Action: that maximizes the expected value of the performance measure given the percept sequence to date and prior knowledge q Rationality Omniscience q Rationality Successful q Rationality Clairvoyant q Rationality Intentionally no Sensing Intelligent Agents CSL452 - ARTIFICIAL INTELLIGENCE 30

31 PEAS Specifying the Task Environment q Must specify the task environment as fully as possible Task Environment for automated taxi driver? o Performance o Environment o Actuator o Sensors Intelligent Agents CSL452 - ARTIFICIAL INTELLIGENCE 31

32 PEAS Specifying the Task Environment q Must specify the task environment as fully as possible Task Environment for automated taxi driver? o Performance- safe, fast, comfortable o Environment-roads, other traffic, traffic signals o Actuator-steering, accelerator, brake, horn, signal o Sensors-video camera, IR sensor, GPS, odometer Intelligent Agents CSL452 - ARTIFICIAL INTELLIGENCE 32

33 PEAS Specifying the Task Environment q How does the following affect the complexity of the problem the rational agent faces? o Performance complex goals makes performance harder to achieve? o Environment o Actuator Lack of effectors makes performance harder to achieve? o Sensors Lack of percepts makes performance harder to achieve? Intelligent Agents CSL452 - ARTIFICIAL INTELLIGENCE 33

34 Properties of the Task Environment Partially vs. Fully Observable Environment Static vs. Dynamic Perception Discrete vs. Continuous Full vs. Partial Satisfaction Single vs. Multiple Agents Episodic vs. Sequential Deterministic vs. Stochastic Action Instantaneous vs. Durative Agent What should I do next? Intelligent Agents CSL452 - ARTIFICIAL INTELLIGENCE 34

35 Properties of the Task Environment q Observable: The agent can sense its environment o best: fully observable worst: unobservable typical: partially observable q Deterministic: The actions have predictable effects o best: deterministic worst: non-deterministic typical: stochastic q Static: The world does not change when the agent is deciding on what to do next o best: static worst: dynamic typical: quasi-static q Episodic: The performance of the agent is determined episodically o best: episodic worst: non-episodic q Discrete: The environment evolves through a discrete set of states o best: discrete worst: continuous typical: hybrid q Agents: # of agents in the environment; are they competing or cooperating? Intelligent Agents CSL452 - ARTIFICIAL INTELLIGENCE 35

36 Task Environment-Examples Environment Observable Deterministic Static Episodic Discrete # Agents Chess Intelligent Agents CSL452 - ARTIFICIAL INTELLIGENCE 36

37 Task Environment-Examples Environment Observable Deterministic Static Episodic Discrete # Agents Chess Fully Deterministic Semi Sequential Discrete Multi Intelligent Agents CSL452 - ARTIFICIAL INTELLIGENCE 37

38 Task Environment-Examples Environment Observable Deterministic Static Episodic Discrete # Agents Chess Fully Deterministic Semi Sequential Discrete Multi Poker Intelligent Agents CSL452 - ARTIFICIAL INTELLIGENCE 38

39 Task Environment-Examples Environment Observable Deterministic Static Episodic Discrete # Agents Chess Fully Deterministic Static Sequential Discrete Multi Poker Partial Stochastic Static Sequential Discrete Multi Intelligent Agents CSL452 - ARTIFICIAL INTELLIGENCE 39

40 Task Environment-Examples Environment Observable Deterministic Static Episodic Discrete # Agents Chess Fully Deterministic Semi Sequential Discrete Multi Poker Partial Stochastic Static Sequential Discrete Multi Taxi-Driving Intelligent Agents CSL452 - ARTIFICIAL INTELLIGENCE 40

41 Task Environment-Examples Environment Observable Deterministic Static Episodic Discrete # Agents Chess Fully Deterministic Semi Sequential Discrete Multi Poker Partial Stochastic Static Sequential Discrete Multi Taxi-Driving Partial Stochastic Dynam ic Sequential Continuo us Multi Intelligent Agents CSL452 - ARTIFICIAL INTELLIGENCE 41

