Fall 2017: CS 4710 - Artificial Intelligence

Table of Contents

Course Description

Introduces artificial intelligence. Covers fundamental concepts and techniques and surveys selected application areas. Core material includes state space search, logic, and resolution theorem proving. Application areas may include expert systems, natural language understanding, planning, bayesian networks, Markov models, multi-agent systems, machine learning, or machine perception. Provides exposure to AI implementation methods.

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Basic Information


The syllabus is available here.

Time and Location

Mondays and Wednesdays, 2:00pm – 3:15pm, 341 Mechanical Engineering Building.

Contact Information

Please see here.

Office Hours

I hold my office hours in 414 Rice Hall.

Exceptions to the Regular Schedule of Office Hours

If you want to meet me outside of my office hours, the most reliable method is to send an email and arrange an appointment.

For possible exceptions to the regular office hours that the TAs hold, please see exceptions for TAs below.

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Teaching Assistants

The following undergraduate students, listed alphabetically by last name,

Office Hours

Teaching assistants hold their office hours in room 436 Rice Hall. A tentative schedule is shown below starting from the week commencing on Monday, September 4, 2017.

(Brittany)(Alyson)(Dimitris @ 414RH)(Akshay)
(Dimitris @ 414RH)
Exceptions to the Regular Schedule of Office Hours for Teaching Assistants

Friday, 8 Sep, 11am-1pm: Moving earlier to Tuesday, 5 Sep, 12.30pm-2.00pm, 436 Rice Hall.

Friday, 15 Sep, 11am-1pm: Moving earlier to Tuesday, 12 Sep, 12.30pm-2.00pm, 436 Rice Hall.

Wednesday, September 20, 11am-1pm: Only one hour will be held between 11am-12pm (same place: 436 Rice Hall).

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Assigned Readings

[Assigned Readings] [Table of Contents] [Top]

Optional Readings

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Homework Assignments

Assignments will be announced through Collab as we make progress. There will be 4-5 assignments worth 50% of your grade.

Assignment 1: Announced Monday, September 4. Due Friday, September 22.

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Class Log

Class 1 (Aug 23, 2017)

Introduction to the course. Syllabus and policies.

Assigned reading: Computer Science as Empirical Inquiry: Symbols and Search, by Allen Newell and Herbert A. Simon

Class 2 (Aug 28, 2017)

Introduction to artificial intelligence (AI). Typical problems and approaches. Different sub-fields of AI and philosophical foundations and concerns.

Relevant material from the book: Sections 1.1 and 1.2 as well as 26.1 and 26.2. Also, Section 1.4 is a one-page describing state of the art achievements in AI.

Class 3 (Aug 30, 2017)

Introduction to knowledge representation. different ways in which we can represent knowledge. Two main approaches: logic-based and associations using semantic networks (primarily expressed using frames).

Class 4 (Sep 4, 2017)

Announcing the first homework assignment through Collab; due: Friday, September 22, 2017.

Mention the Piazza forum.

Brief review on discrete mathematics: predicate logic, connectives, conditionals, functional propositions, quantifiers, inference using modus ponens. Example with a rule-based system.

Deriving new knowledge using forward chaining. Discussed conflict resolution. Reason maintenance.

Querying using backward chaining and obtaining explanations.

CLIPS and examples on CLIPS.

Overview of expert systems' architecture. Automatic knowledge acquisition. MYCIN as an example of an expert system.

Relevant material from the book: Sections 2.1 and 2.2. From Chapter 7 up to Section 7.5.1 where modus ponens is discussed. However note that it might be more convenient to refer to your personal resource on discrete mathematics for such a review, so this is only indicative.

Class 5 (Sep 6, 2017)

Classical search. Breadth-first search (BFS) and depth-first search (DFS). Depth-limited search, DFS with Iterative Deepening.

Relevant material from the book: Sections 3.3 - 3.4.5.

Optional reading: The Science of Brute Force, by Marin J.H. Heule and Oliver Kullmann.

Class 6 (Sep 11, 2017)

Heuristic search. Best-first search, greedy search, and A* (A-star) search.

Discussion on admissible heuristics, consistent heuristics, and optimality of A*.

Relevant material from the book: Sections 3.4.4 - 3.4.5 and 3.4.7. Further, Sections 3.5 - 3.5.2. Section 3.6 is optional as it has a discussion on heuristics.

Class 7 (Sep 13, 2017)

Take-home midterm exam.

Class 8 (Sep 18, 2017)

Local search methods.

Random walk and hill climbing. One vs several starting points.

Simulated annealing: using temperature to shift between random walk and hill climbing.

May discuss introductory notions on genetic algorithms or other models based on evolution.

Relevant material from the book: Sections 4 - 4.1.3. Note that Section 4.1.3 is about local beam search and it has an optional interesting discussion for that method.

Class 9 (Sep 20, 2017)

A Glimpse on Evolutionary Algorithms.

Class 10 (Sep 25, 2017)


Class 11 (Sep 27, 2017)


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