Exploiting an Ontological Model to Study COVID-19 Contagion Chains in Sustainable Smart Cities

The COVID-19 pandemic has caused the deaths of millions of people around the world. The scientific community faces a tough struggle to reduce the effects of this pandemic. Several investigations dealing with different perspectives have been carried out. However, it is not easy to find studies focused on COVID-19 contagion chains. A deep analysis of contagion chains may contribute new findings that can be used to reduce the effects of COVID-19. For example, some interesting chains with specific behaviors could be identified and more in-depth analyses could be performed to investigate the reasons for such behaviors. To represent, validate and analyze the information of contagion chains, we adopted an ontological approach. Ontologies are artificial intelligence techniques that have become widely accepted solutions for the representation of knowledge and corresponding analyses. The semantic representation of information by means of ontologies enables the consistency of the information to be checked, as well as automatic reasoning to infer new knowledge. The ontology was implemented in Ontology Web Language (OWL), which is a formal language based on description logics. This approach could have a special impact on smart cities, which are characterized as using information to enhance the quality of basic services for citizens. In particular, health services could take advantage of this approach to reduce the effects of COVID-19.

Algorithmic Ethics: Formalization and Verification of Autonomous Vehicle Obligations

In this article, we develop a formal framework for automatic reasoning about the obligations of autonomous cyber-physical systems, including their social and ethical obligations. Obligations, permissions, and prohibitions are distinct from a system's mission, and are a necessary part of specifying advanced, adaptive AI-equipped systems. They need a dedicated deontic logic of obligations to formalize them. Most existing deontic logics lack corresponding algorithms and system models that permit automatic verification. We demonstrate how a particular deontic logic, Dominance Act Utilitarianism (DAU) [23], is a suitable starting point for formalizing the obligations of autonomous systems like self-driving cars. We demonstrate its usefulness by formalizing a subset of Responsibility-Sensitive Safety (RSS) in DAU; RSS is an industrial proposal for how self-driving cars should and should not behave in traffic. We show that certain logical consequences of RSS are undesirable, indicating a need to further refine the proposal. We also demonstrate how obligations can change over time, which is necessary for long-term autonomy. We then demonstrate a model-checking algorithm for DAU formulas on weighted transition systems and illustrate it by model-checking obligations of a self-driving car controller from the literature.

Ontology-Based Reasoning for Educational Assistance in Noncommunicable Chronic Diseases

Noncommunicable chronic diseases (NCDs) affect a large part of the population. With the emergence of COVID-19, its most severe cases impact people with NCDs, increasing the mortality rate. For this reason, it is necessary to develop personalized solutions to support healthcare considering the specific characteristics of individuals. This paper proposes an ontology to represent the knowledge of educational assistance in NCDs. The purpose of ontology is to support educational practices and systems oriented towards preventing and monitoring these diseases. The ontology is implemented under Protégé 5.5.0 in Ontology Web Language (OWL) format, and defined competency questions, SWRL rules, and SPARQL queries. The current version of ontology includes 138 classes, 31 relations, 6 semantic rules, and 575 axioms. The ontology serves as a NCDs knowledge base and supports automatic reasoning. Evaluations performed through a demo dataset demonstrated the effectiveness of the ontology. SWRL rules were used to define accurate axioms, improving the correct classification and inference of six instantiated individuals. As a scientific contribution, this study presents the first ontology for educational assistance in NCDs.

Reasoning on vague ontologies using rough set theory

Web ontologies can contain vague concepts, which means the knowledge about them is imprecise and then query answering will not possible due to the open world assumption. A concept description can be very exact (crisp concept) or exact (fuzzy concept) if its knowledge is complete, otherwise it is inexact (vague concept) if its knowledge is incomplete. In this paper, we propose a method based on the rough set theory for reasoning on vague ontologies. With this method, the detection of vague concepts will insert into the original ontology new rough vague concepts where their description is defined on approximation spaces to be used by extended Tableau algorithm for automatic reasoning. The extended Tableau algorithm by this rough set-based vagueness is intended to answer queries even with the presence of incomplete information.

Hyperintensional Reasoning Based on Natural Language Knowledge Base

The success of automated reasoning techniques over large natural-language texts heavily relies on a fine-grained analysis of natural language assumptions. While there is a common agreement that the analysis should be hyperintensional, most of the automatic reasoning systems are still based on an intensional logic, at the best. In this paper, we introduce the system of reasoning based on a fine-grained, hyperintensional analysis. To this end we apply Tichy’s Transparent Intensional Logic (TIL) with its procedural semantics. TIL is a higher-order, hyperintensional logic of partial functions, in particular apt for a fine-grained natural-language analysis. Within TIL we recognise three kinds of context, namely extensional, intensional and hyperintensional, in which a particular natural-language term, or rather its meaning, can occur. Having defined the three kinds of context and implemented an algorithm of context recognition, we are in a position to develop and implement an extensional logic of hyperintensions with the inference machine that should neither over-infer nor under-infer.

