collaborators

7 papers

cs.MA2026

Translating Natural Language to Strategic Temporal Specifications via LLMs

Marco Aruta, Francesco Improta, Vadim Malvone +2

A rigorous formalization of system requirements is a fundamental prerequisite for the verification of Multi-Agent Systems (MAS). However, writing correct formal specifications is w…

cs.MA2026

A Neuro-Symbolic Approach to Strategy Synthesis for Strategic Logics

Marco Aruta, Vadim Malvone, Aniello Murano +2

Reasoning about what agents can achieve through strategic interaction is a core challenge in Multi-Agent Systems (MAS). Logics for strategic ability, such as ATL, provide rigorous…

cs.LO2026

Verification of Robust Multi-Agent Systems

Raphaël Berthon, Joost-Pieter Katoen, Munyque Mittelmann +1

Stochastic multi-agent systems are a central modeling framework for autonomous controllers, communication protocols, and cyber-physical infrastructures. In many such systems, howev…

cs.MA2025

When Natural Strategies Meet Fuzziness and Resource-Bounded Actions (Extended Version)

Marco Aruta, Francesco Improta, Vadim Malvone +1

In formal strategic reasoning for Multi-Agent Systems (MAS), agents are typically assumed to (i) employ arbitrarily complex strategies, (ii) execute each move at zero cost, and (ii…

cs.LO2025

Repairing General Game Descriptions (extended version)

Yifan He, Munyque Mittelmann, Aniello Murano +2

The Game Description Language (GDL) is a widely used formalism for specifying the rules of general games. Writing correct GDL descriptions can be challenging, especially for non-ex…

cs.CV2025

Latent Space Analysis for Interpretable Uncertainty in Melanoma Classification

Ciro Listone, Aniello Murano

Melanoma is a highly aggressive skin cancer, making early and accurate diagnosis critical. While deep learning excels in skin lesion classification, standard ``black-box" models st…

cs.LG2025

ADNF-Clustering: An Adaptive and Dynamic Neuro-Fuzzy Clustering for Leukemia Prediction

Marco Aruta, Ciro Listone, Giuseppe Murano +1

Leukemia diagnosis and monitoring rely increasingly on high-throughput image data, yet conventional clustering methods lack the flexibility to accommodate evolving cellular pattern…