Reading List

Below is a list of important publications for NIL members.

Narrative Theory

General Information

  • Vladimir Iakovlevich Propp. Morphology of the folktale. Trans. Laurence Scott, 1968. University of Texas Press.

    Propp's structural analysis of Russian folktales is usually considered the beginning of narratology.

  • John R. Searle. Minds, brains, and programs. Behavioral and Brain Sciences, vol. 3, num. 03, pp. 417-424, 1980. Cambridge University Press.

    Searle's controversial Chinese Room argument against Strong AI.

  • David Herman, Manfred Jahn, Marie-Laure Ryan. Routledge encyclopedia of narrative theory. 2005. Routledge.

    Defines common narratology terms.

  • H. Porter Abbott. The Cambridge introduction to narrative. 2008. Cambridge University Press.

    An easy to read and helpful introduction to narratology.

  • William Indick. Psychology for screenwriters. 2004. Michael Wiese Productions.

    An insightful application of classical psychological theories to narrative structure.

  • Janet Horowitz Murray. Hamlet on the holodeck: the future of narrative in cyberspace. 1997. Simon and Schuster.

    A vision for the future of storytelling.

  • Michael Mateas, Phoebe Sengers. Narrative intelligence. In Proceedings of the AAAI Fall Symposium on Narrative Intelligence, pp. 1-10, 1999.

    A survey of narratology and its influences on computational models of narrative.

Interactive Narrative


Possible Worlds

Cognitive Science

General Information

QUEST Framework

  • Arthur C. Graesser, Sallie E. Gordon, Lawrence E. Brainerd. QUEST: A model of question answering. Computers & Mathematics with Applications, vol. 23, num. 6, pp. 733-745, 1992. Elsevier.

    Describes QUEST, a empirical method for evaluating how humans answer questions after reading stories.

  • Arthur C. Graesser, Paul J. Byrne, Michael L. Behrens. Answering questions about information in databases. Questions and Information Systems, pp. 229-252, 1992. Lawrence Erlbaum.

    Another survey of QUEST.

  • David B. Christian, R. Michael Young. Comparing cognitive and computational models of narrative structure. In Proceedings of the 19th National Conference of the American Association for Artificial Intelligence, pp. 385-390, 2004.

    A mapping of some QUEST structures onto POCL planning data structures.

  • Rogelio E. Cardona-Rivera, Thomason Price, David Winer, R. Michael Young. Question answering in the context of stories generated by computers. Advances in Cognitive Systems, vol. 4, pp. 227-245, 2016.

    Uses QUEST to answer questions about stories generated by a planning algorithm.

  • Rachelyn Farrell, Scott Robertson, Stephen G. Ware. Asking hypothetical questions about stories using QUEST. In Proceedings of the 9th International Conference on Interactive Digital Storytelling, pp. 136-146, 2016.

    Demonstrates that readers can reason about other possible worlds when answering QUEST-style questions.

Event-Indexing Situation Models

Planning and Causality in Narrative

Presence and Engagement

Personality and Emotion

  • Colin G. DeYoung, Lena C. Quilty, Jordan B. Peterson. Between facets and domains: 10 aspects of the Big Five. Journal of Personality and Social Psychology, vol. 93, num. 5, pp. 880-896, 2007. American Psychological Association.

    A survey of the Big Five (or OCEAN), a hierarchical, factor-based model of personality that has been highly studied and verified.

Artificial Intelligence

General Resources

  • Michael Bratman. Intention, plans, and practical reason. 1987. Harvard University Press.

    The popular belief, desire, intention (BDI) software model outlines an architecture for intelligent agent behavior.

  • Stuart J. Russell, Peter Norvig. Artificial Intelligence: a modern approach. 2009. Pearson.

    A widely used AI textbook covering a many topics.


  • Earl D. Sacerdoti. The nonlinear nature of plans. Stanford Research Institute, 1975.

    First discussion of partially ordered plans.

  • David McAllester, David Rosenblitt. Systematic nonlinear planning. Massachusetts Institute of Technology Artificial Intelligence Laboratory, 1991.

    SNLP (Systematic Non-Linear Planner) introduced causal links and was the first POCL planner.

  • J. Scott Penberthy, Daniel S. Weld. UCPOP: a sound, complete, partial order planner for ADL. In Proceedings of the 3rd International Conference on Principles of Knowledge Representation and Reasoning, vol. 92, pp. 103-114, 1992.

    UCPOP is an iconic POCL planner.

