Planning for the Efficient Updating of Mutual Fund Portfolios

Once there is a decision of rebalancing or updating a portfolio of funds, the process of changing the current portfolio to the target one, involves a set of transactions that are susceptible of being optimized. This is particularly relevant when managers have to handle the implications of different types of instruments. In this work we present linear programming and heuristic search approaches that produce plans for executing the update. The evaluation of our proposals shows cost improvements over the compared based strategy. The models can be easily extended to other realistic scenarios in which a holistic portfolio management is requiredComment: 8 page

Automatic Compilation of Objects to Counters in Automatic Planning. Case of study: Creation Planning

In classical planning, all objects should be represented as constants explicitly, even though their names could be irrelevant, which produces severe instantiation problems. This is specially problematic in tasks with actions for creating new objects, as it involves to estimate how many potential new objects will be necessary to solve the task. We propose a new automatic compilation from the classical to a numeric planning model to represent objects with irrelevant names using numerical functions. The compilation reduces the size of the instantiation and avoids the need of estimating the number of future objects in advance. The compiled planning task can be solved several orders of magnitude faster than its equivalent classical model.No publicad

Reseña de Santos (2020) La Cruel Pedagogía del Virus

This review highlights the contributions of the book La Cruel Pedagogía del Virus (The Cruel Pedagogy of the Virus) from the author Boaventura de Sousa Santos, where we find a critical analysis of the new coronavirus pandemic event. The author puts the current health crisis in its economic, politic and social context of capitalist modernity, bringing out some lessons the virus gives us and proposing some valuable alternatives. The concepts and lessons from the author serve us to think about our practices and the type of knowledge that guide them, focusing on biomedicine, which now occupy a central position in this crisis.Esta reseña destaca las principales contribuciones del libro La Cruel Pedagogía del Virus, del autor Boaventura de Sousa Santos, en la cual se hace un análisis crítico del evento de la pandemia del nuevo coronavirus. El autor encaja la actual crisis sanitaria dentro del contexto económico, político y social de la modernidad capitalista, extrayendo una serie de lecciones que nos da el virus y proponiendo alternativas valiosas. Los conceptos y lecciones del autor nos sirven para reflexionar sobre nuestras prácticas y el tipo de conocimiento que las guía, colocando el foco sobre la biomedicina, la cual ocupa un papel central en esta crisis

Razonamiento basado en casos aplicado a la planificación heurística

La Planificación Automática es una rama de la Inteligencia Artificial que estudia la construcción de conjuntos o secuencias de acciones, llamadas planes, que permiten transformar el estado de un entorno, con el objetivo de alcanzar las metas de un problema planteado. La planificación heurística es un paradigma dentro de la planificación automática que resuelve los problemas utilizando algoritmos de búsqueda que son guiados por una función de evaluación llamada heurística. Este paradigma ha dado grandes frutos en los últimos años gracias al desarrollo de funciones heurísticas que se pueden construir de forma independiente al dominio de planificación. Los inconvenientes que presentan estas heurísticas son que, por un lado tienen un alto coste computacional, dificultando la resolución de problemas grandes dentro de un tiempo razonable. y por otro lado, la poca información en ciertos tipos de dominios, provocando que en ocasiones los algoritmos busquen infructuosamente una solución. Por esto, surge la idea de retomar técnicas de aprendizaje automático que en años pasados fueron utilizadas sobre otros paradigmas de planificación, con la idea de mejorar la eficiencia de los planificadores. El objetivo de esta tesis doctoral es desarrollar un sistema de razonamiento basado en casos que sirva para complementar la búsqueda de un planificador heurístico. Se estudia el uso del conocimiento de los casos en diferentes algoritmos de búsqueda, y se evalúa experimentalmente sobre un conjunto de dominios, que por su diversidad, permite validar la técnica. Adicionalmente, se valora el conocimiento aprendido en los casos para establecer relaciones entre la información que puede almacenarse y las mejoras que se pueden obtener en el proceso de planificación.---------------------------------------------------------------------------------------------------------------------------------------------Automated Planning is an Artificial Intelligence field that studies how to build sequences of actions totally or partially ordered. These sequences, called plans, transform the state of the environment with the aim of achieving a given set of goals. Heuristic Planning is a recent planning paradigm that solves problems with search algorithms guided by an evaluation function called heuristic. Heuristic planning is still nowadays one of the top approaches mainly because we can build domain-independent heuristics with an automated procedure. These heuristics have two drawbacks. The first one is related to their computational cost, which imposes size restrictions to the problem being solved. The second one is the poor guidance heuristics give to the algorithms in some domains. Thereby, part of the research community focuses on applying machine learning techniques used in the past within other planning paradigms. The objective of this dissertation consists of building a case-based reasoning system that supports the search process of a heuristic planner.We will integrate the knowledge given by domain case bases as a search control. The empirical evaluation shows the benefits of the approach. We also analyze the learned knowledge in order to find relations between domain-specific information being gathered and the improvements obtained by the planners in terms of time or plan quality

