COSC-574: Automated Reasoning

Fall 2017

Contents

• Announcements
• Where, When, Who
• Description
• Learning Goals
• Policies
• Assignments and Grading
• Materials: Readings, Videos, and Links
• Schedule
• Other Interesting Links

• 12/14/17: Posted room for final exam: REI 284.
• 11/20/17: Posted p3
• 10/22/17: Posted p2
• 10/15/17: Extended the due date for p1 to 10/22 @ 5 PM.
• 9/21/17: Posted the slides for first-order semantics.
• 9/4/17: Activated Canvas site, updated date for p1, posted p1.
• 8/14/17: Set dates for assignments and exams.
• 3/30/17: Posted this Web site.

• Artificial intelligence: A modern approach, 3rd Edition, by Russell and Norvig. [ Amazon | Lauinger ].

• Logical foundations of artificial intelligence, by Genesereth and Nilsson. [ Amazon | Lauinger ].
• Probabilistic graphical models: Principles and techniques, by Koller and Friedman. [ Amazon | Lauinger ].
• Pattern recognition and machine learning, by Bishop. [ Amazon | Lauinger ].

My course policies are designed to supplement the CS Department's Honor Policy. Unless stated otherwise when I distribute an assignment, the following is the default for all assignments for this course. I've developed my policies from past teaching experiences and from the CS Department's Honor Code at George Mason University.

I am obligated to refer all suspected cases of academic dishonesty by master's students to the Honor Council. I am obligated to refer all suspected cases of academic dishonesty by doctoral students to the dean of the Graduate School. If you have any questions about these policies or how they apply, please discuss such concerns with me during class, during office hours, or by e-mail.

In my experience, students at Georgetown do honest work. The small percentage of students who have submitted someone else's work as their own did so because they did not manage their time wisely.

Students must follow proper scholarly practice for all submitted work, whether graded or ungraded and whether a draft or final version of a proposal, paper, or program. We must acknowledge our reliance on the work of others through citation.

Students may be quite adept at and knowledgeable about citing and quoting material from traditional sources, such as books and articles. Typically, we do not have cite facts, common math formulae, or expressions of our own ideas, observations, interpretations, and analyses, However, new graduate students in computer science may not realize that formulae, theorems, proofs, algorithms, and programs can require the same treatment as any other form of expression.

For convenience, you do not need to cite the course materials, conversations with me or information you obtain from class lectures and discussions. If you are unsure about what requires citation or what constitutes proper scholarly practice, please ask me during class, during office hours, or by e-mail.

I design my courses and assignments so students have what they need to complete the assignments individually without consulting outside resources. I determine the size of and credit for assignments based on the assumption that the work for them is the result of individual effort using only the course resources and materials. Students who use outside resources to complete assignments may not be eligible for full credit. Students who do not acknowledge their use of outside resources to complete assignments may be in violation of my course policies and the university's policies on academic integrity.

The following list details acceptable and unacceptable practices:

• You can:
  • obtain assistance in understanding course materials (textbooks, lecture notes, assignments);
  • obtain assistance in learning to use the computing facilities;
  • obtain assistance in learning to use special features of a programming language's implementation;
  • obtain assistance in determining the syntactic correctness of a particular programming language statement or construct;
  • obtain an explanation of a particular syntactic error;
  • obtain explanations of compilation or run-time error messages.
• You can obtain assistance only from me and the teaching assistants:
  • in designing the data structures and algorithms used in your solution;
  • in modifying the design of an algorithm or data structure determined to be faulty;
  • in implementing your algorithm or data structure in a programming language;
  • in correcting a faulty implementation of your algorithm or data structure;
  • in determining the semantic correctness of your program;
  • in designing an experimental study and interpreting its results.
• You can not:
  • show or give a copy of your work in any amount or form to another student;
  • see or receive a copy of someone else's work in any amount or form;
  • attempt to gain access to files other than your own or those that I designate and authorize;
  • attempt to reverse engineer routines used for automatic grading;
  • inspect or retain in your possession another student's work, whether it was given to you by another student, it was found after other student discarded his or her work, or it accidentally came into your possession;
  • collaborate in any way with someone else in the design, implementation, or logical revision of an algorithm;
  • use or present as your own any algorithm, data structure, or implementation that is not of your own or of my design, or which is not part of the course's required reading. If you modify any procedure which is presented in the course's texts that is not specifically mentioned in class or covered in reading assignments, then a citation with page number must be given;
  • incorporate code written by others (such as can be found on the Internet).

