Since its first description, the acute respiratory distress syndrome (ARDS) has been acknowledged to be a major clinical problem in respiratory medicine. From July 2015 to July 2016 almost 300 indexed articles were published on ARDS. This review summarises only eight of them as an arbitrary overview of clinical relevance: definition and epidemiology, risk factors, prevention and treatment. A strict application of definition criteria is crucial, but the diverse resource-setting scenarios foster geographic variability and contrasting outcome data. A large international multicentre prospective cohort study including 50 countries across five continents reported that ARDS is underdiagnosed, and there is potential for improvement in its management. Furthermore, epidemiological data from low-income countries suggest that a revision of the current definition of ARDS is needed in order to improve its recognition and global clinical outcome. In addition to the well-known risk-factors for ARDS, exposure to high ozone levels and low vitamin D plasma concentrations were found to be predisposing circumstances. Drug-based preventive strategies remain a major challenge, since two recent trials on aspirin and statins failed to reduce the incidence in atrisk patients. A new disease-modifying therapy is awaited: some recent studies promised to improve the prognosis of ARDS, but mortality and disabling complications are still high in survivors in intensive care. Definition and epidemiologySince its first description by ASHBAUGH et al. [1] in 1967, the acute respiratory distress syndrome (ARDS) has been widely recognised as a major clinical problem worldwide, carrying a high morbidity and mortality burden [2][3][4]. Although the recent Berlin definition [5] is probably much better than previous ones, there is still a high variability in both epidemiology and clinical outcomes in diverse healthcare settings [4]. In fact, the incidence of ARDS ranges from 1.5 cases per 100 000 [2] to nearly 79 cases per 100 000 [3], with European countries reporting a lower incidence than USA [6]. Moreover, studies from Brazil reported incidence rates ranging from 1.8 to 31 per 100 000 [7,8].Although the overall survival rate is improving [9,10], there is a notable difference when considering in-hospital mortality over several observational studies [2][3][4][8][9][10][11]. This may be explained by differences in risk factors, availability of diagnostics, ability to recognise ARDS and some selection biases affecting clinical trials [12]. Recently, a large international observational study (the LUNG SAFE trial) evaluated the incidence of ARDS across 459 intensive care units (ICUs) in 50 countries [13]. To assess the clinical recognition of ARDS according to the latest definition, any patient inclusion into the trial was made through a computer algorithm following the Berlin criteria [5], and then compared to the diagnosis made by the attending physicians. Among 4499 patients who developed acute hypoxaemic respiratory failure, ARDS occurred in 1...

This paper presents two prototypical epistemic forward planners, called EFP and PG-EFP, for generating plans in multi-agent environments. These planners differ from recently developed epistemic planners in that they can deal with unlimited nested beliefs, common knowledge, and capable of generating plans with both knowledge and belief goals. EFP is simply a breadth first search planner while PG-EFP is a heuristic search based system. To generate heuristics in PG-EFP, the paper introduces the notion of an epistemic planning graph. The paper includes an evaluation of the planners using benchmarks collected from the literature and discusses the issues that affect their scalability and efficiency, thus identifies potentially directions for future work. It also includes experimental evaluation that proves the usefulness of epistemic planning graphs.

Multi-agent systems have been employed to model, simulate and explore a variety of real-world scenarios. It is becoming more and more important to investigate formalisms and tools that would allow us to exploit automated reasoning in these domains. An area that has received increasing attention is the use of multi-agent systems which allow an agent to reason about the knowledge and beliefs of other agents. This type of reasoning, i.e., about agents' perception of the world and also about agents' knowledge of her and others' knowledge, is referred to as epistemic reasoning. This paper presents an updated formalization and implementation of a multi-agent epistemic planner, called EFP. In particular, the paper explores the advantages of using alternative state representations that deviate from the commonly used Kripke structures. The paper explores such alternatives in the context of an action language for multi-agent epistemic planning. The paper presents also an actual implementation of a planner that uses the novel ideas, demonstrating concrete performance improvements on benchmarks collected from the literature.

