Institution profile

Polytechnic of Bari

Academic institutioneurope · it
Official website
Research library27linked papers
Opportunities0open roles
Selected work

Representative Papers

Who Are You Explaining To? A Multi-Agent System for Audience-Aware XAI Narratives

Aug 11, 2026

Existing explainable AI (XAI) methods, such as SHAP, struggle to deliver explanations that are both faithful to model evidence and appropriately tailored to audiences with diverse professional backgrounds and risk sensitivities—particularly in high-stakes domains like healthcare. To address this gap, this work proposes XstrAI, the first audience-aware, multi-agent XAI narrative framework. Treating local explanations as fixed evidence, XstrAI employs three specialized LLM agents—planning, realization, and verification—that collaboratively generate customized narratives. A bounded revision loop, grounded in inconsistency detection, ensures fidelity and safety. Experiments on diabetes and stroke risk prediction tasks demonstrate that XstrAI significantly outperforms eleven baselines; its explanations are most preferred by both patients and clinicians, remain competitive among data scientists, and enable independent reviewers to accurately identify the intended audience.

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Hierarchical Topology-Aware Planning and Control of Underwater Vehicle-Manipulator Systems in Confined Environments

Aug 09, 2026

This work addresses the challenge of underwater manipulator robots becoming irrecoverably stuck in confined, cluttered, and partially known environments due to limited maneuverability, narrow passages, and actuation uncertainty. To overcome this, the authors propose MANTA, a three-layer hierarchical planning and control framework that integrates topological-level global connectivity reasoning, base-arm coupled trajectory optimization, and a Gaussian process model-based reinforcement learning closed-loop controller (MC-PILCO), enabling dynamic replanning and real-time map updates. Experimental results across 120 trials demonstrate that the proposed approach significantly outperforms baseline methods in task success rate, achieves greater path clearance, reduces manipulator motion, and substantially lowers position and yaw tracking errors.

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Recent publications

Latest Papers

Who Are You Explaining To? A Multi-Agent System for Audience-Aware XAI Narratives

Aug 11, 2026

Existing explainable AI (XAI) methods, such as SHAP, struggle to deliver explanations that are both faithful to model evidence and appropriately tailored to audiences with diverse professional backgrounds and risk sensitivities—particularly in high-stakes domains like healthcare. To address this gap, this work proposes XstrAI, the first audience-aware, multi-agent XAI narrative framework. Treating local explanations as fixed evidence, XstrAI employs three specialized LLM agents—planning, realization, and verification—that collaboratively generate customized narratives. A bounded revision loop, grounded in inconsistency detection, ensures fidelity and safety. Experiments on diabetes and stroke risk prediction tasks demonstrate that XstrAI significantly outperforms eleven baselines; its explanations are most preferred by both patients and clinicians, remain competitive among data scientists, and enable independent reviewers to accurately identify the intended audience.

0 citationsRead paper

Hierarchical Topology-Aware Planning and Control of Underwater Vehicle-Manipulator Systems in Confined Environments

Aug 09, 2026

This work addresses the challenge of underwater manipulator robots becoming irrecoverably stuck in confined, cluttered, and partially known environments due to limited maneuverability, narrow passages, and actuation uncertainty. To overcome this, the authors propose MANTA, a three-layer hierarchical planning and control framework that integrates topological-level global connectivity reasoning, base-arm coupled trajectory optimization, and a Gaussian process model-based reinforcement learning closed-loop controller (MC-PILCO), enabling dynamic replanning and real-time map updates. Experimental results across 120 trials demonstrate that the proposed approach significantly outperforms baseline methods in task success rate, achieves greater path clearance, reduces manipulator motion, and substantially lowers position and yaw tracking errors.

0 citationsRead paper