Case-based retrieval and K-NN classification techniques are suitable for assessing hemodialysis treatment efficiency and for identifying risk situations. In this domain, cases involve time series data, that need to undergo a feature extraction phase in order to reduce dimensionality and to speed up similarity calculation. In this paper, we propose a deep learning architecture for time series feature extraction, based on the use of a convolutional autoencoder. Deep features provide a better time series representation with respect to features produced by the Discrete Cosine Transform (DCT). Indeed, in our experiments, K-NN classification based on deep features has outperformed the DCT-based one. We are also working in the direction of improving interpretability, by using case retrieval results obtained in a different feature space (defined on the basis of domain knowledge) to explain the outputs provided by the adoption of the deep learning technique.

This paper describes the architecture that integrates DALI MASs (Multi-Agent Systems) and ASP (Answer Set Programming) modules for reaching goals in a flexible and timely way, where DALI is a computational-logic-based fully implemented agent-oriented logic programming language and ASP modules includes solvers that allow affordable and flexible planning capabilities. The proposed DALI MAS architecture exploits such modules for flexible goal decomposition and planning, with the possibility to select plans according to a suite of possible preferences and to re-plan upon need. We present an abstract case-study concerning DALI agents which cooperate for exploring an unknown territory under changing circumstances in an optimal or at least sub-optimal fashion. The architecture can be exploited not only by DALI agents, but rather by any kind of logical agent.

Implementing Machine Ethics in Intelligent Agents involves trustworthiness and safety, meaning that agents should do what is expected they should do (at least, even in case of malfunctioning of any kind, concerning high-priority goals) and should not behave in unexpected potentially harmful ways. This topics are strongly related with "assurance", i.e., to ensuring that system users can rely upon the system. This paper deals with assurance of logical agent systems via temporal-logic-based runtime self-monitoring and checking.

Obvious strategyproofness (OSP) is an appealing concept as it allows to maintain incentive compatibility even in the presence of agents that are not fully rational, e.g., those who struggle with contingent reasoning (Li 2015). However, it has been shown to impose some limitations, e.g., no OSP mechanism can return a stable matching (Ashlagi and Gonczarowski 2015). We here deepen the study of the limitations of OSP mechanisms by looking at their approximation guarantees for basic optimization problems paradigmatic of the area, i.e., machine scheduling and facility location. We prove a number of bounds on the approximation guarantee of OSP mechanisms, which show that OSP can come at a significant cost. However, rather surprisingly, we prove that OSP mechanisms can return optimal solutions when they use monitoring — a novel mechanism design paradigm that introduces a mild level of scrutiny on agents’ declarations (Kovacs, Meyer, and Ventre 2015).

Users currently access Wikipedia through two traditional paradigms, text search and hypertext navigation. We believe that user access can be significantly improved by supporting a systematic conceptual exploration of the knowledge base through dynamic taxonomies with a faceted taxonomy organization. This approach allows the easy manipulation of sets of documents and the systematic and intuitive exploration of complex knowledge bases.

Our society is organized around a number of (interdependent) global systems. Logistic and supply chains, health services, energy networks, financial markets, computer networks, and cities are just a few examples of such global, complex systems. These global systems are socio-technical and involve interactions between complex infrastructures, man-made processes, natural phenomena, multiple stakeholders, and human behavior. For the first time in the history of manking, we have access to data sets of unprecedented scale and accuracy about these infrastructures, processes, natural phenomena, and human behaviors. In addition, progress in high-performancing computing, data mining, machine learning, and decision support opens the possibility of looking at these problems more holistically, capturing many of these aspects simultaneously. This paper addresses emergent architectures enabling controlling, predicting and reaoning on these systems.

The Answer Set Programming (ASP) Competition is a biannual event for evaluating declarative knowledge representation systems on hard and demanding AI problems. The competition consists of two main tracks: the ASP system track and the model and solve track. The traditional system track compares dedicated answer set solvers on ASP benchmarks, while the model and solve track invites any researcher and developer of declarative knowledge representation systems to participate in an open challenge for solving sophisticated AI problems with their tools of choice. This article provides an overview of the ASP competition series, reviews its origins and history, giving insights on organizing and running such an elaborate event, and briefly discusses about the lessons learned so far.

Conference on Case-Based Reasoning (ICCBR) has continuously been the preeminent international meeting on case-based reasoning (CBR). Through 2009, ICCBR had been a biennial conference, held in alternation with its sister conference, the European Conference on Case-Based Reasoning (ECCBR), which was located in Europe. At the 2009 ICCBR, the ICCBR Program Committee elected to extend an offer of consolidation with ECCBR. The offer was accepted by the ECCBR 2010 organizers and they considered it approved by the ECCBR community, as the two conferences shared a majority of Program Committee members. Therefore, starting in 2010, ICCBR and ECCBR are merged in a single conference series, called ICCBR.

Bots are, for many Web and social media users, the source of many dangerous attacks or the carrier of unwanted messages, such as spam. Nevertheless, crawlers and software agents are a precious tool for analysts, and they are continuously executed to collect data or to test distributed applications. However, no one knows which is the real potential of a bot whose purpose is to control a community, to manipulate consensus, or to influence user behavior. It is commonly believed that the better an agent simulates human behavior in a social network, the more it can succeed to generate an impact in that community. We contribute to shed light on this issue through an online social experiment aimed to study to what extent a bot with no trust, no profile, and no aims to reproduce human behavior, can become popular and influential in a social media. Results show that a basic social probing activity can be used to acquire social relevance on the network and that the so-acquired popularity can be effectively leveraged to drive users in their social connectivity choices. We also register that our bot activity unveiled hidden social polarization patterns in the community and triggered an emotional response of individuals that brings to light subtle privacy hazards perceived by the user base.

The research on low-complexity nonmonotonic description logics recently identified a fragment of EL with bottom, supporting defeasible inheritance with overriding, where reasoning can be carried out in polynomial time. We contribute to that framework by supporting more axiom schemata and all the concept constructors of EL++ without increasing asymptotic complexity. Moreover, we show that all the syntactic restrictions we adopt are necessary by proving several coNP-hardness results.