  • XuanLong Nguyen, Subbarao Kambhampati. Reviving partial order planning. In Proceedings of the 17th International Joint Conference on Artificial Intelligence, pp. 459-464, 2001.

    Describes several improvements to partial order planning, including the integration of modern state-space planning heuristics.

Forward-Chaining State Space Heuristic Search Planning

  • Blai Bonet, Héctor Geffner. Planning as heuristic search. Artificial Intelligence, vol. 129, num. 1, pp. 5-33, 2001. Elsevier.

    HSP (Heuristic Search Planner) was the beginning of the state space heuristic search family of planners.

  • Jörg Hoffmann, Bernhard Nebel. The FF planning system: fast plan generation through heuristic search. Journal of Artificial Intelligence Research, vol. 14, pp. 253-302, 2001.

    FF (Fast Forward) was a significant improvement over HSP.

  • Malte Helmert. The Fast Downward planning system.. Journal of Artificial Intelligence Research, vol. 26, pp. 191-246, 2006.

    FD (Fast Downward) was a significant improvement over FF.

  • Malte Helmert. Translation. In Understanding Planning Tasks: Domain Complexity and Heuristic Decomposition, pp. 171-206, 2008. Springer.

    A book chapter describing how Fast Downward translates traditional PDDL planning problems into multi-valued planning tasks.

  • Michael Katz, Jörg Hoffmann, Carmel Domshlak. Red-black relaxed plan heuristics. In Proceedings of the 23rd International Conference of Automated Planning and Scheduling, pp. 489-495, 2013.

    An improvement to FF that only relaxes some variables to obtain more accurate relaxed plans.

Other Notable Planning Algorithms

Planning in AAA Video Games

  • Jeff Orkin. Agent architecture considerations for real-time planning in games. In Proceedings of the 1st AAAI international conference on Artificial Intelligence and Interactive Digital Entertainment, pp. 105-110, 2005.

    F.E.A.R. uses real time planning to control its NPCs.

  • Alex J. Champandard, Tim Verweij, Remco Straatman. The AI for Killzone 2's multiplayer bots. In Proceedings of Game Developers Conference, 2009.

    Killzone 2 uses HTN planning to control its NPCs.

Fast Planning in Academic Interactive Story Systems

  • Marc Cavazza, Fred Charles, Steven J. Mead. Character-based interactive storytelling. IEEE Intelligent Systems special issue on AI in Interactive Entertainment, vol. 17, num. 4, pp. 17-24, 2002.

    Uses HSP to create an interactive narrative based on the TV show Friends.

  • David Pizzi, Marc Cavazza. Affective storytelling based on characters' feelings. In Proceedings of the AAAI Fall Symposium on Intelligent Narrative Technologies, pp. 111-118, 2007.

    Uses HSP to create an interactive narrative based on Flaubert's novel Madame Bovary.

  • Julie Porteous, Marc Cavazza, Fred Charles. Applying planning to interactive storytelling: Narrative control using state constraints. ACM Transactions on Intelligent Systems and Technology, vol. 1, num. 2, pp. 1-21, 2010. ACM.

    Uses fast planning with constraints to created an interactive narrative based on Shakespeare's The Merchant of Venice.

  • Stephen G. Ware, R. Michael Young, Christian Stith, Phillip Wright. Interactive narrative planning in The Best Laid Plans. In Proceedings of the 25th AAAI Conference on Artificial Intelligence, Virtual Agent Demonstrations, pp. 4313-4314, 2015.

    The Best Laid Plans uses narrative planning to generate stories at run time that cause conflict.

  • Rachelyn Farrell, Stephen G. Ware. Fast and diverse narrative planning through novelty pruning. In Proceedings of the 12th AAAI international conference on Artificial Intelligence and Interactive Digital Entertainment, pp. 37-43, 2016.

    Demonstrates that novelty pruning can improve the speed of several narrative planning algorithms.

Plan Recognition

  • Miquel Ramírez, Hector Geffner. Plan recognition as planning. In Proceedings of the 21st International Joint Conference on Artificial Intelligence, 2009.

    Describes a framework for using a planner to recognize an agent's plan by filling in missing steps from a partial sequence of observations.

Computational Models of Narrative

Survey Papers

Opinion Papers

  • Ian Horswill. Science considered harmful. In Proceedings of the 6th workshop on Intelligent Narrative Technologies at the 9th AAAI international conference on Artificial Intelligence and Interactive Digital Entertainment, pp. 82-85, 2013.

