Assessing Multi-Agent Reinforcement Learning Algorithms for Autonomous Sensor Resource Management

Unmanned aerial vehicles (UAVs) have applications in search and rescue operations and such operations could be more efficient by using appropriate artificial intelligence (AI) to enable a UAV agent to operate autonomously. Sensor resource management (SRM), which leverages capabilities across location intelligence, facilitates the efficient and effective use of UAVs and their sensors to complete a set of tasks. Furthermore, multiple UAVs, each with different sensor configurations, must be considered when maximizing mission effects. Instantiating operational autonomy for such teams requires considerable coordination. One AI approach relevant to this task is multi-agent reinforcement learning (MARL). However, MARL has seen limited prior use in SRM. This work evaluates the trade-space of MARL algorithms with respect to performing heterogeneous sensor resource management (SRM) tasks, considers the concept of evaluating MARL in a test and evaluation framework and compares a suit of algorithms with random and Bayesian hyperparameter optimization methods

High-precision measurement of the hypertriton lifetime and Λ-separation energy exploiting ML algorithms with ALICE at the LHC.

L'abstract è presente nell'allegato / the abstract is in the attachmen

Bail or Jail? Judicial versus Algorithmic Decision-Making in the Pretrial System

To date, there are approximately sixty risk assessment tools deployed in the criminal justice system. These tools aim to differentiate between low-, medium-, and high-risk defendants and to increase the likelihood that only those who pose a risk to public safety or who are likely to flee are detained. Proponents of actuarial tools claim that these tools are meant to eliminate human biases and to rationalize the decision-making process by summarizing all relevant information in a more efficient way than can the human brain. Opponents of such tools fear that in the name of science, actuarial tools reinforce human biases, harm defendants’ rights, and increase racial disparities in the system. The gap between the two camps has widened in the last few years. Policymakers are torn between the promise of technology to contribute to a more just system and a growing movement that calls for the abolishment of the use of actuarial risk assessment tools in general and the use of machine learning-based tools in particular. This paper examines the role that technology plays in this debate and examines whether deploying artificial intelligence (“AI”) in existing risk assessment tools realizes the fears emphasized by opponents of automation or improves our criminal justice system. It focuses on the pretrial stage and examines in depth the seven most commonly used tools. Five of these tools are based on traditional regression analysis, and two have a machine-learning component. This paper concludes that classifying pretrial risk assessment tools as AI-based tools creates the impression that sophisticated robots are taking over the courts and pushing judges from their jobs, but that impression is far from reality. Despite the hype, there are more similarities than differences between tools based on traditional regression analysis and tools based on machine learning. Robots have a long way to go before they can replace judges, and this paper does not argue for replacement. The long list of policy recommendations discussed in the last chapter highlights the extensive work that needs to be done to ensure that risk assessment tools are both accurate and fair toward all members of society. These recommendations apply regardless of whether machine learning or regression analysis is used. Special attention is paid to assessing how machine learning would impact those recommendations. For example, this paper argues that carefully detailing each of the factors used in the tools and including multiple options to choose from (i.e., not just binary “yes-or-no” questions) will be useful for both regression analysis and machine learning. However, machine learning would likely lead to more personalized and meaningful scoring of criminal defendants because of the ability of machine learning techniques to “zoom in” on the unique details of each individual case

Aesthetic choices: Defining the range of aesthetic views in interactive digital media including games and 3D virtual environments (3D VEs)

Defining aesthetic choices for interactive digital media such as games is a challenging task. Objective and subjective factors such as colour, symmetry, order and complexity, and statistical features among others play an important role for defining the aesthetic properties of interactive digital artifacts. Computational approaches developed in this regard also consider objective factors such as statistical image features for the assessment of aesthetic qualities. However, aesthetics for interactive digital media, such as games, requires more nuanced consideration than simple objective and subjective factors, for choosing a range of aesthetic features. From the study it was found that the there is no one single optimum position or viewpoint with a corresponding relationship to the aesthetic considerations that influence interactive digital media. Instead, the incorporation of aesthetic features demonstrates the need to consider each component within interactive digital media as part of a range of possible features, and therefore within a range of possible camera positions. A framework, named as PCAWF, emphasized that combination of features and factors demonstrated the need to define a range of aesthetic viewpoints. This is important for improved user experience. From the framework it has been found that factors including the storyline, user state, gameplay, and application type are critical to defining the reasons associated with making aesthetic choices. The selection of a range of aesthetic features and characteristics is influenced by four main factors and sub-factors associated with the main factors. This study informs the future of interactive digital media interaction by providing clarity and reasoning behind the aesthetic decision-making inclusions that are integrated into automatically generated vision by providing a framework for choosing a range of aesthetic viewpoints in a 3D virtual environment of a game. The study identifies critical juxtapositions between photographic and cinema-based media aesthetics by incorporating qualitative rationales from experts within the interactive digital media field. This research will change the way Artificial Intelligence (AI) generated interactive digital media in the way that it chooses visual outputs in terms of camera positions, field-view, orientation, contextual considerations, and user experiences. It will impact across all automated systems to ensure that human-values, rich variations, and extensive complexity are integrated in the AI-dominated development and design of future interactive digital media production

Desarrollo de un videojuego para mejorar el nivel de comprensi?n lectora en estudiantes de primaria

Las participaciones de Per? en las pruebas PISA han demostrado que el pa?s posee un bajo nivel principalmente en el ?rea de lectura quedando en la mayor?a de las ocasiones en los ?ltimos puestos. Por otro lado, el mercado de los videojuegos ha crecido de forma exponencial increment?ndose en 50% el n?mero de jugadores peruanos solamente en el primer semestre del 2020 con la aparici?n del coronavirus. La presente investigaci?n consisti? desarrollar un videojuego para mejorar el nivel de comprensi?n lectora en estudiantes de primaria. Los participantes fueron 112 estudiantes de 1ro a 6to grado de primaria con un rango de edad de 5 a 11 a?os. Para el desarrollo del videojuego se utiliz? la metodolog?a en cascada que incluye las fases de comunicaci?n, planeaci?n, modelado, desarrollo y despliegue. Los resultados revelaron que los estudiantes que utilizaron el videojuego demostraron una mejora significativa de 1.77 puntos (1er grado), 1.45 puntos (2do grado), 1.06 puntos (3er grado), 1.17 puntos (4to grado), 1.34 puntos (5to grado) y 1.17 puntos (6to grado) en las evaluaciones realizadas. Asimismo, se evidenci? que m?s del 80% de los estudiantes tuvieron una mejora en los niveles literal e inferencial de comprensi?n lectora