COSC 6368 --- Artificial Intelligence Fall 2016 ( Dr. Eick )

2016 Course Projects and Homeworks

2016 Course Exams and Reviews

COSC 6368 Lecture Transparencies

• 2016 Introduction to AI and Course Information COSC 6368 (will be used for first lecture) and Intelligent Agents (covered in 2016).
• 2016 Search Transparencies:
  • Search1 (Classification of Search Problems, Terminology, and Overview ), Search2 (Problem Solving Agents), Search3 (Heuristic Search and Exploration), Search4 (Randomized Hill Climbing and Backtracking; not covered in textbook), Kamil on Backtracking and Mazes, Search5 (Games; Russel transparencies for Chapter 6; will not cover transparencies that discuss card games), Search5a (Brief Discussion of Bridge and Man vs. Machine Game Contests), Search6 (Russel slides Constraint Satisifaction Problems (CSP); we will cover the first 32 slides in part), Search6a (Dhar & Quale paper on Dependency Directed Backtracking (DDBT), a "better" form of backtracking for CSPs; the idea of DDBT will be discussed in the lecture, but not the whole paper!), Search7 (Discussion of Greedy Search and A*; not covered in the lecture, but in the review in October).
  • Suggestions for Solving the Rook+King vs. King Endgame (WRKBK) Problem.
• 2016 Teaching Material Evolutionary Computing (EC): EC1: Introduction to Evolutionary Computing and EC2:Example: Using EC to Solve Travelling Salesman Problems, Eiben-Smith Introduction to EC (they call 'EC': 'EA')
• 2016 Game Theory Slides: G1: Introduction to Gametheory (USC Economics slide show by Shivendra Awasthi (???), will be used in the lecture) and G2: Mo Tanweer Mohammed's Introduction to Game Theory (not used in the COSC 6368 lecture).
• 2016 AI Planning Slides: PL1: Sketch How a STRIPS-like Planning System Solves a Block's World Problem (was done on the white board in the lecture on Tu., Oct. 6 (Sheet1, Sheet2)), PL2 (Blythe, Ambile, Gil (USC): Introduction to Planning slides, covered in the lecture), PL3 (Jussi Rintanen's ECAI 2014 Planning Tutorial; we will discuss slides 1-7, 10-14, 31, 51-52)
• 2016 Machine Learning Transparencies:
  • Quick Introduction to Machine Learning.
  • Reinforcement Learning: RL1 (Introduction to Reinforcment Learning), RL2 (Using Reinforcement Learning for Robot Soccer), RL3 (Kaelbling's RL Survey Article: read sections 1, 2, 3, 4.1, 4.2, 8.1 and 9 discussed in the lecture)
  • Decision Trees: DT1 (Dr. Eick's Introduction to Decision Trees, DT2 (Russel Decision Tree Slides; only the first 6 transparencies will be used)
  • Neural Networks: NN1 (Russel's Introduction to Neural Networks), NN2 (Dr. Eick's additional NN slides), NN3 (Java Neural Network Animation) and NN4 (Neural Network Consulter).
  • Deep Learning (nothing yet!)
• 2016 Decision Making and Reasoning in Uncertain Environment Transparencies (Under construction!)
  • Review Probability Theory
  • Dr. Eick's Transparencies on "Naive Bayesian Classifiers" (only transparencies 1 & 13 will be used in the lectures)
  • Russel's Introduction to Belief Networks (transparencies 1-6, 8-9 and 29 will be covered in class)
  • Dr. Eick's Computations in and with Belief Networks (to be covered in the lecture) Transparencies
  • Russel's Inference with Belief Networks Transparencies (will not be covered in the lecture, but give a good survey on different implementation stategies for belief networks)
  • Lesser's Introduction to Belief Networks(explains d-separation quite well, but will not be used in the lecture).
  • Slides 1997 AAAI Tutorial by Jack Breese and Daphne Koller on Belief Networks and Decision-Theoretic Reasoning (background knowledge, will not be used in the lecture)
  • Hidden Markov Models (HMM) (will be partially covered in 2016!)
• 2016 Last Words
• 2009 Logical Reasoning Transparencies:
  • Russel Intro to Logic Transparencies ( will not be used in lecture with the exception of 17-19, 21-22, 26, 29, 30; only sections 6.3 and 6.4 of the textbook will be covered in class)
  • Russel Intro to Predicate Calculus Transparencies (transpencies 1-13 will be used in lecture)
  • Russel Resolution Transparencies (to be used in lecture).
  • Dr. Eick's Introduction to Resolution (contains answers for the Sept. 23 FOPL as a language exam), and simple examples of resolution proofs (Asci) --- the resolution method will be discussed in detail in class using a lot of examples.
  • Dr. Eick's additional Transparencies Predicate Logic and Resolution(Postscript, only transparencies 2 and 4 will be covered in 2004).
  • David A. Plaisted's Propositional Approaches to First Order Logic Theorem Proving (gives a good introduction on what researchers in the area of logical reasoning do)
• 2006/2009 Soft Computing Transparencies
  • Zadeh's Definition of Soft Computing
  • Introduction to Soft Computing (just print out the first 3 pages of the pdf-file that will be used in the lecture)
  • Fuzzy Sets (just look at the first 2 transparencies)
• A quick look to Knowlege-based Systems
• Foundations of AI (quite short; to be discussed in the last class of the semester)
• 2009: Topics covered and not covered in COSC 6368.
• Dec. 7, 2004 Review for the final exam;

Prerequisites

Students are expected to have the following background:

• The COSC 4368 prerequisite will not be enforced; you can take COSC 6368 without having taken COSC 4368 or similar courses! COSC 6368 is a self-contained course: no prior knowledge of AI is required!
• Knowledge of basic computer science principles and basic programming skills. Ability to understand and analyze fairly complicated algorithms and data structures. The course will assume familiarity with such concepts as: big-O notation, queues and stacks, trees, graphs.
• Familiarity with the basic concepts of discrete probability theory. However, it suffices to read and understand Chapter 13 of Russell and Norvig which will also.
• Students should have basic programmig skills. Some assignments require programming; however, students can choose the programming language they like the most.

The prerequisites for the class are important, but only up to a point. The real prerequisite for this course is the ability to solve abstract problems, to understand nontrivial algorithms, and to have basic programming and system development skills. To some people these skills come by more easily, whereas others get them by taking the corresponding classes. If you feel that you have these skills, you can easily make up for the prerequisites on your own.

2016 COSC 6368 Student Presentations

Grading

Translation number to letter grades:
A:100-90 A-:90-86 B+:86-82 B:82-77 B-:77-74 C+:74-70
C: 70-66 C-:66-62 D+:62-58 D:58-54 D-:54-50 F: 50-0

Only machine written solutions to homeworks and project reports are accepted. Be aware of the fact that our only source of information is what you have turned in. If we are not capable to understand your approach or solution, you will receive a low score. Moreover, students should not throw away returned assignments or tests.

Students may discuss course material and homeworks, but must take special care to discern the difference between collaborating in order to increase understanding of course materials and collaborating on the homework / course project itself. We encourage students to help each other understand course material to clarify the meaning of homework problems or to discuss problem-solving strategies, but it is not permissible for one student to help or be helped by another student in working through homework problems and in the course project. If, in discussing course materials and problems, students believe that their like-mindedness from such discussions could be construed as collaboration on their assignments, students must cite each other, briefly explaining the extent of their collaboration. Any assistance that is not given proper citation may be considered a violation of the Honor Code, and might result in obtaining a grade of F in the course, and in further prosecution.

Previous Course Exams

Midterm

Final Exam

And finally: Frogland --- all about frogs