    Horswill argues that the emphasis on scientific rigor is harmful to the field of Intelligent Narrative Technologies and that it should adopt evaluation methods from the arts.

  • R. Michael Young. Science considered helpful. In Proceedings of the 11th International Conference on Interactive Digital Storytelling, pp. 21-35, 2018.

    Young responds to Horswill, defending the value of scientific rigor and the scientific approach to INT.

Notable Systems

  • James R. Meehan. TALE-SPIN, an interactive program that writes stories. Proceedings of the 5th International Joint Conference on Artificial Intelligence, pp. 91-98, 1977.

    TALE-SPIN is usually considered the first story generation system.

  • Natlie Dehn. Story generation after TALE-SPIN. In Proceedings of the 7th International Joint Conference on Artificial Intelligence, vol. 81, pp. 16-18, 1981.

    AUTHOR focused on the author's goals rather than the character's goals.

  • Michael Lebowitz. Story-telling as planning and learning. Poetics, vol. 14, num. 6, pp. 483-502, 1985. Elsevier.

    UNIVERSE combined plot fragments to generate serial stories.

  • Peter Weyhrauch. Guiding interactive drama. Ph.D. thesis at Carnegie Mellon University, 1997.

    Moe is an early interactive drama architecture for guiding a user through a plot graph based on adversarial search and an aesthetic evaluation function.

  • Rafael Pérez y Pérez, Mike Sharples. MEXICA: A computer model of a cognitive account of creative writing. Journal of Experimental & Theoretical Artificial Intelligence, vol. 13, num. 2, pp. 119-139, 2001. Taylor & Francis.

    MEXICA models the cognitive process of human composition to create short stories.

  • Mariët Theune, Sander Faas, Anton Nijholt, Dirk Heylen. The virtual storyteller: story creation by intelligent agents. In Proceedings of the 1st international conference on Technologies for Interactive Digital Storytelling and Entertainment, pp. 204-215, 2003. Springer.

    Virtual Storyteller focuses on strong autonomy agents but includes a director agent to look after the plot.

  • Michael Mateas, Andrew Stern. Structuring content in the Façade interactive drama architecture. In Proceedings of the 1st AAAI international conference on Artificial Intelligence and Interactive Digital Entertainment.

    Façade is a fully-realized interactive drama using natural language parsing.

  • Mark J. Nelson, Michael Mateas, David L. Roberts, Charles L. Isbell. Declarative optimization-based drama management in interactive fiction. IEEE Computer Graphics and Applications, vol. 26, num. 3, pp. 32-41, 2006.

    DODM (Declarative Optimization-based Drama Management) modifies the world to encourage players to take specific actions.

  • David Thue, Vadim Bulitko, Marcia Spetch, Eric Wasylishen. Interactive storytelling: a player modelling approach.. In Proceedings of the 3rd AAAI international conference on Artificial Intelligence and Interactive Digital Entertainment, pp. 43-48, 2007.

    PaSSAGE (Player-Specific Stories via Automatically Generated Events) categories players based on their actions and customizes the story accordingly.

  • Mark O. Riedl, Andrew Stern, Don Dini, Jason Alderman. Dynamic experience management in virtual worlds for entertainment, education, and training. International Transactions on Systems Science and Applications Special Issue on Agent Based Systems for Human Learning, vol. 4, num. 2, pp. 23-42, 2008.

    IN-TALE and ASD (Automated Story Director) analyse plans and how they can fail to create a personalized interactive experience.

  • Zach Tomaszewski. On the use of reincorporation in interactive drama. In Proceedings of the 4th workshop on Intelligent Narrative Technologies at the 7th AAAI international conference on Artificial Intelligence and Interactive Digital Entertainment, pp. 84-91, 2011.

    Marlinspike chooses a next event which best reincorporates past events, especially the player's, to create story unity.

  • Jonathan Teutenberg, Julie Porteous. Efficient intent-based narrative generation using multiple planning agents. In Proceedings of the 2013 international conference on Autonomous Agents and Multiagent Systems, pp. 603-610, 2013.

    IMPRACTical is a fast intentional planner that first plans for each agent and then stitches agent plans together to form a story.

  • Joshua McCoy, Mike Treanor, Ben Samuel, Aaron A. Reed, Michael Mateas, Noah Wardrip-Fruin. Social story worlds with Comme il Faut. IEEE Transactions on Computational Intelligence and Artificial Intelligence in Games, vol. 6, num. 2, pp. 97-112, 2014.

