The COVID-19 pandemic considerably affects public health systems around the world. The lack of knowledge about the virus, the extension of this phenomenon, and the speed of the evolution of the infection are all factors that highlight the necessity of employing new approaches to study these events. Artificial intelligence techniques may be useful in analyzing data related to areas affected by the virus. The aim of this work is to investigate any possible relationships between air quality and confirmed cases of COVID-19 in Italian districts. Specifically, we report an analysis of the correlation between daily COVID-19 cases and environmental factors, such as temperature, relative humidity, and atmospheric pollutants. Our analysis confirms a significant association of some environmental parameters with the spread of the virus. This suggests that machine learning models trained on the environmental parameters to predict the number of future infected cases may be accurate. Predictive models may be useful for helping institutions in making decisions for protecting the population and contrasting the pandemic.

Abstract In many machine learning scenarios, looking for the best classifier that fits a particular dataset can be very costly in terms of time and resources. Moreover, it can require deep knowledge of the specific domain. We propose a new technique which does not require profound expertise in the domain and avoids the commonly used strategy of hyper-parameter tuning and model selection. Our method is an innovative ensemble technique that uses voting rules over a set of randomly-generated classifiers. Given a new input sample, we interpret the output of each classifier as a ranking over the set of possible classes. We then aggregate these output rankings using a voting rule, which treats them as preferences over the classes. We show that our approach obtains good results compared to the state-of-the-art, both providing a theoretical analysis and an empirical evaluation of the approach on several datasets.

An Abstract Argumentation Framework (AAF) is an abstract structure consisting of a set arguments, whose origin, nature, and possible internal organisation is not specified, and by a binary relation of attack on the set of arguments, whose meaning is not specified either. Subjective logic provides a standard set of logical operators, intended for use in domains containing uncertainty. In this paper, we define an extension of AAFs in which each argument and attacks is evaluated with an opinion, by revisiting the constellations approach developed for probabilistic AAFs. In this way, different agents can merge their opinions on how much arguments and attacks are “trustworthy”, e.g., they do not represent fallacies or enthymemes. Finally, subjective logic operators can be used to fuse the belief of different possible worlds (i.e., a constellation of sub-graphs in the original AAF) containing different arguments and attacks.

We consider multi-agent settings where a set of agents want to take a collective decision, based on their preferences over the possible candidate options. While agents have their initial inclination, they may interact and influence each other, and therefore modify their preferences, until hopefully they reach a stable state and declare their final inclination. At that point, a voting rule is used to aggregate the agents’ preferences and generate the collective decision. Recent work has modeled the influence phenomenon in the case of voting over a single issue. Here we generalize this model to account for preferences over combinatorially structured domains including several issues. We propose a way to model influence when agents express their preferences as CP-nets. We define two procedures for aggregating preferences in this scenario, by interleaving voting and influence convergence, and study their resistance to bribery.

We consider a multi-agent scenario where a collection of agents needs to select a common decision from a large set of decisions over which they express their preferences. This decision set has a combinatorial structure, that is, each decision is an element of the Cartesian product of the domains of some variables. Agents express their preferences over the decisions via soft constraints. We consider both sequential preference aggregation methods (they aggregate the preferences over one variable at a time) and one-step methods and we study the computational complexity of influencing them through bribery. We prove that bribery is NPcomplete for the sequential aggregation methods (based on Plurality, Approval, and Borda) for most of the cost schemes we defined, while it is polynomial for one-step Plurality.

We consider scenarios where several agents must aggregate their preferences over a large set of candidates with a combinatorial structure. That is, each candidate is an element of the Cartesian product of the domains of some variables. We assume agents compactly express their preferences over the candidates via soft constraints. We consider a sequential procedure that chooses one candidate by asking the agents to vote on one variable at a time. While some properties of this procedure have been already studied, here we focus on independence of irrelevant alternatives, non-dictatorship, and strategy-proofness. Also, we perform an experimental study that shows that the proposed sequential procedure yields a considerable saving in time with respect to a non-sequential approach, while the winners satisfy the agents just as well, independently of the variable ordering and of the presence of coalitions of agents. 1

The stable marriage problem is a well-known problem of matching men to women so that no man and woman, who are not married to each other, both prefer each other. Such a problem has a wide variety of practical applications, ranging from matching resident doctors to hospitals, to matching students to schools or more generally to any two-sided market. In the classical stable marriage problem, both men and women express a strict preference order over the members of the other sex, in a qualitative way. Here we consider stable marriage problems with weighted preferences: each man (resp., woman) provides a score for each woman (resp., man). In this context, we consider the manipulability properties of the procedures that return stable marriages. While we know that all procedures are manipulable by modifying the preference lists or by truncating them, here we consider if manipulation can occur also by just modifying the weights while preserving the ordering and avoiding truncation. It turns out that, by adding weights, we indeed increase the possibility of manipulating and this cannot be avoided by any reasonable restriction on the weights.