This paper proposes a research direction to advance AI which draws inspiration from cognitive theories of human decision making. The premise is that if we gain insights about the causes of some human capabilities that are still lacking in AI (for instance, adaptability, generalizability, common sense, and causal reasoning), we may obtain similar capabilities in an AI system by embedding these causal components. We hope that the high-level description of our vision included in this paper, as well as the several research questions that we propose to consider, can stimulate the AI research community to define, try and evaluate new methodologies, frameworks, and evaluation metrics, in the spirit of achieving a better understanding of both human and machine intelligence. Motivation and Overall VisionAI systems have seen dramatic advancement in recent years, bringing many successful applications that are pervading our everyday life. However, we are still mostly seeing instances of narrow AI: each of these developments are typically focused on a very limited set of competences and goals, e.g., image interpretation, natural language processing, label classification, prediction, and many others. Moreover, while these successes can be accredited to improved algorithms and techniques, they are also tightly linked to the availability of huge datasets and computational power (Marcus 2020). State-of-the-art AI still lacks many capabilities that would naturally be included in a notion of intelligence, for example, if we compare these AI technologies to what human beings are able to do. Examples of these capabilities are generalizability, robustness, explainability, causal analysis, abstraction, common sense reasoning, ethics reasoning, as well as a complex and seamless integration of learning and reasoning supported by both implicit and explicit knowledge.Majority of the AI community make sturdy attempts to address the current limitations of AI and create systems that display the ability for more human-like qualities, using a variety of approaches. One of the main debates is whether endto-end neural network approaches can achieve this goal? or whether we need to integrate machine learning with symbolic and logic-based AI techniques? We believe that the integration route is the most promising, and this is supported

Designing agents that reason and act upon the world has always been one of the main objectives of the Artificial Intelligence community. While for planning in “simple” domains the agents can solely rely on facts about the world, in several contexts, e.g., economy, security, justice and politics, the mere knowledge of the world could be insufficient to reach a desired goal. In these scenarios, epistemic reasoning, i.e., reasoning about agents’ beliefs about themselves and about other agents’ beliefs, is essential to design winning strategies. This paper addresses the problem of reasoning in multi-agent epistemic settings exploiting declarative programming techniques. In particular, the paper presents an actual implementation of a multi-shot Answer Set Programming-based planner that can reason in multi-agent epistemic settings, called PLATO (ePistemic muLti-agent Answer seT programming sOlver). The ASP paradigm enables a concise and elegant design of the planner, w.r.t. other imperative implementations, facilitating the development of formal verification of correctness. The paper shows how the planner, exploiting an ad-hoc epistemic state representation and the efficiency of ASP solvers, has competitive performance results on benchmarks collected from the literature.

As the interest in Artificial Intelligence continues to grow it is becoming more and more important to investigate formalization and tools that allow us to exploit logic to reason about the world. In particular, given the increasing number of multi-agents systems that could benefit from techniques of automated reasoning, exploring new ways to define not only the world's status but also the agents' information is constantly growing in importance. This type of reasoning, i.e., about agents' perception of the world and also about agents' knowledge of her and others' knowledge, is referred to as epistemic reasoning.In our work we will try to formalize this concept, expressed through epistemic logic, for dynamic domains. In particular we will attempt to define a new action-based language for multi-agent epistemic planning and to implement an epistemic planner based on it. This solver should provide a tool flexible enough to be able to reason on different domains, e.g., economy, security, justice and politics, where reasoning about others' beliefs could lead to winning strategies or help in changing a group of agents' view of the world.

Nudging is a behavioral strategy aimed at influencing people's thoughts and actions. Nudging techniques can be found in many situations in our daily lives, and these nudging techniques can targeted at human fast and unconscious thinking, e.g., by using images to generate fear or the more careful and effortful slow thinking, e.g., by releasing information that makes us reflect on our choices. In this paper, we propose and discuss a value-based AIhuman collaborative framework where AI systems nudge humans by proposing decision recommendations. Three different nudging modalities, based on when recommendations are presented to the human, are intended to stimulate human fast thinking, slow thinking, or meta-cognition. Values that are relevant to a specific decision scenario are used to decide when and how to use each of these nudging modalities. Examples of values are decision quality, speed, human upskilling and learning, human agency, and privacy. Several values can be present at the same time, and their priorities can vary over time. The framework treats values as parameters to be instantiated in a specific decision environment.

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