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Transition Edge Sensors detector devices, like the core of the X-IFU instrument that will be on-board the Athena X-ray Observatory, produce current pulses as a response to the incident X-ray photons. The reconstruction of these pulses has been traditionally performed by means of a triggering algorithm based on the derivative signal overcoming a threshold (detection) followed by an optimal filtering (to retrieve the energy of each event). However, when the arrival of the photons is very close in time, the triggering algorithm is incapable of detecting all the individual pulses which are thus piled-up. In order to improve the efficiency of the detection and energy-retrieval process, we study here an alternative approach based on Machine Learning techniques to process the pulses. For this purpose, we construct and train a series of Neural Networks (NNs) not only for the detection but also for the recovering of the arrival time and the energy of simulated X-ray pulses. The data set used to train the NNs consists of simulations performed with the sixte/xifusim software package, the Athena/X-IFU official simulator. The performance of our NN classification clearly surpasses the detection performance of the classical triggering approach for the full range of photon energy combinations, showing excellent metrics and very competitive computing efficiency. However, the precision obtained for the recovery of the energy of the photons cannot currently compete with the standard optimal filtering algorithm, despite its much better computing efficiency.

During the first step of practical reasoning, i.e. deliberation, an intelligent agent generates a set of pursuable goals and then selects which of them he commits to achieve. An intelligent agent may in general generate multiple pursuable goals, which may be incompatible among them. In this paper, we focus on the definition, identification and resolution of these incompatibilities. The suggested approach considers the three forms of incompatibility introduced by Castelfranchi and Paglieri, namely the terminal incompatibility, the instrumental or resources incompatibility and the superfluity. We characterise computationally these forms of incompatibility by means of arguments that represent the plans that allow an agent to achieve his goals. Thus, the incompatibility among goals is defined based on the conflicts among their plans, which are represented by means of attacks in an argumentation framework. We also work on the problem of goals selection; we propose to use abstract argumentation theory to deal with this problem, i.e. by applying argumentation semantics. We use a modified version of the ¿cleaner world¿ scenario in order to illustrate the performance of our proposal.

[EN] In this work, we present an argumentation-based formalization for supporting the process of formation of intentions in practical agents. This is based on the belief-based goal processing model proposed by Castelfranchi and Paglieri, which is a more expressive and refined model than the BDI (Beliefs-Desires-Intentions) model. We focus on the progress of goals since they are desires until they become intentions, including the conditions under which a goal can be cancelled. We use argumentation to support the passage of the goals from their initial state until their final state. Our proposal complies with the properties defined by Castelfranchi and Paglieri, diachrony and synchrony. The first means that the support is given since the goal is a desire until it becomes an intention, and the second means that the support can be tracked, i.e. there is a memory of the cognitive path from the beginning of the process until the end., [ES] Este trabajo presenta una formalización basada en argumentación para el proceso de formación de intenciones en agentes prácticos. Se basa en el modelo de procesamiento de objetivos propuesto por Castelfranchi y Paglieri, el cual es un modelo más expresivo y con más etapas que el modelo BDI (Creencias-Deseos-Intenciones). Nos enfocamos en la evolución de los objetivos desde que son deseos hasta que se convierten en intenciones, incluyendo las condiciones bajo las cuales pueden ser cancelados. La argumentación soporta el paso de los objetivos desde su estado inicial hasta el final. Nuestra propuesta cumple con las propiedades de diacronía y sincronía. La primera quiere decir que el soporte se da desde que el objetivo es un deseo hasta que es una intención, y la segunda significa que la trayectoria del soporte puede ser seguida, i.e. hay una memoria del camino cognitivo que va desde el comienzo del proceso hasta su final.

