• Instructor: David Thue
• Contact: Office in Venus floor 2, telephone 599-6412, e-mail davidthue[ ]ru.is
• Class Times: Tuesdays 10:20-11:55 (V110) & Fridays 10:20-11:55 (M113)
• Online Forum: Piazza Course Page
• Project Tracking Software: Trello
• Reading List & Schedule: Course Presentation Schedule

The ability to create and revise stories is fundamental to human interaction, but computers are still in their infancy of doing either very well. In this course, we will explore computational storytelling from the perspective of Artificial Intelligence. We will read and analyze key papers from the literature, discuss how such technologies might be applied in different domains (e.g., computer games or training simulations), and obtain hands-on experience by building prototypes that extend previous research.

There is no course textbook, but you have access to a wide range of relevant research papers online. Of specific relevance are papers from:

• Interactive Storytelling (ICIDS)
• Artificial Intelligence and Interactive Digital Entertainment (AIIDE)
• Intelligent Narrative Technologies (INT): I, II, III, IV, V, 6, 7, 8, 9, 10

All of these sites should allow you to download their papers free of charge (except perhaps INT 9).

In general, if a paper that you want to read is paywalled, you might find a preprint on the authors' personal or research group websites (use your Google skills). Failing that, you might still be able to access it by contacting the author directly, or via RU's Inter-library Loan system (M.Sc. students get a few free requests).

The course reading list is available here: Course Presentation Schedule

Upon completion of the course, students should be able to:

• Discuss current challenges in computational storytelling
• Describe a variety of algorithms and techniques for addressing those challenges
• Present and critique related research, both orally and in writing
• Pursue original research that extends the concepts discussed in class
• Write a conference-level research report
• Identify computational storytelling projects that could be pursued as thesis research

Please try to use the course discussion system (Piazza) for posting questions regarding lectures or your projects, rather than sending e-mail. That way we can build a shared repository of useful questions/answers.

Piazza Course Page

Please note that attendance during both classes each week (Tuesdays and Fridays) is required. Please inform the instructor if this is hard for you for some reason, such as scheduling conflicts or sick leave.

This course will combine presentations, discussions, and brainstorming in class with a hands-on term project.

Most classes will be dedicated to presenting and discussing topics in computational storytelling, with each topic grounded by an assigned reading and/or gameplay session. Before coming to each class, everyone will read the assigned paper or play the assigned game. During class, one student will present the assigned paper/game using slides and/or live demonstrations, and everyone else will hand in a written review of the paper/game before the presentation starts (max. 2 pages, single spaced). Guidelines for presentations and reviews will be provided.

For each paper that the class reads, each student (except its presenter) will individually write and hand in a review which (1) summarizes the paper's objectives, methods, and key contributions, (2) gives a constructive critique of its content, and (3) makes some suggestions for future work.

Each written review will receive one of three grades: 0 (missing or severely lacking), 0.5 (incomplete), or 1 (complete). Each review will be worth up to 2% of the final grade, for a total of 28% of the final grade. Of the 16 written reviews that each student will complete, the grades of two reviews that scored the lowest will be ignored. For example, for a student who scored 0 for one review, 0.5 for 3 reviews, 1 for 12 reviews, their total score out of 28% would be 12*1*2% + 2*0.5*2% = 26% (the 0 and one of the 0.5s would be ignored). Each review must be handed in before the reviewed paper is presented in class; each late review will receive a grade of zero. The presenter of a paper does not need to write a review for that paper, nor the previous paper that the class reads (to give them more time to prepare the presentation).

Each student will present two of the assigned papers and/or games, using slides (and possibly demonstrations) to: (1) describe necessary background information, (2) explain and critique the technique(s) and evaluation(s) (if any) discussed in the paper, and (3) highlight any follow-up work that has been done since the assigned work was published. Each presentation is expected to last roughly 35 minutes, followed by an in-class discussion (led by the presenter) of the assigned game/paper.

Each presentation will be graded based on its content, its organization, and the clarity with which it is delivered. To encourage improvement from one presentation to the next, if the grade for the second presentation is higher than the grade for the first presentation, the grade for the first presentation will be ignored, and the grade for the second presentation will be used to compute the overall presentation grade. Otherwise, the average of the two presentation grades will be used to compute the overall presentation grade.

NB: Each student must complete both of their Paper Presentations to qualify to receive a grade on the Final Report.

The presentation schedule is available here: Course Presentation Schedule

The participation grade will be based on the instructor's subjective evaluation of the student's participation throughout the semester. This evaluation will focus mostly on their activity during class discussions, but may also consider their use of the online forums and project tracking tools.

Beginning in Week 7, students will work on a small research project in teams of 2 to 3. The topic of the project must be related to computational storytelling, and the project itself must focus on using AI techniques to address a particular challenge. We will form teams in Week 5 following an in-class Idea Jam, during which everyone will propose and discuss several ideas for potential term projects. Each team will propose a project via written hand-in, and, upon its approval by the instructor, work to complete their project for the remainder of the term. Each team's project will be evaluated in four parts: a Project Proposal (due on TBD), Project Updates from Week 7 onward, a Final Presentation (time TBD), and a Final Report in the format of a standard conference paper (due TBD; this course has no final exam).

Near the end of the end of the term, some class time may be set aside for teams to work on their projects and obtain direct feedback from the instructor.

Project Updates are short presentations (5 minutes of slides/demos + 5 minutes for discussion) given by one team at the end of each class from Week 6 onward. Whenever a student presents a paper at the beginning of a class, the rest of their team will give a project update at the end of that class.

* This number may change if the class size changes.

NB: An overall grade of 60% (6/10) is required to pass the course, as required by RU for all M.Sc.-level courses.