Redes neuronales que expresan múltiples estrategias en el videojuego StarCraft 2.

ilustracionesUsing neural networks and supervised learning, we have created models capable of solving problems at a superhuman level. Nevertheless, this training process results in models that learn policies that average the plethora of behaviors usually found in datasets. In this thesis we present and study the Behavioral Repetoires Imitation Learning (BRIL) technique. In BRIL, the user designs a behavior space, the user then projects this behavior space into low coordinates and uses these coordinates as input to the model. Upon deployment, the user can adjust the model to express a behavior by specifying fixed coordinates for these inputs. The main research question ponders on the relationship between the Dimension Reduction algorithm and how much the trained models are able to replicate behaviors. We study three different Dimensionality Reduction algorithms: Principal Component Analysis (PCA), Isometric Feature Mapping (Isomap) and Uniform Manifold Approximation and Projection (UMAP); we design and embed a behavior space in the video game StarCraft 2, we train different models for each embedding and we test the ability of each model to express multiple strategies. Results show that with BRIL we are able to train models that are able to express the multiple behaviors present in the dataset. The geometric structure these methods preserve induce different separations of behaviors, and these separations are reflected in the models' conducts. (Tomado de la fuente)Usando redes neuronales y aprendizaje supervisado, hemos creado modelos capaces de solucionar problemas a nivel súperhumano. Sin embargo, el proceso de entrenamiento de estos modelos es tal que el resultado es una política que promedia todos los diferentes comportamientos presentes en el conjunto de datos. En esta tesis presentamos y estudiamos la técnica Aprendizaje por Imitación de Repertorios de Comportamiento (BRIL), la cual permite entrenar modelos que expresan múltiples comportamientos de forma ajustable. En BRIL, el usuario diseña un espacio de comportamientos, lo proyecta a bajas dimensiones y usa las coordenadas resultantes como entradas del modelo. Para poder expresar cierto comportamiento a la hora de desplegar la red, basta con fijar estas entradas a las coordenadas del respectivo comportamiento. La pregunta principal que investigamos es la relación entre el algoritmo de reducción de dimensionalidad y la capacidad de los modelos entrenados para replicar y expresar las estrategias representadas. Estudiamos tres algoritmos diferentes de reducción de dimensionalidad: Análisis de Componentes Principales (PCA), Mapeo de Características Isométrico (Isomap) y Aproximación y Proyección de Manifolds Uniformes (UMAP); diseñamos y proyectamos un espacio de comportamientos en el videojuego StarCraft 2, entrenamos diferentes modelos para cada embebimiento y probamos la capacidad de cada modelo de expresar múltiples estrategias. Los resultados muestran que, usando BRIL, logramos entrenar modelos que pueden expresar los múltiples comportamientos presentes en el conjunto de datos. La estructura geométrica preservada por cada método de reducción induce diferentes separaciones de los comportamientos, y estas separaciones se ven reflejadas en las conductas de los modelos. (Tomado de la fuente)Maestrí

One Shot Learning with class partitioning and cross validation voting (CP-CVV)

Producción CientíficaOne Shot Learning includes all those techniques that make it possible to classify images using a single image per category. One of its possible applications is the identification of food products. For a grocery store, it is interesting to record a single image of each product and be able to recognise it again from other images, such as photos taken by customers. Within deep learning, Siamese neural networks are able to verify whether two images belong to the same category or not. In this paper, a new Siamese network training technique, called CP-CVV, is presented. It uses the combination of different models trained with different classes. The separation of validation classes has been done in such a way that each of the combined models is different in order to avoid overfitting with respect to the validation. Unlike normal training, the test images belong to classes that have not previously been used in training, allowing the model to work on new categories, of which only one image exists. Different backbones have been evaluated in the Siamese composition, but also the integration of multiple models with different backbones. The results show that the model improves on previous works and allows the classification problem to be solved, an additional step towards the use of Siamese networks. To the best of our knowledge, there is no existing work that has proposed integrating Siamese neural networks using a class-based validation set separation technique so as to be better at generalising for unknown classes. Additionally, we have applied Cross-Validation-Voting with ConvNeXt to improve the existing classification results of a well-known Grocery Store Dataset.The Centre for the Development of Industrial Technology (CDTI) and by the Instituto para la Competitividad Empresarial de Castilla y León - FEDER (Project CCTT3/20/VA/0003

Finding Game Levels with the Right Difficulty in a Few Trials through Intelligent Trial-and-Error

