32 citations · 91 across the 34 of their papers we have counts for
21 papers · 1 filter
Causal Modelling of Support Interventions for Student Competency Assessment
Francesca Mangili, Alessandro Antonucci, Rafael Cabañas
Accurate assessment of student competencies is essential for enabling educators to identify individual needs, design targeted interventions, and evaluate the effectiveness of educa…
Intelligent tutoring systems by Bayesian nets with noisy gates
Alessandro Antonucci, Francesca Mangili, Claudio Bonesana +1
Directed graphical models such as Bayesian nets are often used to implement intelligent tutoring systems able to interact in real-time with learners in a purely automatic way. When…
Rubric-based Learner Modelling via Noisy Gates Bayesian Networks for Computational Thinking Skills Assessment
Giorgia Adorni, Francesca Mangili, Alberto Piatti +2
In modern and personalised education, there is a growing interest in developing learners' competencies and accurately assessing them. In a previous work, we proposed a procedure fo…
Zero-shot Causal Graph Extrapolation from Text via LLMs
Alessandro Antonucci, Gregorio Piqué, Marco Zaffalon
We evaluate the ability of large language models (LLMs) to infer causal relations from natural language. Compared to traditional natural language processing and deep learning techn…
Tractable Bounding of Counterfactual Queries by Knowledge Compilation
David Huber, Yizuo Chen, Alessandro Antonucci +2
We discuss the problem of bounding partially identifiable queries, such as counterfactuals, in Pearlian structural causal models. A recently proposed iterated EM scheme yields an i…
Efficient Computation of Counterfactual Bounds
Marco Zaffalon, Alessandro Antonucci, Rafael Cabañas +2
We assume to be given structural equations over discrete variables inducing a directed acyclic graph, namely, a structural causal model, together with data about its internal nodes…
Learning to Bound Counterfactual Inference from Observational, Biased and Randomised Data
Marco Zaffalon, Alessandro Antonucci, David Huber +1
We address the problem of integrating data from multiple, possibly biased, observational and interventional studies, to eventually compute counterfactuals in structural causal mode…
Belief Revision in Sentential Decision Diagrams
Lilith Mattei, Alessandro Facchini, Alessandro Antonucci
Belief revision is the task of modifying a knowledge base when new information becomes available, while also respecting a number of desirable properties. Classical belief revision…
ADAPQUEST: A Software for Web-Based Adaptive Questionnaires based on Bayesian Networks
Claudio Bonesana, Francesca Mangili, Alessandro Antonucci
We introduce ADAPQUEST, a software tool written in Java for the development of adaptive questionnaires based on Bayesian networks. Adaptiveness is intended here as the dynamical ch…
Structural Learning of Probabilistic Sentential Decision Diagrams under Partial Closed-World Assumption
Alessandro Antonucci, Alessandro Facchini, Lilith Mattei
Probabilistic sentential decision diagrams are a class of structured-decomposable probabilistic circuits especially designed to embed logical constraints. To adapt the classical Le…
A New Score for Adaptive Tests in Bayesian and Credal Networks
Alessandro Antonucci, Francesca Mangili, Claudio Bonesana +1
A test is adaptive when its sequence and number of questions is dynamically tuned on the basis of the estimated skills of the taker. Graphical models, such as Bayesian networks, ar…
CREPO: An Open Repository to Benchmark Credal Network Algorithms
Rafael Cabañas, Alessandro Antonucci
Credal networks are a popular class of imprecise probabilistic graphical models obtained as a Bayesian network generalization based on, so-called credal, sets of probability mass f…
Causal Expectation-Maximisation
Marco Zaffalon, Alessandro Antonucci, Rafael Cabañas
Structural causal models are the basic modelling unit in Pearl's causal theory; in principle they allow us to solve counterfactuals, which are at the top rung of the ladder of caus…
Tractable Inference in Credal Sentential Decision Diagrams
Lilith Mattei, Alessandro Antonucci, Denis Deratani Mauá +2
Probabilistic sentential decision diagrams are logic circuits where the inputs of disjunctive gates are annotated by probability values. They allow for a compact representation of…
Structural Causal Models Are (Solvable by) Credal Networks
Marco Zaffalon, Alessandro Antonucci, Rafael Cabañas
A structural causal model is made of endogenous (manifest) and exogenous (latent) variables. We show that endogenous observations induce linear constraints on the probabilities of…
A Bayesian Approach to Conversational Recommendation Systems
Francesca Mangili, Denis Broggini, Alessandro Antonucci +2
We present a conversational recommendation system based on a Bayesian approach. A probability mass function over the items is updated after any interaction with the user, with info…
Approximate MMAP by Marginal Search
Alessandro Antonucci, Thomas Tiotto
We present a heuristic strategy for marginal MAP (MMAP) queries in graphical models. The algorithm is based on a reduction of the task to a polynomial number of marginal inference…
Imaginary Kinematics
Sabina Marchetti, Alessandro Antonucci
We introduce a novel class of adjustment rules for a collection of beliefs. This is an extension of Lewis' imaging to absorb probabilistic evidence in generalized settings. Unlike…
Reliable Uncertain Evidence Modeling in Bayesian Networks by Credal Networks
Sabina Marchetti, Alessandro Antonucci
A reliable modeling of uncertain evidence in Bayesian networks based on a set-valued quantification is proposed. Both soft and virtual evidences are considered. We show that eviden…
On the Complexity of Strong and Epistemic Credal Networks
Denis D. Maua, Cassio Polpo de Campos, Alessio Benavoli +1
Credal networks are graph-based statistical models whose parameters take values in a set, instead of being sharply specified as in traditional statistical models (e.g., Bayesian ne…
Epistemic irrelevance in credal nets: the case of imprecise Markov trees
Gert de Cooman, Filip Hermans, Alessandro Antonucci +1
We focus on credal nets, which are graphical models that generalise Bayesian nets to imprecise probability. We replace the notion of strong independence commonly used in credal net…