Federico Peinado

Story plot generation based on CBR

In this paper we present a system for automatic story generation that reuses existing stories to produce a new story that matches a given user query. The plot structure is obtained by a case-based reasoning (CBR) process over a case base of tales and an ontology of explicitly declared relevant knowledge. The resulting story is generated as a sketch of a plot described in natural language by means of natural language generation (NLG) techniques.

Transferring Game Mastering Laws to Interactive Digital Storytelling

The Interactive Dilemma is the inevitable conflict between author’s determinism and interactor’s freedom. There are some approaches that try to tackle it, using strategies and heuristic rules that can combine on the fly the previously designed author material with the run-time decisions of the interactor. Interactive Narrative is a relatively new field and it is difficult to find formal studies that shows how to create this kind of art. Our proposal is based on the theoretical study of tabletop Role-Playing Games and it involves the practical implementation of those ideas for managing the interaction in a simple text adventure game. Game Masters are the best models we have found in real life for designing and directing interactive stories. In this paper we transfer their player modeling, their rules for interaction management and their improvising algorithms from the real world to a new Interactive Storytelling system.

Evaluation of Automatic Generation of Basic Stories

This paper presents an application that automatically generatesbasic stories: short texts that only narrate the main events of the plot. The system operates with a representation in Description Logics, combining stored fabulas with the narrative knowledge implemented in a domain-specific ontology. The domain of application is the traditional folk tale, using the well-known morphology of Vladimir Propp as narratological background. In order to evaluate the results, human judges blindly compared one of the generated basic stories to two alternatives: one rendered directly from a stored fabula of the knowledge base and another randomly generated. As a conclusion, possibilities of measuring the utility of the system in terms of quality and originality of the generated artifact are discussed.

Revisiting Character-Based Affective Storytelling under a Narrative BDI Framework

Belief-Desire-Intention (BDI) is a well-known cognitive theory, especially in the field of Software Agents. Modelling characters using software agents has been proven to be a suitable approach for obtaining emergent and autonomous behaviours in Interactive Storytelling. In this paper it is claimed that an effective extension of previous models to the BDI framework is useful for designing intelligent characters. An example shows how internal thoughts and motivations of Madame Bovary s main characters can be more naturally formalised as a cognitive side of the story. A narrative reformulation of BDI theory is needed to avoid the implicit complexity of other proposals.

A Case Based Reasoning Approach to Story Plot Generation

Automatic construction of story plots has always been a longed-for utopian dream in the entertainment industry, especially in the more commercial genres that are fuelled by a large number of story plots with only a medium threshold on plot quality, such as TV series or video games. We propose a Knowledge Intensive CBR (KI-CBR) approach to the problem of generating story plots from a case base of existing stories analyzed in terms of Propp functions. A CBR process is defined to generate plots from a user query specifying an initial setting for the story, using an ontology to measure the semantical distance between words and structures taking part in the texts.

Ontological reasoning to configure emotional voice synthesis

The adequate representation of emotions in affective computing is an important problem and the starting point of studies related to emotions. There are different approaches for representing emotions, selecting one of this existing methods depends on the purpose of the application. Another problem related to emotions is the amount of different emotional concepts which makes it very difficult to find the most specific emotion to be expressed in each situation. This paper presents a system that reasons with an ontology of emotions implemented with semantic web technologies. Each emotional concept is defined in terms of a range of values along the three-dimensional space of emotional dimensions. The capabilities for automated classification and establishing taxonomical relations between concepts are used to provide a bridge between an unrestricted input and a restricted set of concepts for which particular rules are provided. The rules applied at the end of the process provide configuration parameters for a system for emotional voice synthesis.

EmoTales: creating a corpus of folk tales with emotional annotations

Emotions are inherent to any human activity, including human–computer interactions, and that is the reason why recognizing emotions expressed in natural language is becoming a key feature for the design of more natural user interfaces. In order to obtain useful corpora for this purpose, the manual classification of texts according to their emotional content has been the technique most commonly used by the research community. The use of corpora is widespread in Natural Language Processing, and the existing corpora annotated with emotions support the development, training and evaluation of systems using this type of data. In this paper we present the development of an annotated corpus oriented to the narrative domain, called EmoTales, which uses two different approaches to represent emotional states: emotional categories and emotional dimensions. The corpus consists of a collection of 1,389 English sentences from 18 different folk tales, annotated by 36 different people. Our model of the corpus development process includes a post-processing stage performed after the annotation of the corpus, in which a reference value for each sentence was chosen by taking into account the tags assigned by annotators and some general knowledge about emotions, which is codified in an ontology. The whole process is presented in detail, and revels significant results regarding the corpus such as inter-annotator agreement, while discussing topics such as how human annotators deal with emotional content when performing their work, and presenting some ideas for the application of this corpus that may inspire the research community to develop new ways to annotate corpora using a large set of emotional tags.

Ontological reasoning for improving the treatment of emotions in text

With the advent of affective computing, the task of adequately identifying, representing and processing the emotional connotations of text has acquired importance. Two problems facing this task are addressed in this paper: the composition of sentence emotion from word emotion, and a representation of emotion that allows easy conversion between existing computational representations. The emotion of a sentence of text should be derived by composition of the emotions of the words in the sentence, but no method has been proposed so far to model this compositionality. Of the various existing approaches for representing emotions, some are better suited for some problems and some for others, but there is no easy way of converting from one to another. This paper presents a system that addresses these two problems by reasoning with two ontologies implemented with Semantic Web technologies: one designed to represent word dependency relations within a sentence, and one designed to represent emotions. The ontology of word dependency relies on roles to represent the way emotional contributions project over word dependencies. By applying automated classification of mark-up results in terms of the emotion ontology the system can interpret unrestricted input in terms of a restricted set of concepts for which particular rules are provided. The rules applied at the end of the process provide configuration parameters for a system for emotional voice synthesis.

Minstrel reloaded: from the magic of lisp to the formal semantics of OWL

This paper is a review of a story generation system called Minstrel. It uses complex but hand-crafted Lisp knowledge structures to generate short computer-generated stories within the King Arthur domain. The knowledge representation model of Minstrel is reimplemented using a W3C standard language to analyze the pros and cons of technology updates over this kind of classic AI projects.

Assessing the Novelty of Computer-Generated Narratives Using Empirical Metrics

Novelty is a key concept to understand creativity. Evaluating a piece of artwork or other creation in terms of novelty requires comparisons to other works and considerations about the elements that have been reused in the creative process. Human beings perform this analysis intuitively, but in order to simulate it using computers, the objects to be compared and the similarity metrics to be used should be formalized and explicitly implemented. In this paper we present a study on relevant elements for the assessment of novelty in computer-generated narratives. We focus on the domain of folk-tales, working with simple plots and basic narrative elements: events, characters, props and scenarios. Based on the empirical results of this study we propose a set of computational metrics for the automatic assessment of novelty. Although oriented to the implementation of our own story generation system, the measurement methodology we propose can be easily generalized to other creative systems.