The Athena X-ray Observatory, to be launched in the early 2030s, is designed to implement the Hot and Energetic Universe science theme. The X-ray Integral Field Unit (X-IFU) proposed for the Athena mission is a cryogenic imaging spectrometer, offering spatially-resolved high-spectral resolution X-ray spectroscopy over a 5 arcminute equivalent diameter field of view. In this project, we aim to detect and reconstruct the X-ray pulses that will be detected by the X-IFU instrument using Deep Learning techniques over a dataset of simulations with SIXTE (a software package for X-ray telescope observation simulations). We construct and train a Convolution Neural Network (CNN) to differentiate between single, double and triple pulses (i.e., detections with one, two or three pulses in the same record, respectively). We use a hyper-parameter bayesian optimization to select the best CNN architecture. Finally, we present the results of our CNN classification on a test set of simulated pulses, covering all the possible cases in terms of energy ranges and pulse distances, achieving excellent performance metrics.

In this work, we present an argumentation-based formalization for supporting the process of formation of intentions in practical agents. This is based on the belief-based goal processing model proposed by Castelfranchi and Paglieri, which is a more expressive and refined model than the BDI (Beliefs-Desires-Intentions) model. We focus on the progress of goals since they are desires until they become intentions, including the conditions under which a goal can be cancelled. We use argumentation to support the transition of the goals from their initial state until the last one. Our proposal complies with both supporting relation properties defined by Castelfranchi and Paglieri, diachrony and synchrony. The former means that the support happens since the goal is a desire until it becomes an intention, and the latter that the support can be tracked, i.e. there is a memory of the cognitive path from the beginning of the process until the end. In this work, we present an argumentation-based formalization for supporting the process of formation of intentions in practical agents. This is based on the belief-based goal processing model proposed by Castelfranchi and Paglieri, which is a more expressive and refined model than the BDI (Beliefs-Desires-Intentions) model. We focus on the progress of goals since they are desires until they become intentions, including the conditions under which a goal can be cancelled. We use argumentation to support the transition of the goals from their initial state until the last one. Our proposal complies with both supporting relation properties defined by Castelfranchi and Paglieri, diachrony and synchrony. The former means that the support happens since the goal is a desire until it becomes an intention, and the latter that the support can be tracked, i.e. there is a memory of the cognitive path from the beginning of the process until the end

An intelligent agent may in general pursue multiple goals at the same time, which leads to arise some conflict among them. In this paper, we focus on these conflicts or incompatibilities among goals. Our approach is based on the model of Castelfranchi and Paglieri, in which, three forms of incompatibility and the criteria for selection of goals are defined. We characterise computationally the different forms of incompatibility (terminal, instrumental and superfluity) and propose a way, based on abstract argumentation theory, for selecting those goals that will continue to be pursued.

Most of reasoning for decision making in daily life is based on preferences. As other kinds of reasoning processes, there are many formalisms trying to capture preferences, however none of them is able to capture all the subtleties of the human reasoning. In this paper we analyze how to formalize the preferences expressed by humans and how to reason with them to produce rankings. Particularly, we show that qualitative preferences are best represented with a combination of reward logics and conditional logics. We propose a new algorithm based on ideas of similarity between objects commonly used in case-based reasoning. We see that the new approach produces rankings close to the ones expressed by users. © 2016 Elsevier B.V.

Preferences are part of every day life driving to choice and action. We consider that there is a gap between preferences expressed by people and those we can find in the repositories. In this paper we explore a small set of preferences in the domain of movies, given by humans, in order to understand the expressive possibilities of some languages appearing in the literature: conditional logics and reward logics. After some experiments we contribute with a proposal for reasoning about preferences. © 2015 The authors and IOS Press. All rights reserved.

When people need help with day-to-day tasks they turn to family, friends or neighbours to help them out. Finding someone to help out can be a stressful waste of time. Despite an increasingly networked world, technology falls short in supporting such daily irritations. u-Help provides a platform for building a community of helpful people and supports them in finding help for day-to-day tasks. It relies on a trio of techniques that allow a requester and volunteer to find one another easily, and build up a community around such provision of services. First, we use an ontology to distinguish between the various tasks that u-Help allows people to provide. Second, a computational trust model is used to aggregate feedback from community members and allows people to discover who are good or bad at performing the various tasks. Last, a flooding algorithm, similar to the popular Gnutella algorithm, quickly disseminates requests for help through the community. This paper describes these three techniques in detail. This work is implemented as an iPhone application that we also describe in this paper.

