A Parametric Hierarchical Planner for Experimenting Abstraction Techniques

This paper presents a parametric system, devised and implemented to perform hierarchical planning by delegating the actual search to an external planner (the "parameter") at any level of abstraction, including the ground one. Aimed at giving a better insight of whether or not the exploitation of abstract spaces can be used for solving complex planning problems, comparisons have been made between instances of the hierarchical planner and their non hierarchical counterparts. To improve the significance of the results, three different planners have been selected and used while performing experiments. To facilitate the setting of experimental environments, a novel semi-automatic technique, used to generate abstraction hierarchies starting from ground-level domain descriptions, is also described

PACMAS: A Personalized, Adaptive, and Cooperative MultiAgent System Architecture

In this paper, a generic architecture, designed to support the implementation of applications aimed at managing information among different and heterogeneous sources, is presented. Information is filtered and organized according to personal interests explicitly stated by the user. User pro- files are improved and refined throughout time by suitable adaptation techniques. The overall architecture has been called PACMAS, being a support for implementing Personalized, Adaptive, and Cooperative MultiAgent Systems. PACMAS agents are autonomous and flexible, and can be made personal, adaptive and cooperative, depending on the given application. The peculiarities of the architecture are highlighted by illustrating three relevant case studies focused on giving a support to undergraduate and graduate students, on predicting protein secondary structure, and on classifying newspaper articles, respectively

MASSP3: A System for Predicting Protein Secondary Structure

A system that resorts to multiple experts for dealing with the problem of predicting secondary structures is described, whose performances are comparable to those obtained by other state-of-the-art predictors. The system performs an overall processing based on two main steps: first, a "sequence-to-structure" prediction is performed, by resorting to a population of hybrid genetic-neural experts, and then a "structure-to-structure" prediction is performed, by resorting to a feedforward artificial neural networks. To investigate the performance of the proposed approach, the system has been tested on the RS126 set of proteins. Experimental results (about 76% of accuracy) point to the validity of the approach

Generating Abstractions from Static Domain Analysis

This paper addresses the problem of how to implement a proactive behavior according to a two-tiered (i.e., both theoretical and pragmatic) perspective. Theoretically, we claim that abstraction must be used to render agents able to solve complex problems. Pragmatically, we illustrate a technique devised to generate abstract spaces starting from a “ground” description of the domain being modeled

A Layered Architecture for Implementing Autonomous Planning Agents

This paper briefly describes an architecture for implementing autonomous agents that embody sophisticated planning capabilities. In particular, we are currently working on a two-pass vertically layered architecture, designed to deal with a complex environment. Such an architecture is currently based on three levels of abstraction (i.e., situated, strategic and deliberative), but has been designed for being easily generalized to a N-levels architecture, depending on the given environment and task complexity. Each level controls the underlying one, so that an agent behavior is supported by a clean hierarchical organization. Our autonomous agents act in a virtual world created for a computer game, and must interact with it by suitably planning and executing complex actions

Towards Argumentation-based Recommendations for Personalised Patient Empowerment

Patient empowerment is a key issue in healthcare. Approaches to increase patient empowerment encompass patient self-management programs. In this paper we present ArgoRec, a recommender system that exploits argumentation for leveraging explanatory power and natural language interactions so as to improve patients' user experience and quality of recommendations. ArgoRec is part of a great effort concerned with supporting complex chronic patients in, for instance, their daily life activities after hospitalisation, pursued within the CONNECARE project by following a co-design approach to define a comprehensive Self-Management System

Chapter xCARE: A Development Platform for Supporting Smart and Pervasive Healthcare

We are assisting to an important change in the healthcare domain where healthy citizens and patients are more and more in the center and become active partners in the entire process. In this scenario, smart and pervasive solutions assume a relevant role for remotely assisting citizens and patients together with their carers and supporting the overall team of professionals. From a software-engineering perspective, to follow and/or anticipate changes in requirements, modular solutions must be investigated and developed. Moreover, issues like personalization, adaptation, and scalability must be considered from the very beginning. In this chapter, we present xCARE, a microservices-based platform explicitly implemented to support the development of smart and pervasive healthcare systems. To show the potentiality and adaptability of xCARE, three relevant applications are presented: (i) a self-management system to support chronic complex patients; (ii) a patient management system that allows the team of professionals to assist patients before a major surgery together with a self-management system for the patients themselves; and (iii) an automatic self-management system for healthy citizens that want to follow healthier habits and that supports behavioral change

Comparative analysis of predictive methods for early assessment of compliance with continuous positive airway pressure therapy

Background: Patients suffering obstructive sleep apnea are mainly treated with continuous positive airway pressure (CPAP). Although it is a highly effective treatment, compliance with this therapy is problematic to achieve with serious consequences for the patients’ health. Unfortunately, there is a clear lack of clinical analytical tools to support the early prediction of compliant patients. Methods: This work intends to take a further step in this direction by building compliance classifiers with CPAP therapy at three different moments of the patient follow-up, before the therapy starts (baseline) and at months 1 and 3 after the baseline. Results: Results of the clinical trial shows that month 3 was the time-point with the most accurate classifier reaching an f1-score of 87% and 84% in cross-validation and test. At month 1, performances were almost as high as in month 3 with 82% and 84% of f1-score. At baseline, where no information of patients’ CPAP use was given yet, the best classifier achieved 73% and 76% of f1-score in cross-validation and test set respectively. Subsequent analyzes carried out with the best classifiers of each time point revealed baseline factors (i.e. headaches, psychological symptoms, arterial hypertension and EuroQol visual analog scale) closely related to the prediction of compliance independently of the time-point. In addition, among the variables taken only during the follow-up of the patients, Epworth and the average nighttime hours were the most important to predict compliance with CPAP. Conclusions: Best classifiers reported high performances after one month of treatment, being the third month when significant differences were achieved with respect to the baseline. Four baseline variables were reported relevant for the prediction of compliance with CPAP at each time-point. Two characteristics more were also highlighted for the prediction of compliance at months 1 and 3.This work is part of the myOSA project (RTC-2014-3138-1), funded by the Spanish Ministry of Economy and Competitiveness (Ministerio de Economía y Competitividad) under the framework “Retos-Colaboración”, State Scientific and Technical Research and Innovation Plan 2013-2016. The study was also partially funded by the European Community under “H2020-EU.3.1. – Societal Challenges – Health, demographic change and well-being” programme, project grant agreement number 689802 (CONNECARE)