Methods for dynamic difficulty adjustment allow games to be tailored to particular players to maximize their engagement. However, current methods often only modify a limited set of game features such as the difficulty of the opponents, or the availability of resources. Other approaches, such as experience-driven Procedural Content Generation (PCG), can generate complete levels with desired properties such as levels that are neither too hard nor too easy, but require many iterations. This paper presents a method that can generate and search for complete levels with a specific target difficulty in only a few trials. This advance is enabled by through an Intelligent Trial-and-Error algorithm, originally developed to allow robots to adapt quickly. Our algorithm first creates a large variety of different levels that vary across predefined dimensions such as leniency or map coverage. The performance of an AI playing agent on these maps gives a proxy for how difficult the level would be for another AI agent (e.g. one that employs Monte Carlo Tree Search instead of Greedy Tree Search); using this information, a Bayesian Optimization procedure is deployed, updating the difficulty of the prior map to reflect the ability of the agent. The approach can reliably find levels with a specific target difficulty for a variety of planning agents in only a few trials, while maintaining an understanding of their skill landscape.Comment: To be presented in the Conference on Games 202

Bringing robotics taxonomies to continuous domains via GPLVM on hyperbolic manifolds

Robotic taxonomies have appeared as high-level hierarchical abstractions that classify how humans move and interact with their environment. They have proven useful to analyse grasps, manipulation skills, and whole-body support poses. Despite the efforts devoted to design their hierarchy and underlying categories, their use in application fields remains scarce. This may be attributed to the lack of computational models that fill the gap between the discrete hierarchical structure of the taxonomy and the high-dimensional heterogeneous data associated to its categories. To overcome this problem, we propose to model taxonomy data via hyperbolic embeddings that capture the associated hierarchical structure. To do so, we formulate a Gaussian process hyperbolic latent variable model and enforce the taxonomy structure through graph-based priors on the latent space and distance-preserving back constraints. We test our model on the whole-body support pose taxonomy to learn hyperbolic embeddings that comply with the original graph structure. We show that our model properly encodes unseen poses from existing or new taxonomy categories, it can be used to generate trajectories between the embeddings, and it outperforms its Euclidean counterparts

Learning a Behavioral Repertoire from Demonstrations

International audienceImitation Learning (IL) is a machine learning approach to learn a policy from a set of demonstrations. IL can be useful to kick-start learning before applying reinforcement learning (RL) but it can also be useful on its own, e.g. to learn to imitate human players in video games. Despite the success of systems that use IL and RL, how such systems can adapt in-between game rounds is a neglected area of study but an important aspect of many strategy games. In this paper, we present a new approach called Behavioral Repertoire Imitation Learning (BRIL) that learns a repertoire of behaviors from a set of demonstrations by augmenting the state-action pairs with behavioral descriptions. The outcome of this approach is a single neural network policy conditioned on a behavior description that can be precisely modulated. We apply this approach to train a policy on 7,777 human demonstrations for the build-order planning task in StarCraft II. Dimensionality reduction is applied to construct a low-dimensional behavioral space from a high-dimensional description of the army unit composition of each human replay. The results demonstrate that the learned policy can be effectively manipulated to express distinct behaviors. Additionally, by applying the UCB1 algorithm, the policy can adapt its behavior-in-between games-to reach a performance beyond that of the traditional IL baseline approach

A Hexagonal Pattern in the Paraninfo at the Universidad de Alcalá

In the sixteenth century, the assembly hall, or Paraninfo, at the recently inaugurated Universidad de Alcala. was known as the pieça del theatro. This study focuses on the layouts and proportions designed for this hall, which could be the key to understanding the whole project as a Renaissance theater that was singularly inspired by Roman models. Specifically, the hexagonal pattern of the coffered ceiling represents a genuine formal exploration in comparison with other wooden ceilings having similar geometric bases. All this led us to study this uncommon masterpiece as a proposal for a new model toward an intellectual and architectural recovery of the Antiquity, in the core of the Universidad, conceived for the representation of academic ceremonies and humanistic theater.Research funded by Comunidad de Madrid/Universidad de Alcalá, Project CM/JIN/2019-041, IP: Manuel De Miguel Sánchez