Artificial Intelligence Research and Development. Proceedings of the 11th International Conference of the Catalan Association for Artificial Intelligence. Volume 184 Frontiers in Artificial Intelligence and Applications

Hospitals have a specified set of antibiotics for restricted use (ARU), very expensive, which are only recommended for special pathologies. The pharmacy department daily checks the prescription of this kind of antibiotics since it is often the case that, after a careful analysis, one can get the same therapeutic effects by using normal antibiotics which are much cheaper and usually less aggressive. In this paper, we describe a multi-agent system to help in the revision of medical prescriptions containing antibiotics of restricted use. The proposed approach attaches an agent to each patient which is responsible of checking different medical aspects related to his/her prescribed therapy. A pharmacy agent is responsible for analyzing it and suggesting alternative antibiotic treatments. All these agents are integrated in a hospital distributed scenario composed by many different kinds of software and human agents. This patient-centered multi-agent scenario is specified using the design methodology of Electronic Institutions. © 2003 Elsevier Science B.V. All rights reserved.

The research at the IIIA has produced over more than a decade two versions of a tool for developing knowledge-based systems: Milord and Milord II. This tool has been mainly used for the development of medical applications. In this paper we summarize the Milord II approximate reasoning approach based on fuzzy sets, and three medical applications: rheumatology diagnosis (Renoir), pneumonia diagnosis (Pneumon-IA) and pneumonia treatment (Terap-IA). Copyright © 2001 Elsevier Science B.V. | The research at the IIIA has produced over more than a decade two versions of a tool for developing knowledge-based systems: Milord and Milord II. This tool has been mainly used for the development of medical applications. In this paper we summarize the Milord II approximate reasoning approach based on fuzzy sets, and three medical applications: rheumatology diagnosis (Renoir), pneumonia diagnosis (Pneumon-IA) and pneumonia treatment (Terap-IA).

In this paper we propose a deductive calculus aiming at improving the query/simple-answer communication behaviour of many intelligent systems. In an uncertain reasoning context this behaviour consists of getting certainty values for propositions as answers to queries. Instead, with our calculus, answers to queries will become sets of formulas: a set of propositions and a set of specialised rules containing propositions for which the truth value is unknown in their left part. This type of behaviour is much more informative because it returns to users not only the answer to a query but all the relevant information, related to the answer, necessary to, possibly, improve the solution. To exemplify the general approach a family of propositional rule-based languages founded on multiple-valued logics is presented and formalised. The deductive system defined on top of these languages is based on a Specialisation Inference Rule (SIR): (A1 ∧ A2 ∧ ⋯ ∧ An → P, V), (A1, V′) ⊢ (A2 ∧ ⋯ ∧ An → P, V″), where V, V′ and V″ are truth intervals. This inference rule provides a way of generating rules containing less conditions in their premise by eliminating the conditions for which a definitive truth value already exists. The soundness and atom completeness of the deductive system are proved. The implementation of this deductive calculus is based on partial deduction techniques. Finally, an example of the application of the specialisation calculus to a multi-agent system is provided. © 1998 Elsevier Science Inc.

In this paper the analysis, design and implementation of a soccer team of micro-robots is explained. Besides the technical difficulties to develop these micro-robots, this paper also shows how to develop a multi-agent co-operative system by means of Matlab/Simulink (MATLAB and SIMULINK are Trade Marks of Math Works. Windows 95 is a Trade Mark of Microsoft Corporation.) a widely known computer aided control system design framework. Agent-oriented paradigms (AOPs) formalise interactions between multiple agents in terms of changing their mental states by communication between agents. Their practical implementations are usually conceived by means of object-oriented paradigms. Nevertheless, the implementation of agent-oriented paradigms in Matlab/Simulink is not straightforward. Thus, the obtained real implementation is an integrated system that includes several programming paradigms so as hardware platforms. Finally, the proposal of the integrated framework for the micro-robots soccer team is shown.