42 Task Environment-Examples Environment Observable Deterministic Static Episodic Discrete # Agents Chess Fully Deterministic Semi Sequential Discrete Multi Poker Partial Stochastic Static Sequential Discrete Multi Taxi-Driving Partial Stochastic Dynam ic Medical- Diagnosis Sequential Continuo us Multi Intelligent Agents CSL452 - ARTIFICIAL INTELLIGENCE 42

43 Task Environment-Examples Environment Observable Deterministic Static Episodic Discrete # Agents Chess Fully Deterministic Semi Sequential Discrete Multi Poker Partial Stochastic Static Sequential Discrete Multi Taxi-Driving Partial Stochastic Dynam ic Medical- Diagnosis Partial Stochastic Dynam ic Sequential Sequential Continuo us Continuo us Multi Multi Intelligent Agents CSL452 - ARTIFICIAL INTELLIGENCE 43

44 Task Environment-Examples Environment Observable Deterministic Static Episodic Discrete # Agents Chess Fully Deterministic Semi Sequential Discrete Multi Poker Partial Stochastic Static Sequential Discrete Multi Taxi-Driving Partial Stochastic Dynam ic Medical- Diagnosis Image Analysis Partial Stochastic Dynam ic Sequential Sequential Continuo us Continuo us Multi Multi Intelligent Agents CSL452 - ARTIFICIAL INTELLIGENCE 44

45 Task Environment-Examples Environment Observable Deterministic Static Episodic Discrete # Agents Chess Fully Deterministic Semi Sequential Discrete Multi Poker Partial Stochastic Static Sequential Discrete Multi Taxi-Driving Partial Stochastic Dynam ic Medical- Diagnosis Image Analysis Partial Stochastic Dynam ic Sequential Sequential Continuo us Continuo us Fully Deterministic Static Episodic Continuo us Multi Multi Single Intelligent Agents CSL452 - ARTIFICIAL INTELLIGENCE 45

46 Task Environment-Examples Environment Observable Deterministic Static Episodic Discrete # Agents Chess Fully Deterministic Semi Sequential Discrete Multi Poker Partial Stochastic Static Sequential Discrete Multi Taxi-Driving Partial Stochastic Dynam ic Medical- Diagnosis Image Analysis Partial Stochastic Dynam ic Fully Deterministic Dynam ic Sequential Sequential Episodic Continuo us Continuo us Continuo us The real world is partially observable, stochastic, dynamic and continuous How do we handle it then? Multi Single Single Intelligent Agents CSL452 - ARTIFICIAL INTELLIGENCE 46

47 Types of Agents q Types of agents (increasing in generality and ability to handle complex environments) o Simple reflex agents o Model based reflex agents o Goal-based agents o Utility-based agents o Learning agents Intelligent Agents CSL452 - ARTIFICIAL INTELLIGENCE 47

48 Simple Reflex Agents Intelligent Agents CSL452 - ARTIFICIAL INTELLIGENCE 48

49 Vacuum World Intelligent Agents CSL452 - ARTIFICIAL INTELLIGENCE 49

50 Model Based Reflex Agents State Estimation Intelligent Agents CSL452 - ARTIFICIAL INTELLIGENCE 50

51 Goal Based Agents Search/Planning State Estimation Search: process of looking for a sequence of actions that reaches the goal state Planning: can be viewed as search in a structured environment. Intelligent Agents CSL452 - ARTIFICIAL INTELLIGENCE 51

52 Utility Based Agents Utility function: internalization of the performance measure Conflicting goals Multiple uncertain goals Decision theoretic planning Intelligent Agents CSL452 - ARTIFICIAL INTELLIGENCE 52

53 Learning Agents Intelligent Agents CSL452 - ARTIFICIAL INTELLIGENCE 53