1. CSE 571 (14362) Artificial Intelligence (TTh 3:15 – 4: 30 PM, BYAC 150) Instructor: Chitta Baral Office hours: TTh 4:40 to 5:30 PM

2. 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.

3. Meaning of the word: ``intelligence'' n 1: the ability to comprehend; to understand and 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: WordNet ® 1.6, © 1997 Princeton University

4. Artificial Intelligence Based on the above, `artificial intelligence' is about the science and engineering necessary to create artifacts that can – acquire knowledge, learn from experience learn from reading and processing natural language text – reason with knowledge (leading to doing tasks such as planning, explaining, diagnosing, acting rationally, etc.),

5. Two main parts of this course Knowledge representation, reasoning (and declarative problem solving) –50% from the text book –10% from the book `Causality' by Judea Pearl and papers by Judea Pearl and Joe Halpern; and on Bayes' nets Learning –10% on learning logical rules such as Progol, FOIL etc. –10% on learning Bayes' nets, causal structures etc. –20% on natural language processing, Human language technology.

6. 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, 3.2.1, 3.2.4, 3.4, 3.4.1) Chapter 4 Chapter 5 (Sections 5.1-5.4, 5.6) Chapter 8 (Sections 8.1-8.3) Time line: –4 classes -- Ch1, Ch 8 (Smodels and DLV syntax) –4 classes -- Ch 2 and 3 –3 classes -- Ch 4 –6 classes -- Ch 5, some of Ch 8 Several papers for the other parts (to be listed)

7. Grading Two tests (No finals) 30% –Test dates (Test 1 – March 24th; Test 2 -- May 3rd) –Test 2 may be rolled over to the project (need instructor permission) One project 40% (demo during May 3-6) –Develop a Question answering system on a particular domain –Decide on domain by Feb 15 th. –First Status report March 10 th –Second Status report April 21 st. Homework & programming assignments 20% Class participation 10% –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.)

8. Modus Operandi – for not non-NLP part 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 60-65 minutes of the class. In the last 10-15 minutes of the class I will motivate the content to be discussed in the next class.