Using the relaxed plan heuristic to select goals in oversubscription planning problems

Oversubscription planning (OSP) appears in many real problems where nding a plan achieving all goals is infeasi- ble. The objective is to nd a feasible plan reaching a goal sub- set while maximizing some measure of utility. In this paper, we present a new technique to select goals \a priori" for problems in which a cost bound prevents all the goals from being achieved. It uses estimations of distances between goals, which are com- puted using relaxed plans. Using these distances, a search in the space of subsets of goals is performed, yielding a new set of goals to plan for. A revised planning problem can be created and solved, taking into account only the selected goals. We present experiments in six di erent domains with good results.This work has been partially supported by MICIIN TIN2008-06701-C03-03 and CCG10-UC3M/TIC-5597 projects.Publicad

A case-based approach to heuristic planning

Most of the great success of heuristic search as an approach to AI Planning is due to the right design of domain-independent heuristics. Although many heuristic planners perform reasonably well, the computational cost of computing the heuristic function in every search node is very high, causing the planner to scale poorly when increasing the size of the planning tasks. For tackling this problem, planners can incorporate additional domain-dependent heuristics in order to improve their performance. Learning-based planners try to automatically acquire these domain-dependent heuristics using previous solved problems. In this work, we present a case-based reasoning approach that learns abstracted state transitions that serve as domain control knowledge for improving the planning process. The recommendations from the retrieved cases are used as guidance for pruning or ordering nodes in different heuristic search algorithms applied to planning tasks. We show that the CBR guidance is appropriate for a considerable number of planning benchmarks.This work has been partially supported by the Spanish MEC projects PELEA: TIN2008-6701-C03-03 and PlanInteraction: TIN2011-27652-C03-02.Publicad

The IBaCoP planning system: instance-based configured portfolios

Sequential planning portfolios are very powerful in exploiting the complementary strength of different automated planners. The main challenge of a portfolio planner is to define which base planners to run, to assign the running time for each planner and to decide in what order they should be carried out to optimize a planning metric. Portfolio configurations are usually derived empirically from training benchmarks and remain fixed for an evaluation phase. In this work, we create a per-instance configurable portfolio, which is able to adapt itself to every planning task. The proposed system pre-selects a group of candidate planners using a Pareto-dominance filtering approach and then it decides which planners to include and the time assigned according to predictive models. These models estimate whether a base planner will be able to solve the given problem and, if so, how long it will take. We define different portfolio strategies to combine the knowledge generated by the models. The experimental evaluation shows that the resulting portfolios provide an improvement when compared with non-informed strategies. One of the proposed portfolios was the winner of the Sequential Satisficing Track of the International Planning Competition held in 2014.We thank the authors of the base planners because our work is based largely on their previous effort. This work has been partially supported by the Spanish projects TIN2011-27652-C03-02, TIN2012-38079-C03-02 and TIN2014-55637-C2-1-R

6-hydroxydopamine and ovariectomy has no effect on heart rate variability parameters of females

OBJECTIVES: In addition to the classic motor symptoms of Parkinson’s disease (PD), patients also present with non-motor symptoms, such as autonomic dysfunction, which is present in almost 90% of patients with PD, affecting the quality of life and mortality. Regarding sex differences in prevalence and presentation, there is increasing concern about how sex affects autonomic dysfunction. However, there are no previous data on autonomic cardiac function in females after 6-hydroxydopamine (6-OHDA) striatal injection. METHODS: Wistar female rats were ovariectomized. After 20 days, the animals received bilateral injections of 6-OHDA (total dose per animal: 48 mg) or a vehicle solution in the striatum. Thirty days after 6-OHDA injection, subcutaneous electrodes were implanted for electrocardiogram (ECG) recording. Ten days after electrode implantation, ECG signals were recorded. Analyses of heart rate variability (HRV) parameters were performed, and the 6-OHDA lesion was confirmed by analyzing the number of tyrosine hydroxylase-positive neurons in the substantia nigra pars compacta (SNpc). RESULTS: A high dose of 6-OHDA did not affect HRV of females, independent of ovariectomy. As expected, ovariectomy did not affect HRV or lesions in the SNpc after 6-OHDA injection. CONCLUSIONS: We suggest that females with 6-OHDA present with cardioprotection, independent of ovarian hormones, which could be related to female vagal predominance

Efficiently Reasoning with Interval Constraints in Forward Search Planning

In this paper we present techniques for reasoning natively with quantitative/qualitative interval constraints in statebased PDDL planners. While these are considered important in modeling and solving problems in timeline based planners; reasoning with these in PDDL planners has seen relatively little attention, yet is a crucial step towards making PDDL planners applicable in real-world scenarios, such as space missions. Our main contribution is to extend the planner OPTIC to reason natively with Allen interval constraints. We show that our approach outperforms both MTP, the only PDDL planner capable of handling similar constraints and a compilation to PDDL 2.1, by an order of magnitude. We go on to present initial results indicating that our approach is competitive with a timeline based planner on a Mars rover domain, showing the potential of PDDL planners in this setting