Multi-Criteria Optimal Planning for Energy Policies in CLP

In the policy making process a number of disparate and diverse issues such as economic development, environmental aspects, as well as the social acceptance of the policy, need to be considered. A single person might not have all the required expertises, and decision support systems featuring optimization components can help to assess policies. Leveraging on previous work on Strategic Environmental Assessment, we developed a fully-fledged system that is able to provide optimal plans with respect to a given objective, to perform multi-objective optimization and provide sets of Pareto optimal plans, and to visually compare them. Each plan is environmentally assessed and its footprint is evaluated. The heart of the system is an application developed in a popular Constraint Logic Programming system on the Reals sort. It has been equipped with a web service module that can be queried through standard interfaces, and an intuitive graphic user interface.Comment: Accepted at ICLP2014 Conference as Technical Communication, due to appear in Theory and Practice of Logic Programming (TPLP

Risk Prediction Models for Depression in Community-Dwelling Older Adults

Objective: To develop streamlined Risk Prediction Models (Manto RPMs) for late-life depression. Design: Prospective study. Setting: The Survey of Health, Ageing and Retirement in Europe (SHARE) study. Participants: Participants were community residing adults aged 55 years or older. Measurements: The outcome was presence of depression at a 2-year follow up evaluation. Risk fac-tors were identified after a literature review of longitudinal studies. Separate RPMs were developed in the 29,116 participants who were not depressed at baseline and in the combined sample of 39,439 of non-depressed and depressed subjects. Models derived from the combined sample were used to develop a web-based risk calculator. Results: The authors identified 129 predictors of late-life depression after reviewing 227 studies. In non-depressed participants at baseline, the RPMs based on regression and Least Absolute Shrinkage and Selection Operator (LASSO) penalty (34 and 58 predictors, respectively) and the RPM based on Artificial Neural Networks (124 predictors) had a similar perfor-mance (AUC: 0.730-0.743). In the combined depressed and non-depressed par-ticipants at baseline, the RPM based on neural networks (35 predictors; AUC: 0.807; 95% CI: 0.80-0.82) and the model based on linear regression and LASSO penalty (32 predictors; AUC: 0.81; 95% CI: 0.79-0.82) had satisfactory accu-racy. Conclusions: The Manto RPMs can identify community-dwelling older individuals at risk for developing depression over 2 years. A web-based calcula-tor based on the streamlined Manto model is freely available at https://manto. unife.it/for use by individuals, clinicians, and policy makers and may be used to target prevention interventions at the individual and the population levels

Solving Mathematical Puzzles: a Deep Reasoning Challenge Position Paper

Abstract. This is the era of big-data: high-volume, high-velocity and high-variety information assets are being collected, demanding cost-effective information processing. Analytic techniques primarily based on statistical methods are showing astonishing results, but exhibit also limited reasoning capabilities. On the other end of the spectrum the era of bigreasoning is emerging with next-generation cognitive and autonomous end-to-end solvers. A problem description in terms of text and diagrams is given: problem solvers should automatically understand the problem, identify its components, devise a model, identify a solving technique and find a solution with no human intervention. We propose a challenge: to design and implement an end-to-end solver for mathematical puzzles able to compete with primary school students. Mathematical puzzles require mathematics to solve them, but also logic, intuition and imagination are essential ingredients, thus calling for an unprecedented integration of many different AI techniques

Process Discovery on Deviant Traces and Other Stranger Things

As the need to understand and formalise business processes into a model has grown over the last years, the process discovery research field has gained more and more importance, developing two different classes of approaches to model representation: procedural and declarative. Orthogonally to this classification, the vast majority of works envisage the discovery task as a one-class supervised learning process guided by the traces that are recorded into an input log. In this work instead, we focus on declarative processes and embrace the less-popular view of process discovery as a binary supervised learning task, where the input log reports both examples of the normal system execution, and traces representing a “stranger” behaviour according to the domain semantics. We therefore deepen how the valuable information brought by both these two sets can be extracted and formalised into a model that is “optimal” according to user-defined goals. Our approach, namely NegDis, is evaluated w.r.t. other relevant works in this field, and shows promising results regarding both the performance and the quality of the obtained solution

Discovering Business Processes models expressed as DNF or CNF formulae of Declare constraints

In the field of Business Process Management, the Process Discovery task is one of the most important and researched topics. It aims to automatically learn process models starting from a given set of logged execution traces. The majority of the approaches employ procedural languages for describing the discovered models, but declarative languages have been proposed as well. In the latter category there is the Declare language, based on the notion of constraint, and equipped with a formal semantics on LTLf. Also, quite common in the field is to consider the log as a set of positive examples only, but some recent approaches pointed out that a binary classification task (with positive and negative examples) might provide better outcomes. In this paper, we discuss our preliminary work on the adaptation of some existing algorithms for Inductive Logic Programming, to the specific setting of Process Discovery: in particular, we adopt the Declare language with its formal semantics, and the perspective of a binary classification task (i.e., with positive and negative examples

A recommender system for behavioral change in 60-70-year-old adults

Early old age (60-70 years old) is a particular period of life when possible habit modifications may occur, often related to job retirement. While taking up a more sedentary lifestyle may be pernicious for health, changing behavior by introducing simple exercises within daily life routines can effectively prevent age-related functional decline. This article presents the Profiling Tool, a system that provides 60-70-year-old adults with personalized recommendations to integrate simple activities, promoting balance, strength, and physical activity into their daily life. Its first implementation has been designed on information from literature, data from previously available longitudinal datasets, and experts' opinions. It has been deployed within a randomized controlled trial. Strategies for its update are based on model-based reinforcement learning approaches.publishedVersionPeer reviewe