LE/EECS 4401 3.0 Artificial Intelligence
GS/EECS 5326 3.0 Topics in Artificial Intelligence
Winter 2021
Department of Electrical
Engineering & Computer Science,
York University
Course Project
Proposal (worth 5% of course grade) due: March 9
Presentation (worth 10% of course grade) due: April 6 & 8
Final report (worth 20% of course grade) due: April 13 for EECS4401 and May 3 for EECS5326
This course requires doing a project in the area of artificial
intelligence, on a topic related to the material covered in the
course. The project may involve doing a literature survey, an
evaluation of a an AI solver or tool, an implementation of an AI
method, or even a research paper on an AI problem and a proposed solution.
Some topics suggestions appear below, classified according to the type
of project. Projects in EECS 4401 must be done in
teams of two or three students; in EECS 5326, the project must be individual.
For team projects, each student must be responsible for a distinct part of the
project presentation and a distinct part of the final report, which
will be graded separately (the introduction and conclusion of the report can be common).
Project Deliverables
By March 9, you should select a topic and submit a short
(one or two pages) project proposal on eClass.
The proposal should outline your
topic, state the project's objectives, give a tentative timetable for
the tasks to be accomplished, and include a list of references
(especially important for literature surveys). For implementation
projects, it is recommended that a preliminary design for the system
be included. You are encouraged to discuss your project with the
instructor in advance. You will also get feedback on your proposal.
On April 1, 6, and 8 students will give a short (approx. 15 minutes)
class presentation on their project and answer questions
(you will be given a time slot in advance).
Your final report on the project is due by April 13 for EECS4401 and April 31 for EECS5326. For
literature survey projects, include an overview of the topic area, a
discussion of the papers read, and a complete list of references;
critical analysis is expected.
For system evaluation or implementation projects, state the
project's motivation and objectives, provide a readable description
the system that was implemented or evaluated, explain how the system
is used, describe the testing that was performed, and discuss how
usable the system is; include the documented code and/or sample
tests in appendix as appropriate.
Note that it is not sufficient to simply download a solver and run
some of the examples included with it; you must either develop some
new examples or evaluate the solver on a wide range of benchmarks.
Suggested Project Topics
- Experiment with and evaluate a solver/reasoner such as for example, the
Z3 SMT solver, the
Clingo ASP solver, etc.
- Investigate the description logic languages that are
part of
OWL
and/or experiment with an ontology editor/reasoner platform such
as Protege; examine how description logics can be used for ontology based data access.
- Investigate the PDDL planning domain specification language
and/or experiment with planning systems
(see the the Haslum et al. book as well as Muise's http://planning.domains for some
domains, a classical planner, and a domain editor; Muise's site
also provides other systems for FOND planning and multiagent epistemic planning).
- Experiment with an agent programming language such as Golog (see
Reiter's book Ch 6, which also discusses a simple Prolog implementation with
some examples; more complete implementations include Levesque's
Ergo
and Sardina and Vassos's
IndiGolog)
or
Jason.
- Implement a reinforcement learning algorithm such as Q-learning or SARSA and evaluate it on some problems (see Russell and
Norvig, Ch. 21).
-
Survey recent work on a topic such as AI safety or deep reinforcement learning.