32 citations · 91 across the 34 of their papers we have counts for
34 papers
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…
Hierarchical Multi-Agent Reinforcement Learning for Air Combat Maneuvering
Ardian Selmonaj, Oleg Szehr, Giacomo Del Rio +3
The application of artificial intelligence to simulate air-to-air combat scenarios is attracting increasing attention. To date the high-dimensional state and action spaces, the hig…
Approximating Counterfactual Bounds while Fusing Observational, Biased and Randomised Data Sources
Marco Zaffalon, Alessandro Antonucci, Rafael Cabañas +1
We address the problem of integrating data from multiple, possibly biased, observational and interventional studies, to eventually compute counterfactuals in structural causal mode…
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…
MementoHash: A Stateful, Minimal Memory, Best Performing Consistent Hash Algorithm
Massimo Coluzzi, Amos Brocco, Alessandro Antonucci +1
Consistent hashing is used in distributed systems and networking applications to spread data evenly and efficiently across a cluster of nodes. In this paper, we present MementoHash…
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…
Modelling Assessment Rubrics through Bayesian Networks: a Pragmatic Approach
Francesca Mangili, Giorgia Adorni, Alberto Piatti +2
Automatic assessment of learner competencies is a fundamental task in intelligent tutoring systems. An assessment rubric typically and effectively describes relevant competencies a…
Bounding Counterfactuals under Selection Bias
Marco Zaffalon, Alessandro Antonucci, Rafael Cabañas +2
Causal analysis may be affected by selection bias, which is defined as the systematic exclusion of data from a certain subpopulation. Previous work in this area focused on the deri…
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…
Humans as Path-Finders for Safe Navigation
Alessandro Antonucci, Paolo Bevilacqua, Stefano Leonardi +2
One of the most important barriers toward a widespread use of mobile robots in unstructured and human populated work environments is the ability to plan a safe path. In this paper,…
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…
Robust Model Checking with Imprecise Markov Reward Models
Alberto Termine, Alessandro Antonucci, Alessandro Facchini +1
In recent years probabilistic model checking has become an important area of research because of the diffusion of computational systems of stochastic nature. Despite its great succ…
Relation Clustering in Narrative Knowledge Graphs
Simone Mellace, K Vani, Alessandro Antonucci
When coping with literary texts such as novels or short stories, the extraction of structured information in the form of a knowledge graph might be hindered by the huge number of p…
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…
SST-BERT at SemEval-2020 Task 1: Semantic Shift Tracing by Clustering in BERT-based Embedding Spaces
K Vani, Sandra Mitrovic, Alessandro Antonucci +1
Lexical semantic change detection (also known as semantic shift tracing) is a task of identifying words that have changed their meaning over time. Unsupervised semantic shift traci…
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…