Preferences and uncertainty are common in many real-life problems. In this article, we focus on bipolar preferences and uncertainty modelled via uncontrollable variables, and we assume that uncontrollable variables are specified by possibility distributions over their domains. To tackle such problems, we concentrate on uncertain bipolar problems with totally ordered preferences, and we eliminate the uncertain part of the problem, while making sure that some desirable properties hold about the robustness of the problem and its relationship with the preference of the optimal solutions. We also consider several semantics to order the solutions according to different attitudes with respect to the notions of preference and robustness.

The stable marriage problem (SM) has a wide variety of practical applications, ranging from matching resident doctors to hospitals, to matching students to schools, or more generally to any two-sided market. In the classical formulation, n men and n women express their preferences over the members of the other sex. Solving an SM means finding a stable marriage: a matching of men to women with no blocking pair. A blocking pair consists of a man and a woman who are not married to each other but both prefer each other to their partners. It is possible to find a male-optimal (resp., female-optimal) stable marriage in polynomial time. However, it is sometimes desirable to find stable marriages without favoring a group at the expenses of the other one. In this paper we present a local search approach to find stable marriages. Our experiments show that the number of steps grows as little as O(nlog(n)). We also show empirically that the proposed algorithm samples very well the set of all stable marriages of a given SM, thus providing a fair and efficient approach to generate stable marriages.

Real-life problems present several kinds of preferences. We focus on problems with both positive and negative preferences, which we call bipolar preference problems. Although seemingly specular notions, these two kinds of preferences should be dealt with differently to obtain the desired natural behaviour. We technically address this by generalising the soft constraint formalism, which is able to model problems with one kind of preference. We show that soft constraints model only negative preferences, and we add to them a new mathematical structure which allows to handle positive preferences as well. We also address the issue of the compensation between positive and negative preferences, studying the properties of this operation. Finally, we extend the notion of arc consistency to bipolar problems, and we show how branch and bound (with or without constraint propagation) can be easily adapted to solve such problems.

Preferences and uncertainty are common in many real-life problems. In this article, we consider preferences modelled via soft constraints that allow for the representation of quantitative preferences. Moreover, we consider uncertainty modelled via uncontrollable variables, that is, variables whose value cannot be decided by us. We also assume that some information is provided for such variables in the form of a possibility distribution over their domains. Possibilities provide a way to model imprecise probabilities, and give us a way to know which values are more possible than others for the uncontrollable variables. For such problems, the aim is to find a solution with a high preference which is also very robust with respect to the uncontrollable part. To tackle such problems, we adopt an existing approach that eliminates the uncertain part of the problem while adding some constraints in the remaining part, and then solves the resulting problem. However, contrarily to the specific methods present in the literature, we formulate several desirable properties, on the robustness of the problem's solutions and its relationship with their preferences, that should be satisfied by any specific method based on this approach. We also define several semantics to order the solutions according to different attitudes to risk, and we discuss which of the desirable properties are satisfied by each of the considered semantics. Finally, we present a solver for this kind of problems, and we show some experimental results of its application over the classes of such problems.

Abstract. Preferences are not always expressible via complete linear orders: sometimes it is more natural to allow for the presence of incomparable outcomes. This may hold both in the agents ’ preference ordering and in the social order. In this paper we consider this scenario and we study what properties it may have. In particular, we show that, despite the added expressivity and ability to resolve conflicts provided by incomparability, classical impossibility results (such as Arrow’s theorem, Muller-Satterthwaite’s theorem, and Gibbard-Satterthwaite’s theorem) still hold. We also prove some possibility results, generalizing Sen’s theorem for majority voting. To prove these results, we define new notions of unanimity, monotonicity, dictator, triple-wise value-restriction, and strategy-proofness, which are suitable and natural generalizations of the classical ones for complete orders. 1