A multidimensional review of the cash management problem

In this paper, we summarize and analyze the relevant research on the cash management problem appearing in the literature. First, we identify the main dimensions of the cash management problem. Next, we review the most relevant contributions in this field and present a multidimensional analysis of these contributions, according to the dimensions of the problem. From this analysis, several open research questions are highlighted

On the dominant set selection problem and its application to value alignment

Decision makers can often be confronted with the need to select a subset of objects from a set of candidate objects by just counting on preferences regarding the objects' features. Here we formalise this problem as the dominant set selection problem. Solving this problem amounts to finding the preferences over all possible sets of objects. We accomplish so by: (i) grounding the preferences over features to preferences over the objects themselves; and (ii) lifting these preferences to preferences over all possible sets of objects. This is achieved by combining lex-cel -a method from the literature¿with our novel anti-lex-cel method, which we formally (and thoroughly) study. Furthermore, we provide a binary integer program encoding to solve the problem. Finally, we illustrate our overall approach by applying it to the selection of value-aligned norm systems

Instilling moral value alignment by means of multi-objective reinforcement learning

AI research is being challenged with ensuring that autonomous agents learn to behave ethically, namely in alignment with moral values. Here, we propose a novel way of tackling the value alignment problem as a two-step process. The first step consists on formalising moral values and value aligned behaviour based on philosophical foundations. Our formalisation is compatible with the framework of (Multi-Objective) Reinforcement Learning, to ease the handling of an agent's individual and ethical objectives. The second step consists in designing an environment wherein an agent learns to behave ethically while pursuing its individual objective. We leverage on our theoretical results to introduce an algorithm that automates our two-step approach. In the cases where value-aligned behaviour is possible, our algorithm produces a learning environment for the agent wherein it will learn a value-aligned behaviour

Empirical analysis of daily cash flow time series and its implications for forecasting

Usual assumptions on the statistical properties of daily net cash flows include normality,absence of correlation and stationarity. We provide a comprehensive study based on a real-world cash flow data set showing that: (i) the usual assumption of normality, absence of correlation and stationarity hardly appear; (ii) non-linearity is often relevant for forecasting; and (iii) typical data transformations have little impact on linearity and normality. This evidence may lead to consider a more data-driven approach such as time-series forecasting in an attempt to provide cash managers with expert systems in cash management

Automated Synthesis of Compact Normative Systems

Most normative systems make use of explicit representations of norms (namely, obligations, prohibitions, and permissions) and associated mechanisms to support the self-regulation of open societies of self-interested and autonomous agents. A key problem in research on normative systems is that of how to synthesise effective and efficient norms. Manually designing norms is time consuming and error prone. An alternative is to automatically synthesise norms. However, norm synthesis is a computationally complex problem. We present a novel online norm synthesis mechanism, designed to synthesise compact normative systems. It yields normative systems composed of concise (simple) norms that effectively coordinate a multiagent system (MAS) without lapsing into overregulation. Our mechanism is based on a central authority that monitors a MAS, searching for undesired states. After detecting undesirable states, the central authority then synthesises norms aimed to avoid them in the future. We demonstrate the effectiveness of our approach through experimental results

Artificial Intelligence for a Fair, Just, and Equitable World

From the 1970s onward, we started to dream of the leisure society in which, thanks to technological progress and consequent increase in productivity, working hours would be minimized and we would all live in abundance. We all could devote our time almost exclusively to personal relationships, contact with nature, sciences, the arts, playful activities, and so on. Today, this utopia seems more unattainable than it did then. Since the 21st century, we have seen inequalities increasingly accentuated: of the increase in wealth in the United States between 2006 and 2018, adjusted for inflation and population growth, more than 87% went to the richest 10% of the population, and the poorest 50% lost wealth [1] . Following the crisis of 2008, social inequalities, rights violations, planetary degradation, and the climate emergency worsened and increased (see [2] ). In 2019, the world's 2153 billionaires had more wealth than 4.6 billion people [3] . The World Bank estimates that COVID-19 will push up to 150 million people into extreme poverty [4]

Lessons learned from supplementing archaeological museum exhibitions with virtual reality