Policies dealing logistics:

• You have permission to occasionally take digital photos of the material on the board for your personal use, but you can not post these recordings on the Internet. It is important to understand that all of the course material is covered by copyright, either mine or someone else's.
• You should submit all assignments on time. For late projects, there will be a 1% deduction for each minute after the deadline. In the real world, it won't be your grade that decreases. It'll be your stock price.
• I grant extensions only to students who have documentation for a medical issue, a family emergency, or an accommodation from the Academic Resource Center. In the cases of a medical issue or a family emergency, it would be best to coordinate with your advising dean since these situations often affect your work in all of your classes.
• If you use your laptop for development, you should keep a backup of your projects on a university or department machine (e.g., cs-class) so I can verify that you completed the work for an assignment before the deadline.
• You must take the final exam with the section and during the period designated by the Registrar.
• It is my job to maintain a constructive learning environment for everyone. Please silence your cell phone and other electronic devices.
• If you must miss class, be sure to get the lecture notes from a classmate.
• It is fine if you must leave class early, arrive late, or leave the room to answer your phone, but you should do so in a manner that does not disturb your fellow students.
• In the case of inclement weather that results in the university's closure, we will meet virtually during normal class times using Zoom.

• Programming Projects, 50%
• Project 1, assigned M 9/4 R 8/31, due Su 10/15 Su 10/22 @ 5 PM, 20 points
• Project 2, assigned T 10/17 W 10/22, due T 11/14 @ 5 PM, 20 points
• Project 3, assigned M 11/13 11/20, due R 12/7 @ 11:59 PM, 10 points
• Midterm Exam, T 10/17, 20%
• Final Exam, T 12/19 @ 4–6 PM in REI 284, 30%

String Grades::getLetterGrade()
{
  if (grade >= 94)
    return "A";
  else if (grade >= 90)
    return "A-";
  else if (grade >= 87)
    return "B+";
  else if (grade >= 84)
    return "B";
  else if (grade >= 80)
    return "B-";
  else if (grade >= 67)
    return "C";
  else
    return "F";
} // Grades::getLetterGrade

• Autolab, for submitting projects
• Piazza, for online discussions.
• Canvas, for document distribution and submitting projects if Autolab breaks
• Dynarski, S. M. (10 August 2017). For better learning in college lectures, lay down the laptop and pick up a pen. Research Report, Brookings Institution, Washington, DC. (Read if interested.)
• Perez-Hernandez, D. (28 March 2014). Taking notes by hand benefits recall, researchers find. The Chronicle of Higher Education. (Read if interested.)
• Mueller, P. A. and Oppenheimer, D. M. (2014). The pen is mightier than the keyboard: Advantages of longhand over laptop note taking. Psychological Science, 25(6):1159–1168. (Read if interested.)
• Russell and Norvig's algorithm for uniform-cost search.
• Russell and Norvig's Unify algorithm.
• Slides: Genesereth and Nilsson's Sematics for First-Order Logic
• Robinson, J. A. (1965). A machine-oriented logic based on the resolution principle. Journal of the ACM 12.1:23–41.
• Kowalski, R. and Kuehner, D. (1971). Linear resolution with a selection function. Artificial Intelligence 2:228–260.
• Russell and Norvig's forward and backward chaining algorihtms.
• Slides: Bayesian Decision Theory.
• Bishop's Chapter 8: Graphical Models
• Barber, D. (2012). Bayesian Reasoning and Machine Learning. Cambridge University Press: Cambridge, UK.
• Russell and Norvig's variable elimination algorihtm.
• Russell and Norvig's Gibbs sampling algorithm.
• Slides: Algorithm for Probability Propagation in Trees.
• Tarjan, R. E. and Yannakakis, M. (1984). Simple linear-time algorithms to test chordality of graphs, test acyclicity of hypergraphs, and selectively reduce acyclic hypergraphs. SIAM Journal on Computing 13.3:566--579.
• Golumbic, M. C. (2004). Algorithmic graph theory and perfect graphs Elsevier: Amsterdam.
• Browne, C. et al. (2012). A survey of Monte Carlo tree search methods. IEEE Transactions on Computational Intelligence and AI in Games 4(1): 1–43.
• Slides: Monte Carlo Tree Search.
• de Salvo Braz, R., Amir, E., and Roth, D. (2008). A survey of first-order probabilistic models. In Innovations in Bayesian networks: Theory and applications, 289–317. Springer: Berlin-Heidelberg.
• Russell, S. (2015). Unifying logic and probability. Communications of the ACM 58.7:88–97.
• Nilsson, N. (1986). Probabilistic logic. Artificial Intelligence 28.1:71–87.
• Getoor, L. and Grant, J. (2006). PRL: A probabilistic relational language. Machine Learning 61.1:7–31.
• Richardson, M. and Domingos, P. (2006). Markov logic networks. Machine Learning 61.1:107–136.
• Gogate, V. and Domingos, P. (2011). Probabilistic theorem proving. In Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, 256–265. AUAI Press: Corvalis, OR.
• Domingos, P. and Web, W. A. (2012). A tractable first-order probabilistic logic. In Proceedings of the 26th National Conference on Artificial Intelligence, 1902–1909. AAAI Press: Menlo Park, CA.

• Google Scholar

Copyright © 2019 Mark Maloof. All Rights Reserved. This material may not be published, broadcast, rewritten, or redistributed.