Syllabus Winter 2015/2016

GMAP 368 / DIGM T580: Artificial Intelligence in Gaming

Winter Term 2015-2016

Meeting Time: Mondays 9:00 – 11:50, URBN Center 250

Instructor: Prof. Stefan Rank [stefan.rank (AT) drexel.edu]

Office Hours: by appointment

Course Description

This course covers common AI techniques used in different game genres and provides students an opportunity to gain experience by investigating and prodding existing tools and games. Apart from covering common control methods for Non Player Characters (NPCs), the course will help you to understand the potential and the limitations of different AI methods, giving you the means to decide when to use simpler methods and when to go deeper into traditional or recent AI techniques, based on the design goals of a particular game.

The main purpose of this course is to prepare students with the knowledge to assess the usefulness of different AI methods, and to provide opportunities for gaining practical experience with a selection of AI tools and methods. The choice of skill specialization, or research in the case of graduate students, is up to you.

Learning Objectives

  • Understand AI methods commonly used in Gaming

  • Understand Artificial Intelligence as a scientific discipline vs. AI in gaming

  • Enable the assessment of AI methods based on design goals of a game

  • Develop skills in or in-depth knowledge about a chosen field of AI

Format

Classes will be a combination of presentations, class discussions, individual and group assignments, and lab. Class participation is an important part of your evaluation and grade. In addition, students will be required to work outside of class, doing research online and in books and journals, and hands-on exposure to various tools.

Over the whole course, you will contribute in the following ways (see also the schedule):

  • Two Topic Presentations (length TBD, about 15 minutes):

  • Present and demonstrate your chosen tool, technique or research topic to the other students.

    • In a first presentation, you will teach your colleagues the basics of the topic that they need to understand how it works.

    • In a second presentation, you will present a practical example that you put together to help others understand it. The instructor must approve your choice of topic beforehand.

    • For undergraduates, this presentation needs to be accompanied with a practical software demonstration that you have programmed, adapted, or put together yourself.

    • Graduate students can choose to focus on a more research-oriented approach and provide a package of information resources instead of a practical demonstrator.

  • One Topic Summary:

  • A report about your chosen tool, technique, or research topic.

  • Topic discussions and presentation feedback during class. Students enrolled in the class will comment on and offer feedback regarding peer presentations and assigned readings (see below).

Submission and Formatting

  • Submission of slides and related material will all be handled via email.

  • Unless otherwise noted, submissions of draft slides and demonstration code are due four days before class at 11:59 (noon) and the submission of the final version is due on the day before class at 23:59 (midnight).

  • Any submission must have your name, a date, a title, and the name and number of the course on the first page/slide.

Group Assignment

  • Group A (6):

      • Alex Hollander: path finding algorithms

      • Ryan Badurina: behavior trees

      • Matt Bodner: procedural animation of crowds, crowd behaviors, collision avoidance

      • Thomas Fox: group tactics and strategy (in an efficient way)

      • Ian Ambrose: group tactics and strategy in sports and military games

      • Michael Rodgriguez: board game AI

  • Group B (6):

      • Xavi Smith: reasoning, learning

      • Demi Barzana: audio and AI (adaptive audio, adaptive to audio input)

      • Kristina Bezouglova: personality modeling for buddy/companion characters

      • Brett Harte: emotion modeling

    • Matthew Esham: chatbots and natural language processing

      • Joseph Jalbert: procedural dialogue (with emotions)

  • Group C (6):

      • Vaishali Rajendran: procedural animation (to appear "natural")

      • KaiLin Chuang: procedural animation

      • Tom Trahey: procedural animation (characters)

      • John Frankel: procedural generation, making it fun and not too random

      • Evan Freed: procedural content generation, level generation

      • Matthew Bucher: procedural generation (levels)

Texts:

Suggested reading:

Other suggested reading material may be provided by the instructor on a weekly basis.

Suggested Topics:

    • Detailed topic suggestions.

    • Topic suggestions will be adapted after the initial course units.

    • Procedural content generation, behavior trees, speech interfaces, chatbots, natural language processing, emotion modeling, agent control architectures, embodied conversational agents, personality modeling, user modeling, finite state machines, path finding, search algorithms, intelligent sensing interfaces, experience management, procedural animation, reasoning, history of enemy AI in games, ...

Requirements and Grading Policy

    • Class Participation: Active and informed class participation is expected and mandatory. This includes participation in discussions and providing feedback to classmates.

  • Late Policy: All work must be submitted on time. Any work 1 day late will be penalized 15% and 2 days late will be penalized 30%. No work will be accepted beyond 2 days.

    • Plagiarism and Academic Integrity: For the benefit of those who may not have been told before, cheating or plagiarism are violations of both personal and academic integrity. Such violations are punishable with a failing grade on the work and may also result in a failing grade for the course and disciplinary actions on the part of the University. In this course in particular, plagiarism, including cutting and pasting from the internet without proper citation, will result in an F grade for the class

  • Students are responsible for checking their **Drexel email account** daily for course announcements. If you have any question about an assignment, please email the instructor at least **24 hours** before the deadline.

    • Two unexcused absences will constitute a loss of 20% of the final grade. 3 absences, without a valid excuse will result in an F grade.

Students receiving an “A” grade will be active and constructive participants in class discussions, deliver high-quality class presentations, and an instructive summary paper.

Grading System

  • 20 points - Class participation, active and informed, constructive criticism of peers (10 + 10, per half-term)

  • 50 points - Class Presentations including demonstration material/code, informed and informative, responsive to feedback (25 + 25)

  • 10 points - Mid-term Test

  • 20 points - Summary Paper

A+: 100-97, A: 94-96, A-: 90-93, B+: 87-89, B: 84-86, B-: 80-83,

C+: 77-79, C: 74-76, C-: 70-73, D+: 67-69, D: 60-66, F: 0-59

Schedule (may be updated during the term)

Drexel University Code of Conduct

Academic Integrity, Plagiarism, and Cheating Policy

http://drexel.edu/provost/policies/academic_dishonesty.asp

Drexel University Student Handbook

http://drexel.edu/studentaffairs/community_standards/studentHandbook/

Students with Disability Statement

http://drexel.edu/ods/student_reg.html

Course Drop Policy

http://drexel.edu/provost/policies/course_drop.asp

Course Change Policy

The instructor reserves the right to change the course during the term at his or her discretion. These changes will be communicated to students via the syllabus, website announcement, or email.