A generative and case-based implementation of Proppian morphology

Automatic construction of story plots has always been a longed-for utopian dream in the entertainment industry, specially in the more commercial genres that are fueled by a large number of story plots with only a medium threshold on plot quality, such as TV series or video games. Although few professionals would contemplate full automation of the creative processes involved in plot writing, many would certainly welcome a fast prototyping tool that could produce a large number of acceptable plots involving a given set of initial circumstances or restrictions on the kind of characters that should be involved. Such a collection of plots might provide inspiration, initiate new ideas, or possibly even include a few plot sketches worthy of revision. Subsequent selection and revision of these plot sketches by professional screen writers could produce revised, fully human-authored valid plots. By making such a collection of tentative plots available to company screen writers, a smaller number of writers might be able to provide the material needed to keep the technical teams in work.

ProtoProppis a software application that can generate new stories. The domain of the application is the Russian Folk Tale, and we have used Vladimir Propp's formalization of the structure of such tales (Propp) as a guideline for the generation process.

The particular method employed for plot generation is case-based reasoning (Riesbeck & Schank). Case-based reasoning (CBR) is an AI method that relies on reusing solutions to problems solved in the past to solve problems in the present. The concept of case identifies a pair formed by a problem and its solution. The main idea is to store past problems and their associated solutions as cases, in what is called a case base. Whenever a new problem has to be solved, the case base is searched for similar problems faced in the past (retrieval step), and the solution of the best matching past case is adapted to the new problem (adaptation step), to account for differences between the new problem and the old one. The full CBR cycle involves two additional steps (revision and update) which provide the means for enriching the case base with the results of solving new problems using this method, but those steps are not relevant to the current endeavour. Traditional CBR is very useful in domains where the information to be handled is too complex to model explicitly, and in which there is an easily accessible store of previously solved problems and their solutions. The ProtoPropp application considers a plot generation problem in terms of Proppian functions, and a solution to that problem in terms of the assignment of a conceptual representation of the plot of a story. This involves transcribing existing folk tales into conceptual representations of their contents and associating them with elements of Proppian morphology. This is done by resorting to a formalized knowledge base of concepts, organised into a taxonomy, which explicitly includes the relations between them. Such a knowledge base, following current terminology in AI, is referred to as an ontology (Gruber 1994). The use of explicit conceptual knowledge to guide the CBR process characterises Knowledge Intensive CBR (Díaz-Agudo & González-Calero 2003).

In this project we propose a Knowledge Intensive Case-Based Reasoning (KI-CBR) approach to the problem of generating story plots from a case base of existing stories analyzed in terms of Proppian functions. A case-based reasoning process is defined to generate plots from a user query, with two important phases: retrieval of old stories, and adaptation to build a new one. The user query specifies an initial setting for the story, and the ontology is used to measure during the generation process the semantic distance between concepts specified by the user and those that appear in the texts.

ProppianOnto: the formalized knowledge

ProppianOnto is the name of the ontology developed to implement the formalization of Propp's theory (see Figure 1). It is built using OWL description logic according to current standards (Bechhofer et al. 2004). It includes concepts like PlotCase, Character or ProppianFunction that our system needs to reason about stories.

[Figure 1: Implementation of ProppianOnto]

The cases are plots taken from the Alexander Afanasiev's collection of Russian Fairy Tales. According to our interpretation of Propp, a plot case can have structures of two types.
PlotCasePlotCase1..n Character1..n Character1..n MoveCase1 AlienForm1 Resolution0..1 Epilogue0..1 Epilogue

The elements in these structures can themselves have structure. The structure of a move case and a Proppian function are shown below.
MoveCaseProppianFunction1..n Character1..n Character1..n ProppianFunction1..n Event1..n Place1..n Itembefore: 0..1 ProppianFunctionafter: 0..1 ProppianFunction
The structure of resolutions, epilogues and alien forms are similar to the move case.

ProtoPropp: the generation process

The generation of a new tale starts with a query presented by the user which represents the constraints that the desired tale should fulfil. The query is composed by filling in a form created which has six boxes for the main character roles (hero, villain, prisoner, donor, helper and false hero) and five boxes for the main Proppian functions (preparation, conflict, donor move, helper move and resolution), as it can be seen in Figure 2.

[Figure 2: Author interface of ProtoPropp]

Based on this query, the system attempts to find the most similar tales in the case base.

Because of the hierarchical structure of the conceptual taxonomy of the ontology, the given constraints can be fulfilled either in terms of strict similarity — the requested concept occurs in the tale — or in terms of conceptual similarity - either a specialization or an abstraction of the requested concept occurs in the tale.

1.
There is (at least) one retrieved tale with all the constraints in the query. The system gives the option of adding more constraints.

2.
There are no retrieved tales fulfilling all the given constraints but at least one retrieved tale fulfils some of them. The system selects the tale fulfilling the highest number of stated constraints. A second retrieval process is initiated, searching for a different tale satisfying the failed constraints. In this second process, the system can relax the similarity to ensure that some tale is found. That tale is mixed with the first retrieved tale to create a new one, adding or changing restrictions to the first tale, depending on the inter-relations between Proppian functions and other logical considerations, and generating from scratch the elements that the system was not able to find in the case base.

3.
There are no retrieved tales satisfying any of the query restrictions. The system offers the possibility of adding more restrictions or changing the current restrictions to create a new story.

Whenever more restrictions are needed, the system always has the default option of filling them in randomly.

The resulting tale is obtained in an abstract form, as a conceptual representation of the ingredients that make it up. The last step is to use a natural language generator - cFROGS (García-Ibánez) - to convert this conceptual representation to a simple text that can be easily read by the user.

Conclusions and future work

This system shows how narrative structure theories can be implemented in a computational generator of stories. It is a long road, but every step we take will help us to learn more about the way stories are built. The current version of the prototype is restricted to single move plot cases. Complex stories made of more than one move case need more control over the dependencies between structural elements, and their construction should be directed by appropriate inference over the concepts represented explicitly within the system. For this strategy to succeed, the existing knowledge base must be extended to include any narratological concepts that may play a role in the adequate resolution of any conflicts arising from the existence of complex dependencies between the structural elements of a plot.

Bibliography

Bechhofer, S., et al.
OWL Web Ontology Language Reference
W3C
2004

Díaz-Agudo, B.
González-Calero, P.A.
Knowledge Intensive CBR through Ontologies
Expert Update
6.1
44–54
2003

García Ibáñez, C., et al.
Una arquitectura software para el desarrollo de aplicaciones de generación de lenguaje natural
Procesamiento de Lenguaje Natural
33
111–118
2004

Gruber, T.R.
Towards Principles for the Design of Ontologies Used for Knowledge Sharing
Guarino, N.
R. Poli
Formal Ontology in Conceptual Analysis and Knowledge Representation
Kluwer
n.p.
1994
n. pag.

Propp, V.
Morphology of the Folktale
University of Texas Press
Austin and London
1968

Riesbeck, C.K.
Schank, R.C.
Inside Case-Based Reasoning
Lawrence Erlbaum
Evanston, Illinois
1989