Archaeological excavations provide us with important clues about the past. Excavated artefacts represent an important connection to civilisations that no longer exist and help us understand some of their customs, traditions and common practices. With the help of academics and practitioners from various disciplines the results of archaeological excavations can be analysed and a body of knowledge about the corresponding society can be created and shared with members of the general public. Museums have traditionally served the purpose of communicating this knowledge and backing it up with the help of the excavated artefacts. Many museum visitors, however, find it difficult to develop a coherent understanding of the corresponding society only based on the artefacts and annotations showed in museums. Effective modern techniques that have high potential in helping museum visitors with better understanding of the past are 3D reconstruction and Virtual Reality. 3D reconstruction offers a cost effective way of recreating historical settlements in a computer-generated virtual environment, while Virtual Reality helps with immersing people into such environments and reaching a high degree of realism. With the help of these technologies it becomes possible to relive history, imagine yourself being a part of the reconstructed society and learn about its culture firsthand. The combination of 3D reconstruction and Virtual Reality \anton{represents} a very powerful learning tool, however this tool has been rarely used in a museum setting and its correct use has not been properly investigated. In this paper we present a study into using Virtual Reality in itinerant archaeological exhibitions. We discuss the lessons we have learned from developing an interactive Virtual Reality simulation of the Neolithic settlement of La Draga. These lessons feature our analysis of qualitative and quantitative feedback of museum visitors, as well as what we have learned from analysing their navigation and interaction patterns

Experiencia de coordinación con el método de la clase al revés en grados de Ingeniería Informática

En este trabajo se presenta una experiencia de implantación progresiva de la metodología de clase al revés en los dos primeros cursos comunes a dos grados de Ingeniería Informática en la Escuela Politécnica de la Universidad de Extremadura. A lo largo de los dos años que ha durado la experiencia, vertebrada con dos proyectos de innovación docente, han participado 13 asignaturas y 22 profesores de distintas áreas de conocimiento. El principal objetivo de este trabajo es explicar cómo se ha implantado esta experiencia para afrontar el reto de coordinación que supone involucrar a varias asignaturas con profesores con distintos niveles de conocimiento y aplicación de la clase al revés en sus asignaturas. La colaboración entre el profesorado ha sido fundamental para el aprendizaje y para el desarrollo de materiales y actividades adecuados para cada asignatura. Una buena ejecución de estos proyectos debería llevar a que la metodología sobreviva en las aulas más allá de la finalización de los proyectos docentes. Así, otra de las contribuciones del trabajo es medir el impacto que ha tenido esta metodología dos cursos después de la finalización de los proyectos. Además, se presentarán las lecciones aprendidas y que pueden ayudar a poner en marcha proyectos similares donde intervienen muchos profesores y asignaturas.In this paper we present an experience of progressive implementation of the flipped classroom methodology. We introduced changes in the two first common courses of two Degrees in Computer Science at Polytechnic School of University of Extremadura. The experience lasted for two years and it was vertebrated over two teaching innovation projects, involving 13 subjects and 22 teachers from different fields of knowledge. The main objective of this work is to explain how this experience was implemented to face the coordination challenge that arise when involving several subjects, and teachers with different levels of knowledge and application of the flipped classroom. Collaboration among teachers was essential for learning and developing appropriate materials and activities. A good execution of these projects should make the methodology survives in classrooms beyond completion of the teaching projects. So, another contribution of the work is to measure the impact of the methodology two courses after the completion of the projects. In addition, we enumerate some lessons learned willing to help similar projects, where many teachers and subjects are involved

Agent Environments for Multi-agent Systems – A Research Roadmap

Ten years ago, researchers in multi-agent systems became more and more aware that agent systems consist of more than only agents. The series of workshops on Environments for Multi-Agent Systems (E4MAS 2004-2006) emerged from this awareness. One of the primary outcomes of this endeavor was a principled understanding that the agent environment should be considered as a primary design abstraction, equally important as the agents. A special issue in JAAMAS 2007 contributed a set of influential papers that define the role of agent environments, describe their engineering, and outline challenges in the field that have been the drivers for numerous follow up research efforts. The goal of this paper is to wrap up what has been achieved in the past 10 years and identify challenges for future research on agent environments. Instead of taking a broad perspective, we focus on three particularly relevant topics of modern software intensive systems: large scale, openness, and humans in the loop. For each topic, we reflect on the challenges outlined 10 years ago, present an example application that highlights the current trends, and from that outline challenges for the future. We conclude with a roadmap on how the different challenges could be tackled. © Springer International Publishing Switzerland 2015.Peer reviewe