Using automated reasoning to explore the metabolism of unconventional organisms: a first step to explore host–microbial interactions

Systems modelled in the context of molecular and cellular biology are difficult to represent with a single calibrated numerical model. Flux optimisation hypotheses have shown tremendous promise to accurately predict bacterial metabolism but they require a precise understanding of metabolic reactions occurring in the considered species. Unfortunately, this information may not be available for more complex organisms or non-cultured microorganisms such as those evidenced in microbiomes with metagenomic techniques. In both cases, flux optimisation techniques may not be applicable to elucidate systems functioning. In this context, we describe how automatic reasoning allows relevant features of an unconventional biological system to be identified despite a lack of data. A particular focus is put on the use of Answer Set Programming, a logic programming paradigm with combinatorial optimisation functionalities. We describe its usage to over-approximate metabolic responses of biological systems and solve gap-filling problems. In this review, we compare steady-states and Boolean abstractions of metabolic models and illustrate their complementarity via applications to the metabolic analysis of macro-algae. Ongoing applications of this formalism explore the emerging field of systems ecology, notably elucidating interactions between a consortium of microbes and a host organism. As the first step in this field, we will illustrate how the reduction in microbiotas according to expected metabolic phenotypes can be addressed with gap-filling problems.

Reasoning about Who to Notify to avoid Alarm Fatigue: Algorithm Development and Validation (Preprint)

BACKGROUND Alarm Fatigue is a scenario experienced by an overwhelmed and fatigued healthcare team that is desensitized and slow to respond to alarms. The most common alarm-related issues that may lead to Alarm Fatigue include the excessive number of alarms, a number of alarms generated by many different types of alarm devices, and the high percentage of false alarms (80%-99%). All of these alerts have to be processed by the healthcare teams who are consistently under pressure: they should analyze the high volume of inputs they are receiving in order to answer to them quickly and correctly, by making decisions in real-time about the response to the next alarm. Under alarm fatigue conditions, the staff may ignore and/or silence alarms, putting patients in risky situations. OBJECTIVE This paper’s main goal is to propose a feasible solution for mitigating alarm fatigue by using an automatic reasoning mechanism to choose the best caregiver to be assigned to a given notification within the set of available caregivers in an Intensive Care Unit. METHODS Our main contribution in this work consists of an algorithm that decides who is the best caregiver to notify in an ICU. We formalized this problem as a Constraint-Satisfaction Problem and we present one example of how it can be solved. We designed a case study where patients’ vital signs were collected through a vital signs’ generator that also triggers alarms. We conducted five experiments to test our algorithm considering different situations for an ICU. The evaluation of our algorithm was made through the comparison between the results of the choices made by our reasoning algorithm and another strategy that we call “blind” strategy, which randomly assigns caregivers to notifications. RESULTS Experiments are used to demonstrate that providing a reasoning system we could decide who is the best caregiver to receive a notification. By comparing the choices made by our reasoning algorithm and the “blind” strategy, our reasoning algorithm achieved a better result in terms of prioritizing the assignments we wanted to make based on our defined criteria: patient’s severity, the distance between caregivers and patients, caregivers’ experience, the probability of a notification to be false, and the number of notifications caregivers have received. CONCLUSIONS The experimental results strongly suggest that this reasoning algorithm is a useful strategy for mitigating alarm fatigue. We showed, in our experiments, that caregivers with higher levels of experience received more notifications than the ones with lower levels. Our future work is to deal with resource negotiation and to evaluate the distribution of the notifications to the caregivers’ teams made by the algorithms.

Reasoning on Knowledge Graphs with Debate Dynamics

We propose a novel method for automatic reasoning on knowledge graphs based on debate dynamics. The main idea is to frame the task of triple classification as a debate game between two reinforcement learning agents which extract arguments – paths in the knowledge graph – with the goal to promote the fact being true (thesis) or the fact being false (antithesis), respectively. Based on these arguments, a binary classifier, called the judge, decides whether the fact is true or false. The two agents can be considered as sparse, adversarial feature generators that present interpretable evidence for either the thesis or the antithesis. In contrast to other black-box methods, the arguments allow users to get an understanding of the decision of the judge. Since the focus of this work is to create an explainable method that maintains a competitive predictive accuracy, we benchmark our method on the triple classification and link prediction task. Thereby, we find that our method outperforms several baselines on the benchmark datasets FB15k-237, WN18RR, and Hetionet. We also conduct a survey and find that the extracted arguments are informative for users.