MarioGPT: Open-Ended Text2Level Generation through Large Language Models

Procedural Content Generation (PCG) algorithms provide a technique to generate complex and diverse environments in an automated way. However, while generating content with PCG methods is often straightforward, generating meaningful content that reflects specific intentions and constraints remains challenging. Furthermore, many PCG algorithms lack the ability to generate content in an open-ended manner. Recently, Large Language Models (LLMs) have shown to be incredibly effective in many diverse domains. These trained LLMs can be fine-tuned, re-using information and accelerating training for new tasks. In this work, we introduce MarioGPT, a fine-tuned GPT2 model trained to generate tile-based game levels, in our case Super Mario Bros levels. We show that MarioGPT can not only generate diverse levels, but can be text-prompted for controllable level generation, addressing one of the key challenges of current PCG techniques. As far as we know, MarioGPT is the first text-to-level model. We also combine MarioGPT with novelty search, enabling it to generate diverse levels with varying play-style dynamics (i.e. player paths). This combination allows for the open-ended generation of an increasingly diverse range of content

WSPH and ISPH Calculations of a Counter-Rotating Vortex Dipole

Viscosity and vorticity are magnitudes playing an important role in many engineering physical phenomena such as: boundary layer separation, transition flows, shear flows, etc., demonstrating the importance of the vortical viscous flows commonly used among the SPH community. The simulation presented here, describes the physics of a pair of counter-rotating vortices in which the strain field felt by each vortex is due to the other one. Different from the evolution of a single isolated vortex, in this case each vortex is subjected to an external stationary strain field generated by the other, making the streamlines deform elliptically. To avoid the boundary influence, a large computational domain has been used ensuring insignificant effect of the boundary conditions on the solution. The performance of the most commonly used viscous models in simulating laminar flows, Takeda’s (TVT), Morris’ (MVT) and Monaghan-Cleary’s (MCGVT) has been discussed comparing their results. These viscous models have been used under two different compressibility hypotheses. Two cases have been numerically analyzed in this presentation. In the first case, a 2D system of two counter-rotating Lamb O seen vortices is considered. At first, the system goes through a rapid relaxation process in which both vortices equilibrate each other. This quasi-steady state is obtained after the relaxation phase is advected at a constant speed and slowly evolves owing to viscous diffusion. The results of the different Lamb-O seen numerical solutions have been validated with good agreement by comparison with the numerical results of a finite element code (ADFC) solution. A second case, somewhat more complex than the previous one, is a 3D Batchelor vortex dipole obtained by adding an axial flow to the system of the first case. The Batchelor vortex model considered here is a classical option normally used to model the structure of trailing vortices in the far-wake of an aircraft

TRIBUNAL ARBITRAL DE SOCIEDAD SI 03 S.A. VS. EMPRESA DE TRANSPORTE DEL TERCER MILENIO - TRANSMILENIO S.A.

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Heterologous expression of the yeast Tpo1p or Pdr5p membrane transporters in Arabidopsis confers plant xenobiotic tolerance

This deposit is composed by the main article plus the supplementary materials of the publication.Soil contamination is a major hindrance for plant growth and development. The lack of effective strategies to remove chemicals released into the environment has raised the need to increase plant resilience to soil pollutants. Here, we investigated the ability of two Saccharomyces cerevisiae plasma-membrane transporters, the Major Facilitator Superfamily (MFS) member Tpo1p and the ATP-Binding Cassette (ABC) protein Pdr5p, to confer Multiple Drug Resistance (MDR) in Arabidopsis thaliana. Transgenic plants expressing either of the yeast transporters were undistinguishable from the wild type under control conditions, but displayed tolerance when challenged with the herbicides 2,4-D and barban. Plants expressing ScTPO1 were also more resistant to the herbicides alachlor and metolachlor as well as to the fungicide mancozeb and the Co(2+), Cu(2+), Ni(2+), Al(3+) and Cd(2+) cations, while ScPDR5-expressing plants exhibited tolerance to cycloheximide. Yeast mutants lacking Tpo1p or Pdr5p showed increased sensitivity to most of the agents tested in plants. Our results demonstrate that the S. cerevisiae Tpo1p and Pdr5p transporters are able to mediate resistance to a broad range of compounds of agricultural interest in yeast as well as in Arabidopsis, underscoring their potential in future biotechnological applications.Fundação para a Ciência e a Tecnologia grants: (EXPL/AGR-PRO/1013/2013, PTDC/BIA-PLA/1084/2014, SFRH/BPD/44640/2008, SFRH/BPD/81221/2011, PD/BD/105735/2014, PD/00133/2012, SFRH/BD/92552/2013, UID/BIO/04565/2013, UID/Multi/04551/2013). Programa Operacional Regional de Lisboa 2020 grant: (Project N. 007317).info:eu-repo/semantics/publishedVersio