Narratives are ubiquitous in human experience. We usethem to entertain, communicate, convince, and explain.One workshop participant noted that “as far as I know,
every society in the world has stories, which suggests they havea psychological basis, that stories do something for you.” To tru-ly understand and explain human intelligence, reasoning, andbeliefs, we need to understand why narrative is universal andexplain the function it serves.
Computational modeling is a natural method for investigat-ing narrative. As a complex cognitive phenomenon, narrativetouches on many areas that have traditionally been of interestto artificial intelligence researchers: its different facets draw onour capacities for natural language understanding and genera-tion, commonsense reasoning, analogical reasoning, planning,physical perception (through imagination), and social cogni-tion. Successful modeling will undoubtedly require researchersfrom these many perspectives and more, using a multitude ofdifferent techniques from the AI toolkit, ranging from, forexample, detailed symbolic knowledge representation to large-scale statistical analyses. The relevance of AI to narrative, andvice versa, is compelling.
The Computational Models of Narrative workshop1 had threemain objectives: (1) to understand the scope and dimensions ofnarrative models, identifying gaps and next steps, (2) to evalu-ate the state of the art, and (3) to begin to build a communityfocused on computational narrative. The interdisciplinarygroup of 22 participants (see figure 1) included computer scien-
Mark A. Finlayson, Whitman Richards, and Patrick H. Winston
n On October 8–10, 2009, an interdisciplinarygroup met in Beverley, Massachusetts, to evalu-ate the state of the art in the computationalmodeling of narrative. Three important findingsemerged: (1) current work in computationalmodeling is described by three different levels ofrepresentation; (2) there is a paucity of studiesat the highest, most abstract level aimed atinferring the meaning or message of the narra-tive; and (3) there is a need to establish a stan-dard data bank of annotated narratives, analo-gous to the Penn Treebank.
tists, psychologists, linguists, media developers,philosophers, and storytellers. Ten speakers wereselected to represent a range of views, and theirpresentations were organized into four groups,each followed by an extensive discussion moderat-ed by a panel. The day after the presentations,there was a lively, morning-long extended discus-sion. The meeting’s audio was captured and lateranalyzed in depth. A detailed summary of thegroup’s conclusions at the workshop appears else-where (Richards, Finlayson, and Winston 2009),together with recommendations for future initia-tives.2 Regarding models of narrative, the mainfindings were: (1) a three-level organization of nar-rative representations unifies work in the area, (2)the area suffers from a deficit of investigation atthe highest, most abstract level aimed at the“meaning” of the narrative, and (3) there is a needto establish a standard data bank of annotated nar-ratives, analogous to the Penn Treebank (Marcus,Marcinkiewicz, and Santorini 1993).
A Three-Level OrganizationComputational modeling requires a precise state-ment of the problem (or problems) to be solved.Thus, an obvious first step is to understand hownarrative should be represented.
There were three common denominators amongthe representations presented at the workshop: (1)narratives have to do with sequences of events, (2)narratives have hierarchical structure, and (3) theyare grounded in a commonsense knowledge of theworld. Similarly, it was uncontroversial that narra-tives can be told from multiple points of view, andthat all four of these characteristics were inde-pendent of whether or not a narrative was toldwith words.3
After analysis of the presentations and discus-sions, it became clear that all the representationsconsidered at the workshop were subsumed with-in a three-level structure. The heavily investigatedmiddle level stressed event sequences that werebuilt on the classic logical-predicate-like represen-tations introduced in artificial intelligence in itsearliest days, exemplified by instances such asKISS(JOHN, MARY) and CAUSE(SHOOT, DIE).
Below the middle level were representations thatexamined the detailed structure of the narrativesin question. There was quite a bit of work at thisdetail level, such as commonsense reasoning(Mueller 2007), discourse structures (Asher andLascarodes 2003), argument-support hierarchies(Bex, Prakken, and Verheij 2007), or plan graphs(Young 2007).
Above the middle level was a third, moreabstract kind of representation. This abstract levelencoded structures that were not directly presentin the story itself but had to be inferred in light of
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a larger context. Structures include plot functions(in Vladimir Propp’s sense), plot units (WendyLehnert) or the simply stated “meaning” of a nar-rative. Only a single piece of work presented dur-ing the workshop dealt with this upper level (Fin-layson 2009), and it became clear during thediscussion that there was a paucity of work at thislevel. Yet all participants agreed that tackling thislevel was crucial to truly understanding the char-acter of narrative.