    Comme il Faut is the AI system which manages the social behaviors of characters in the game Prom Week.

  • Stephen G. Ware, R. Michael Young. Glaive: a state-space narrative planner supporting intentionality and conflict. In Proceedings of the 10th AAAI international conference on Artificial Intelligence and Interactive Digital Entertainment, pp. 80-86, 2014. (awarded Best Student Paper)

    Glaive is a fast forward-chaining state space narrative planner supporting intentionality and conflict.

  • Michael Mateas, Peter Mawhorter, Noah Wardrip-Fruin. Intentionally generating choices in interactive narratives. In Proceedings of the Sixth International Conference on Computational Creativity, pp. 292-299, 2015.

    Dunyazad automatically generates choose-your-own-adventure style stories using a simple but expressive theory of how people think about choices.

  • Yun-Gyung Cheong, R. Michael Young. Suspenser: a story generation system for suspense. IEEE Transactions on Computational Intelligence and Artificial Intelligence in Games, vol. 7, num. 1, pp. 39-52, 2015.

    Suspenser uses plan-based models of narrative to generate stories which evoke a feeling of suspense by remmoving the number of successful foreseeable outcomes for the protagonist.

  • Boyang Li, Stephen Lee-Urban, Darren Scott Appling, Mark O. Riedl. Crowdsourcing narrative intelligence. Advances in Cognitive systems, vol. 2, num. 1, pp. 1-18, 2012.

    Scheherazade learns plot graphs from a crowd-sourced corpus of simple stories about any topic.

  • Brent Harrison, Mark O. Riedl. Learning from stories: using crowdsourced narratives to train virtual agents. In Proceedings of the 12th AAAI international conference on Artificial Intelligence and Interactive Digital Entertainment, pp. 183-189, 2016.

    Quixote uses reinforcement learning to train agents to behave like characters from a crowd-sourced corpus of simple stories.

Plan-Based Models of Narrative

Intelligent Tutoring and Training

Survey Papers

  • Kurt VanLehn. The behavior of tutoring systems. International Journal of Artificial Intelligence in Education, vol. 16, num. 3, pp. 227-265, 2006.

    A seminal survey of intelligent tutoring systems research by a leading researcher in the field.

  • Amy L. Alexander, Tad Brunyé, Jason Sidman, Shawn A. Weil. From gaming to training: A review of studies on fidelity, immersion, presence, and buy-in and their effects on transfer in pc-based simulations and games. In DARWARS Training Impact Group, 2005.

    Surveys many factors that are important to virtual training and which may lead to improved transfer of knowledge from the virtual world to the real world.

  • Wenting Ma, Olusola O. Adesope, John C. Nesbit, Qing Liu. Intelligent tutoring systems and learning outcomes: a meta-analysis. Journal of Educational Psychology, vol. 106, num. 4, pp. 901–-918, 2014.

    A meta-survey of 107 intelligent tutoring systems involving 14,321 human subjects demonstrating that they improve learning and retention.

  • James A. Kulik, J. D. Fletcher. Effectiveness of intelligent tutoring systems: a meta-analytic review. Review of Educational Research, vol. 86, num. 1, pp. 42-78, 2016.

    A meta-survey of 50 intelligent tutoring systems demonstrating that they significantly improve learning.

Transfer, Presence, Fidelity, Immersion, etc.

  • Bob G. Witmer, Michael J. Singer. Measuring presence in virtual environments: a presence questionnaire. Presence: Teleoperators and Virtual Environments, vol. 7, num. 3, pp. 225-240, 1998. MIT Press.

    Describes a questionnaire for measuring the subjective experience of presence (closely related to agency) in virtual task environments.

  • Jonathan A. Stevens, J. Peter Kincaid. The relationship between presence and performance in virtual simulation training. Open Journal of Modelling and Simulation, vol. 3, pp. 41-48, 2015.

    Finds some evidence that improved persence leads to improved performance, which may in turn lead to improved transfer. Includes a good related work section on the relationship between presence, performance, fidelity, and transfer.

Intelligent Tutoring Systems in Education

Military Training Simulations

Other Applications

  • Kenneth Hullett, Michael Mateas. Scenario generation for emergency rescue training games. In Proceedings of the 4th international conference on Foundations of Digital Games, pp. 99-106, 2009.

    An HTN planner is used to generate training scenarios for emergency rescue workers that need to focus on specific skills.