TVTropes is a wiki that describes tropes and which ones are used in which artistic work. We are mostly interested in films, so after releasing the TropeScraper Python module that extracts data from this site, in this report we use scraped information to describe statistically how tropes and films are related to each other and how these relations evolve in time. In order to do so, we generated a dataset through the tool TropeScraper in April 2020. We have compared it to the latest snapshot of DB Tropes, a dataset covering the same site and published in July 2016, providing descriptive analysis, studying the fundamental differences and addressing the evolution of the wiki in terms of the number of tropes, the number of films and connections. The results show that the number of tropes and films doubled their value and quadrupled their relations, and films are, at large, better described in terms of tropes. However, while the types of films with the most tropes has not changed significantly in years, the list of most popular tropes has. This outcome can help on shedding some light on how popular tropes evolve, which ones become more popular or fade away, and in general how a set of tropes represents a film and might be a key to its success. The dataset generated, the information extracted, and the summaries provided are useful resources for any research involving films and tropes. They can provide proper context and explanations about the behaviour of models built on top of the dataset, including the generation of new content or its use in machine learning.

Designing a story is widely considered a crafty yet critical task that requires deep specific human knowledge in order to reach a minimum quality and originality. This includes designing at a high level different elements of the film; these high‐level elements are called tropes when they become patterns. The present paper proposes and evaluates a methodology to automatically synthesize sets of tropes in a way that they maximize the potential rating of a film that conforms to them. We use machine learning to create a surrogate model that maps film ratings from tropes, trained with the data extracted and processed from huge film databases in Internet, and then we use a genetic algorithm that uses that surrogate model as evaluator to optimize the combination of tropes in a film. In order to evaluate the methodology, we analyse the nature of the tropes and their distributions in existing films, the performance of the models and the quality of the sets of tropes synthesized. The results of this proof of concept show that the methodology works and is able to build sets of tropes that maximize the rating and that these sets are genuine. The work has revealed that the methodology and tools developed are directly suitable for assisting in the plots generation as an authoring tool and, ultimately, for supporting the automatic generation of stories, for example, in massively populated videogames.

From the database DBTropes.org, we have created a dataset of films and the tropes that they use, which we have called PicTropes. In this report we provide the descriptive analysis and a further discussion on the dataset PicTropes: The extracted features will help us decide the best values for a future recommendation system and content generator, whereas the analysis of the distribution functions that fit the best will help us interpret the relation between the films and the tropes that were found inside them. Additionally, we provide rankings of the top-25 tropes and films, which will help us discuss and formulate questions to guide future extensions of the PicTropes dataset.

The procedural generation of massive subplots and backstories in secondary characters that inhabit OpenWorld videogames usually lead to stereotyped characters that act as a mere backdrop for the virtual world; however, many game designers claim that the stories can be very relevant for the player's experience. For this reason we are looking for a methodology that improves the variability of the characters' personality while enhancing the quality of their backstories following artistic or literary guidelines. In previous works, we used multi agent systems in order to obtain stochastic, but regulated, inter-relations that became backstories; later, we have used genetic algorithms to promote the appearance of high level behaviors inside them. Our current work continues the previous research line and propose a three layered system (Evolutionary computation -Agent-Based Model - Logical Reasoner) that is applied to the promotion of the monomyth, commonly known as the hero's journey, a social pattern that constantly appears in literature, films, and videogames. As far as we know, there is no previous attempt to model the monomyth as a logical theory, and no attempt to use the sub-solutions for narrating purposes. Moreover, this paper shows for the first time this multi-paradigm three-layeredmethodology to generate massive backstories. Different metrics have been tested in the experimental phase, from those that sum all the monomyth-related tropes to those that promote distribution of archetypes in the characters. Results confirm that the system canmake the monomyth emerge and that the metric has to take into account facilitator predicates in order to guide the evolutionary process.

In many optimization processes, the fitness or the considered measure of goodness for the candidate solutions presents uncertainty, that is, it yields different values when repeatedly measured, due to the nature of the evaluation process or the solution itself. This happens quite often in the context of computational intelligence in games, when either bots behave stochastically, or the target game possesses intrinsic random elements, but it shows up also in other problems as long as there is some random component. Thus, it is important to examine the statistical behavior of repeated measurements of performance and, more specifically, the statistical distribution that better fits them. This work analyzes four different problems related to computational intelligence in videogames, where Evolutionary Computation methods have been applied, and the evaluation of each individual is performed by playing the game, and compare them to other problem, neural network optimization, where performance is also a statistical variable. In order to find possible patterns in the statistical behavior of the variables, we track the main features of its distributions, skewness and kurtosis. Contrary to the usual assumption in this kind of problems, we prove that, in general, the values of two features imply that fitness values do not follow a normal distribution; they do present a certain common behavior that changes as evolution proceeds, getting in some cases closer to the standard distribution and in others drifting apart from it. A clear behavior in this case cannot be concluded, other than the fact that the statistical distribution that fitness variables follow is affected by selection in different directions, that parameters vary in a single generation across them, and that, in general, this kind of behavior will have to be taken into account to adequately address uncertainty in fitness in evolutionary algorithms.