QuestionsIn the course of discussions, three questions wereraised again and again, and it was clear that par-ticipants were far from consensus on answers toany of them: Why narrative? What are the appro-priate representations of narrative? What areappropriate experimental paradigms for narrative?
Why narrative? Participants were unable to givea concise and cogent reason why narrative per se,rather than a variety of other cognitive or socialprocesses, should be an object of study. Several par-ticipants claimed narrative was only an epiphe-nomenon, where the real target of inquiry shouldbe, say, analogy, planning, or social interaction. Inany event, all the proposed answers still beggedthe question of what is special about narrative inparticular—there are many ways of structuringevents, but not all of them are a narrative.Although numerous examples and dimensionswere proposed and discussed, there was no con-sensus on a definition for narrative, no procedurefor distinguishing narratives from nonnarratives,and no procedure for distinguishing good narra-tives from bad.
What are the appropriate representations of narra-tive? While there was some shared core structure tothe representations presented (noted previously—the middle level), researchers’ representations var-ied widely and covered different parts of the repre-sentational spectrum. Numerous “dimensions” ofnarrative were identified, but there was no con-sensus as to how the dimensions mapped into orwere split across representations.
What are appropriate experimental paradigms fornarrative? Related to the above was the issue ofhow the different representations dictated theform of experimental paradigms needed to vali-date the computational models. Consistent withuncertainties in precise computational definitions,there was also no consensus on what kinds ofexperiments would be the most informative.Should model evaluations be based on questionanswering, or on story simulation, or on analogiesbetween narratives that reflected human judg-ments?
Courses of ActionOne goal of the workshop was to identify nextsteps that could further progress in the area. In theintensive discussion on the last day of the work-shop, at least three of the courses of immediateaction proposed were universally applauded.
First, the participants agreed that the workshopwas a boost to understanding narrative, by bring-ing together a variety of approaches and showinglinks and differences. The group felt the commu-nity was fragmented and needed to be encouragedand grown. Many of the participants had not pre-viously met, and consequently a variety of per-spectives and approaches were new to large seg-ments of this small group. A second workshopwould be the obvious next step toward establish-ing a larger, still broader community. We visualizethe second workshop as doubling in size andincluding several areas not represented, such asgame-theoretic approaches to narrative, studies ofgossip and rumor, and narrative theory researchersfrom the humanities. It was generally agreed thatmore thought is also needed to reach an agreementon methods for evaluating story understanding, aswell as various experimental paradigms.
In addition to broadening the scope of partici-pants, a second workshop is needed to investigatewhether a new community should be set up (withits own annual meetings and publication vehicles)or whether the participants are naturally a subsetof an already established community. In particular,some participants laid great stress on investigatingwhether a publication venue directly associatedwith the area would be appropriate.
Second, it was suggested a catalogue be assem-bled listing potential applications of narrative—bigproblems on which narrative might give traction.An obvious example would be comparing newsreports from different perspectives or cultures.Such a catalogue would be of great use to motivat-ing work and securing funding.
Third, nearly every participant noted the sorelack of a shared corpus of stories—a necessary toolif one is to compare successes and strengths of var-ious approaches. Hence it was proposed to create astory databank. One important property of such acollection would be to provide at least one com-putationally tractable representation of the texts inan agreed-upon format. It was acknowledged thatannotation is a time-consuming and delicate
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Figure 1. Workshop Participants in Beverley, Massachusetts, Thursday, 9 October 2009.
Back row, left to right: J. Keyser (MIT), I. Horswill (Northwestern), M. Young (North Carolina State University), B. Verheij (Groningen), M.Cox (DARPA), S. Narayanan (ICSI and Berkeley), T. Lyons (AFOSR), L. Jackson (Naval Postgraduate School); Middle row, left to right: H. Lieber-man (MIT), K. Forbus (Northwestern), M. Finlayson (MIT), E. Mueller (IBM), P. Winston (MIT), N. Asher (Texas), J. Hobbs (USC ISI), V. Sub-rahmanian (Maryland); Front row, left to right: N. Cohn (Tufts), R. Jackendoff (Tufts), P. Gervás (U. Complutense Madrid), W. Richards (MIT),R. Swanson (USC ICT), E. Tomai (Pan American), M. Seifter (MIT).
process, but despite this obstacle there was generalagreement that there should be some attempt tomake a story databank. A committee was set up topursue the task as well as to decide upon an anno-tation format. Interested parties should contactPablo Gervás at the Universidad Complutense deMadrid or Mark Finlayson at the MassachusettsInstitute of Techology.
