CSL452 Artificial Intelligence Spring 2016cse.iitrpr.ac.in/ckn/courses/s2016/csl452/w1.pdf · oLabs...

CSL452 Artificial Intelligence Spring 2016 NARAYANAN C KRISHNAN [email protected]

General Information q Course Structure o 3-0-2 (4 credits)

q Class Timings o Monday -9.00-9.50am o Tuesday – 9.55-10.45am o Wednesday – 10.50-11.40am

q Lab hours o Thursday -9.00-10.45am

q Teaching Assistant o TBA

q Office hours o Instructor – only through

prior appointment by email

q Course google group o [email protected]

q Pre-registered students have already been added. q Pseudonym o 5 character o Jan 8th, 5.00pm

06/01/16 CSL452 - ARTIFICIAL INTELLIGENCE 2

Reference Material q Course Textbook o Artificial Intelligence A Modern Approach,

Stuart Russell and Peter Norvig, 3rd edition o Low price edition will suffice

q Other reference materials o http://aima.cs.berkeley.edu/ o  AI – Rich and Knight

q Pre-requisites o  CSL 201 – Data Structures


Tentative Course Schedule


Learning Every Week


Quizzes – 30% q Almost every Thursday o 9.00-10.30am 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


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


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.

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.


Course Website q http://cse.iitrpr.ac.in/ckn/courses/s2016/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 email 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.


Introduction

Motivation – Why study AI?

Introduction CSL452 - ARTIFICIAL INTELLIGENCE 12

What comes to your mind when you hear AI?


Kasparov said that he sometimes saw deep intelligence and creativity in the machine's moves


HAL - Heuristic Algorithmic, capable of •  Speech Recognition •  Facial Recognition •  Natural Language Processing •  Lip Reading •  Art Appreciation •  Reproducing emotional

behavior •  Reasoning •  Playing chess

What is AI? q What do you think?


Definition of AI


Thinking humanly Thinking rationally Acting humanly Acting rationally

Definition of AI

q Acting Humanly – o Turing test

o  Is it sufficient to imitate a human (living being)?



Definition of AI

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.



Definition of AI

q Thinking 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?



Definition of AI

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



Intelligent Agents

Definition of AI

Intelligent Agents CSL452 - ARTIFICIAL INTELLIGENCE 26

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

Thinking humanly Thinking rationally

Acting humanly Acting rationally

Agent Environment

Agent

Perc

epti

on

Action

What should I do next?

Intelligent Agents 27 CSL452 - ARTIFICIAL INTELLIGENCE

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.


Vacuum World


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


PEAS – Specifying the Task Environment q Must specify the task environment as fully as possible

o Performance

o Environment

o Actuator

o Sensors

Task Environment for automated taxi driver?


PEAS – Specifying the Task Environment q Must specify the task environment as fully as possible

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

Task Environment for automated taxi driver?


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?


Properties of the Task Environment

Environment

Agent

Perc

epti

on A

ction

What should I do next?

Static vs. Dynamic

Partially vs. Fully Observable

Deterministic vs. Stochastic

Instantaneous vs. Durative Full vs.

Partial Satisfaction

Discrete vs. Continuous

Single vs. Multiple Agents

Episodic vs. Sequential


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?

Task Environment-Examples Environment Observable Deterministic Static Episodic Discrete #

Agents Chess


Task Environment-Examples Environment Observable Deterministic Static Episodic Discrete #

Agents Chess Fully Deterministic Semi Sequential Discrete Multi


Task Environment-Examples


Environment Observable Deterministic Static Episodic Discrete # Agents

Chess Fully Deterministic Semi Sequential Discrete Multi

Poker




Chess Fully Deterministic Static Sequential Discrete Multi

Poker Partial Stochastic Static Sequential Discrete Multi






Taxi-Driving






Taxi-Driving Partial Stochastic Dynamic

Sequential Continuous

Multi








Multi

Medical-Diagnosis








Multi

Medical-Diagnosis

Partial Stochastic Dynamic


Multi








Multi

Medical-Diagnosis



Multi

Image Analysis








Multi

Medical-Diagnosis



Multi

Image Analysis

Fully Deterministic Static Episodic Continuous

Single



The real world is partially observable, stochastic, dynamic and continuous How do we handle it then?






Multi

Medical-Diagnosis



Single

Image Analysis

Fully Deterministic Dynamic

Episodic Continuous

Single

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


Simple Reflex Agents


Vacuum World


Model Based Reflex Agents


State Estimation

Goal Based Agents


State Estimation

Search/Planning

Search: process of looking for a sequence of actions that reaches the goal state Planning: can be viewed as search in a structured environment.

Utility Based Agents


•  Utility function: internalization of the performance measure •  Conflicting goals •  Multiple uncertain goals •  Decision theoretic planning

Learning Agents


CSL452 Artificial Intelligence Spring 2016cse.iitrpr.ac.in/ckn/courses/s2016/csl452/w1.pdf · oLabs...

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