CSE 571 11147 Artificial Intelligence MW 3 15

CSE 571 (11147) Artificial Intelligence (MW 3: 15 – 4: 30 PM, ECA A 219) Instructor: Chitta Baral Office hours: Tuesday 2 to 5 PM

Meaning of the word: ``intelligence'' • 1 (a) The capacity to acquire and apply knowledge. (b) The faculty of thought and reason. (c) Superior powers of mind. See Synonyms at mind. • 2 An intelligent, incorporeal being, especially an angel. • 3 Information; news. See Synonyms at news. • 4 (a) Secret information, especially about an actual or potential enemy. (b) An agency, staff, or office employed in gathering such information. (c) Espionage agents, organizations, and activities considered as a group • Source: The American Heritage® Dictionary of the English Language, Fourth Edition Copyright © 2000 by Houghton Mifflin Company. Published by Houghton Mifflin Company. All rights reserved.

Meaning of the word: ``intelligence'' • n • 1: • • • the ability to comprehend; to understand profit from experience [ant: stupidity] 2: a unit responsible for gathering and interpreting intelligence 3: secret information about an enemy (or potential enemy); "we sent out planes to gather intelligence on their radar coverage" 4: new information about specific and timely events; "they awaited news of the outcome" [syn: news, tidings, word] 5: the operation of gathering information about an enemy [syn: intelligence activity, intelligence operation] Source: Word. Net ® 1. 6, © 1997 Princeton University

Artificial Intelligence • Based on the above, `artificial intelligence' is about the science and engineering necessary to create artifacts that can – acquire knowledge, i. e. , can learn and extract knowledge; and – reason with knowledge (leading to doing tasks such as planning, explaining, diagnosing, acting rationally, etc. ),

Two main parts of this course • Knowledge representation, reasoning (and declarative problem solving) – 60% from the text book -- Aug 25 th to Oct 22 – 10% from the book `Causality' by Judea Pearl and papers by Judea Pearl and Joe Halpern; and on Bayes' nets -- Oct 29 th, Nov 3, 5 • Learning – 15% on learning logical rules such as Progol, FOIL etc -- Nov 10, 12, 17; – 15% on learning Bayes' nets, causal structures etc. -Nov 19 -26, Dec 1

Syllabus from the text book • Chapter 1 (Sections 1. 1 -1. 3). • Chapter 2 • Chapter 3 (Sections 3. 1, 3. 1. 1 -3. 1. 3, 3. 1. 5, 3. 2. 1, 3. 2. 4, 3. 4. 1) • Chapter 4 • Chapter 5 (Sections 5. 1 -5. 4, 5. 6) • Chapter 8 (Sections 8. 1 -8. 3) • Time line: – – • Aug 25, 27, Sept 3, 8 -- Ch 1, Ch 8 (Smodels and DLV syntax) Sept 10, 15, 17, 22 -- Ch 2 and 3 Sept 24, 29, Oct 1 -- Ch 4 Oct 6, 8, 13, 15, 20, 22 -- Ch 5, some of Ch 8 Several papers for the other parts (to be listed)

Grading • Two tests (No finals) 30% – Test dates (Test 1 -- Oct 27; Test 2 -- Dec 3) • One project 30% (Due by Dec 5 th, demo during Dec 1 -5) – – – • • • Sept 29 th -- I will suggest a list of ideas Start discussing mine and other ideas by Oct 1 st Finalize project proposal by Oct 15 th; First Status report Nov 5 th Second Status report Nov 17 th Homework & programming assignments 20% Class notes (making slides -- due in 7 days) 10% Class participation 10% – percentage of classes you have asked non-trivial question and answered questions; attendance will be taken in every class; – coming late after the attendance has been taken will result in being marked absent and will count negatively. • first class disruption -- arriving late or a similar activity - without prior permission will count -1% of the grade; the next one -2%; and so on. )

Modus Operandi • Students will be assigned material to read. • They have to come prepared to the class where I will ask questions and clarify things. • This will happen during the first 60 minutes of the class. In the last 15 minutes of the class I will motivate the content to be discussed in the next class. • Each class one student will take notes and make slides.