Generating fiction environments for a multi-agent system optimized by genetic algorithms (with some specific requirements related to the desirable plots), presents two main problems: first it is impossible to know in advance the optimal value for the particular designed fitness function, and at the same time, it creates a vast search space for the parameters that it needs. The purpose of this paper is to define a methodology to find the best parameter values for both, the evolutionary algorithm, and the own fictional world configuration. This design includes running, to completion, a world simulation represented as a chromosome, and assigning a fitness to it, thus composing a very complex fitness landscape. In order to optimize the resources allocated to evolution and to have some guarantees that the final result will be close to the optimum, we systematically analyze a set of possible values of the most relevant parameters, obtaining a set of generic rules. These rules, when applied to the plot requisites, and thus, to the fitness function, will lead to a reduced range of parameter values that will help the storyteller to create optimal worlds with a reduced computation budget.

Noise or uncertainty appear in many optimization processes when there is not a single measure of optimality or fitness but a random variable representing it. These kind of problems have been known for a long time, but there has been no investigation of the statistical distribution those random variables follow, assuming in most cases that it is distributed normally and, thus, it can be modelled via an additive or multiplicative noise on top of a non-noisy fitness. In this paper we will look at several uncertain optimization problems that have been addressed by means of Evolutionary Algorithms and prove that there is no single statistical model the evaluations of the fitness functions follow, being different not only from one problem to the next, but in different phases of the optimization in a single problem.

The creation of fictional stories is a very complex task that usually implies a creative process where the author has to combine characters, conflicts and backstories to create an engaging narrative. This work presents a general methodology that uses individual based models to generate cohesive and coherent backstories where desired archetypes (universally accepted literary symbols) emerge and their life stories are a by-product of the simulation. This methodology includes the modeling and parameterization of the agents, the environment where they will live and the desired literary setting. The use of a genetic algorithm (GA) is proposed to establish the parameter configuration that will lead to backstories that best fit the setting. Information extracted from a simulation can then be used to create the backstories. To demonstrate the adequacy of the methodology, we perform an implementation using a specific multi-agent system and evaluate the results.

The creation of fictional stories is a very complex task that usually implies a creative process where the author has to combine characters, conflicts and plots to create an engaging narrative. This work presents a simulated environment with hundreds of characters that allows the study of coherent and interesting literary archetypes (or behaviours), plots and sub-plots. We will use this environment to perform a study about the number of profiles (parameters that define the personality of a character) needed to create two emergent scenes of archetypes: "natality control" and "revenge". A Genetic Algorithm (GA) will be used to find the fittest number of profiles and parameter configuration that enables the existence of the desired archetypes (played by the characters without their explicit knowledge). The results show that parametrizing this complex system is possible and that these kind of archetypes can emerge in the given environment.

Stories are not only painfully weaved by crafty writers in the solitude of their studios; they also have to be produced massively for non-player characters in the video game industry or tailored to particular tastes in personalized stories. However, the creation of fictional stories is a very complex task that usually implies a creative process where the author has to combine characters, conflicts and backstories to create an engaging narrative. This work describes a general methodology to generate cohesive and coherent backstories where desired archetypes (universally accepted literary symbols) can emerge in complex stochastic systems. This methodology supports the modeling and parametrization of the agents, the environment where they will live and the desired literary setting. The use of a Genetic Algorithm (GA) is proposed to establish the parameter configuration that will lead to backstories that best fit the setting. Information extracted from a simulation can then be used to create the literary work. To demonstrate the adequacy of the methodology, we perform an implementation using a specific multi-agent system and evaluate the results, testing with three different literary settings.

The creation of fictional stories is a very complex task that usually implies a creative process where the author has to combine characters, conflicts and plots to create an engaging narrative. This work presents a simulated environment with hundreds of characters that allows the study of coherent and interesting literary archetypes (or behaviours), plots and sub-plots. We will use this environment to perform a study about the number of profiles (parameters that define the personality of a character) needed to create two emergent scenes of archetypes: "natality control" and "revenge". A Genetic Algorithm (GA) will be used to find the fittest number of profiles and parameter configuration that enables the existence of the desired archetypes (played by the characters without their explicit knowledge). The results show that parametrizing this complex system is possible and that these kind of archetypes can emerge in the given environment.

This paper compares the use of RGB and HSV histograms during the execution of an Evolutionary Algorithm. This algorithm generates abstract images that try to match the histograms of a target image. Three different fitness functions have been used to compare: the differences between the individual with the RGB histogram of the test image, the HSV histogram, and an average of the two histograms at the same time. Results show that the HSV fitness also increases the similarities of the RGB (and therefore, the average) more than the other two measures.