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Notes1. Sponsored by the AFOSR under MURI contract#FA9550-05-1-0321 to MIT. Special thanks to Maria Rebe-lo for administrative support.
2. See hdl.handle.net/1721.1/50232.
3. Neil Cohn demonstrated all four of these characteris-tics in a narrative form that can be purely visual: the visu-al language used in comics.
ReferencesAsher, N., and Lascarides, A. 2003. Logics of Conversation.New York: Cambridge University Press.
Bex, F. J.; Prakken, H.; and Verheij, B. 2007. FormalizingArgumentative Story-Based Analysis of Evidence. In Pro-ceedings of the Eleventh International Conference on ArtificialIntelligence and Law, 1–10. New York: Association forComputing Machinery.
Finlayson, M. A. 2009. Deriving Narrative Morphologiesvia Analogical Story Merging. In New Frontiers in AnalogyResearch: Proceedings of the Second International Conferenceon Analogy, 127–136. Sofia, Bulgaria: New Bulgarian Uni-versity Press.
Marcus, M. P.; Marcinkiewicz, M. A.; and Santorini, B.1993. Building a Large Annotated Corpus of English: ThePenn Treebank. Computational Linguistics 19(2): 313–330.
Mueller, E. T. 2007. Modeling Space and Time in Narra-tives about Restaurants. Literary and Linguistic Computing22(1): 67–84.
Richards, W.; Finlayson, M. A.; and Winston, P. H. 2009.Advancing Computational Models of Narrative. CSAILTechnical Report No. 2009-063. Massachusetts Instituteof Technology Computer Science and Artificial Intelli-gence Laboratory, Cambridge, MA.
Young, R. M. 2007. Story and Discourse: A Bipartite Mod-el of Narrative Generation in Virtual Worlds. InteractionStudies 8(2): 177–208.
Mark Alan Finlayson is a doctoral candidate in electricalengineering and computer science at the MassachusettsInstitute of Technology. His research interests are in com-putational models of human intelligence, in particular,the effect of culture and narrative on cognition. He holdsa B.S. from the University of Michigan and an M.S. fromMIT, both in electrical engineering.
Whitman Richards is a professor of cognitive sciences inthe Department of Brain and Cognitive Sciences at theMassachusetts Institute of Technology and has been onthe MIT faculty since 1965. He is also a professor of cog-nitive science, media arts, and sciences at the MIT MediaLab. He holds a B.S. in physical metallurgy (materials sci-ence) and a Ph.D. in experimental psychology, both fromMIT.
Patrick Henry Winston is the Ford Professor of ArtificialIntelligence and Computer Science at MIT. He has beenon the MIT faculty since 1970 and was director of theMIT Artificial Intelligence Laboratory from 1972 to 1997.He served as president of AAAI from 1985 to 1987. Hisprinciple research interest is in how vision, language, andmotor faculties together account for intelligence. Heholds B.S. and M.S. degrees in electrical engineering anda Ph.D. in computer science, all from MIT.
NEW!
AAAI Symposium on EducationalAdvances in Artificial Intelligence
(EAAI)
The first AAAI Symposium on Educational Advances inArtificial Intelligence (EAAI) will be held in conjunctionwith AAAI-10 in Atlanta. The EAAI symposium provides avenue for AI researchers involved in education to sharetheir innovative approaches to education and teaching.In contrast to work on using AI as a building block in edu-cational systems (such as intelligent tutoring systems),EAAI focuses on pedagogical issues related to teaching AIat a variety of levels (from K–12 through postgraduatetraining).
The EAAI symposium is comprised of several compo-nents, including a program of high-quality refereedpapers, panels, special sessions, and invited talks; apresymposium workshop for mentoring new faculty,instructors, and teaching assistants; an Educational andTeaching Video track within the AAAI Video Program; aStudent and Educator Robotics track within the AAAIRobotics Exhibition and Workshop; and a poster session,held in conjunction with the AAAI poster session.
For more information about the symposium, please visitthe AAAI-10 website or write to us at aaai10@aaai. org.
