Pubblicazioni

[c4]

Strategic Reasoning over Golog Programs in the Nondeterministic Situation Calculus

Giuseppe De Giacomo, Yves Lespérance, Matteo Mancanelli

To appear in Proc. of the 40th Annual AAAI Conference on Artificial Intelligence (AAAI 2026), Singapore, SG.

[abstract] [paper]

We investigate the problem of synthesizing strategies that guarantee the successful execution of a high-level nondeterministic agent program δ in Golog within a nondeterministic first-order basic action theory, considering the environment as adversarial. Our approach constructs a symbolic program graph that captures the control flow of δ independently of the domain, enabling strategy synthesis through the cross product of the program graph with the domain model. We formally relate graph-based transitions to standard Golog semantics and provide a synthesis procedure that is sound though incomplete (in general, the problem is undecidable, given that we have a first-order representation of the state). We also extend the framework to handle the case where the environment's possible behaviors are specified by a Golog program.

[c3]

Situation Calculus Temporally Lifted Abstractions for Generalized Planning

Giuseppe De Giacomo, Yves Lespérance, Matteo Mancanelli

Proc. of the 39th Annual AAAI Conference on Artificial Intelligence (AAAI 2025), Philadelphia, PA, USA.

[abstract] [doi] [paper] [bibtex]

We present a new formal framework for generalized planning (GP) based on the situation calculus extended with LTL constraints. The GP problem is specified by a first-order basic action theory whose models are the problem instances. This low-level theory is then abstracted into a high-level propositional nondeterministic basic action theory with a single model. A refinement mapping relates the two theories. LTL formulas are used to specify the temporally extended goals as well as assumed trace constraints. If all LTL trace constraints hold at the low level and the high-level model can simulate all the low-level models with respect to the mapping, we say that we have a temporally lifted abstraction. We prove that if we have such an abstraction and the agent has a strategy to achieve a LTL goal under some trace constraints at the abstract level, then there exists a refinement of the strategy to achieve the refinement of the goal at the concrete level. We use LTL synthesis to generate the strategy at the abstract level. We illustrate our approach by synthesizing a program that solves a data structure manipulation problem.

@inproceedings{de2025situation,
title={Situation Calculus Temporally Lifted Abstractions for Generalized Planning},
author={De Giacomo, Giuseppe and Lesp{\'{e}}rance, Yves and Mancanelli, Matteo},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
volume={39},
number={14},
pages={14848--14857},
year={2025}
}

[i1]

Neuro-Symbolic Predictive Process Monitoring

Axel Mezini, Elena Umili, Ivan Donadello, Fabrizio Maria Maggi, Matteo Mancanelli, Fabio Patrizi

[abstract] [paper] [bibtex]

This paper addresses the problem of suffix prediction in Business Process Management (BPM) by proposing a Neuro-Symbolic Predictive Process Monitoring (PPM) approach that integrates data-driven learning with temporal logic-based prior knowledge. While recent approaches leverage deep learning models for suffix prediction, they often fail to satisfy even basic logical constraints due to the absence of explicit integration of domain knowledge during training. We propose a novel method to incorporate Linear Temporal Logic over finite traces (LTLf) into the training process of autoregressive sequence predictors. Our approach introduces a differentiable logical loss function, defined using a soft approximation of LTLf semantics and the Gumbel-Softmax trick, which can be combined with standard predictive losses. This ensures the model learns to generate suffixes that are both accurate and logically consistent. Experimental evaluation on three real-world datasets shows that our method improves suffix prediction accuracy and compliance with temporal constraints. We also introduce two variants of the logic loss (local and global) and demonstrate their effectiveness under noisy and realistic settings. While developed in the context of BPM, our framework is applicable to any symbolic sequence generation task and contributes toward advancing Neuro-Symbolic AI.

@article{mezini2025neuro,
title={Neuro-Symbolic Predictive Process Monitoring},
author={Mezini, Axel and Umili, Elena and Donadello, Ivan and Maggi, Fabrizio Maria and Mancanelli, Matteo and Patrizi, Fabio},
journal={arXiv preprint arXiv:2509.00834},
year={2025} }

[c2]

Situation Calculus Temporally Lifted Abstractions for Generalized Planning - Extended Abstract

Giuseppe De Giacomo, Yves Lespérance, Matteo Mancanelli

Workshop on Symbolic and Neuro-Symbolic Architectures for Intelligent Robotics Technology (SYNERGY 2024), co-located with KR 2024, Hanoi, Vietnam.

[abstract] [paper] [bibtex]

We present a new formal framework for generalized planning (GP) based on the situation calculus extended with LTL constraints. The GP problem is specified by a first-order basic action theory whose models are the problem instances. This low-level theory is then abstracted into a high-level propositional nondeterministic basic action theory with a single model. A refinement mapping relates the two theories. LTL formulas are used to specify the temporally extended goals as well as assumed trace constraints. If all LTL trace constraints hold at the low level and the high-level model can simulate all the low-level models with respect to the mapping, we say that we have a temporally lifted abstraction. We prove that if we have such an abstraction and the agent has a strategy to achieve a LTL goal under some trace constraints at the abstract level, then there exists a refinement of the strategy to achieve the refinement of the goal at the concrete level. We use LTL synthesis to generate the strategy at the abstract level. We illustrate our approach by synthesizing a program that solves a data structure manipulation problem.

@inproceedings{DBLP:conf/kodis/GiacomoLM24,
author = {Giuseppe De Giacomo and Yves Lesp{\'{e}}rance and Matteo Mancanelli},
editor = {Luc{\'{\i}}a G{\'{o}}mez {\'{A}}lvarez and Jonas Haldimann and Jesse Heyninck and Srdjan Vesic and Francesco Fabiano and Marcello Balduccini},
title = {Situation Calculus Temporally Lifted Abstractions for Generalized Planning - Extended Abstract},
booktitle = {Joint Proceedings of the Joint Workshop on Knowledge Diversity and Cognitive Aspects of {KR} and the Workshop on Symbolic and Neuro-Symbolic Architectures for Intelligent Robotics Technology (KoDis-CAKR-SYNERGY 2024) co-located with the 21st International Conference on Principles of Knowledge Representation and Reasoning {(KR} 2024), Hanoi, Vietnam, November 2-8, 2024},
series = {{CEUR} Workshop Proceedings},
volume = {3876},
publisher = {CEUR-WS.org},
year = {2024},
}

[c1]

PHYDI: Initializing Parameterized Hypercomplex Neural Networks as Identity Functions

Matteo Mancanelli, Eleonora Grassucci, Aurelio Uncini, Danilo Comminiello

IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2023), Rome, Italy.

[abstract] [doi] [poster] [codice] [bibtex] Top 5% Outstanding Paper

Neural models based on hypercomplex algebra systems are growing and prolificating for a plethora of applications, ranging from computer vision to natural language processing. Hand in hand with their adoption, parameterized hypercomplex neural networks (PHNNs) are growing in size and no techniques have been adopted so far to control their convergence at a large scale. In this paper, we study PHNNs convergence and propose parameterized hypercomplex identity initialization (PHYDI), a method to improve their convergence at different scales, leading to more robust performance when the number of layers scales up, while also reaching the same performance with fewer iterations. We show the effectiveness of this approach in different benchmarks and with common PHNNs with ResNets- and Transformer-based architecture.

@inproceedings{mancanelli2023MLSP,
author={Mancanelli, Matteo and Grassucci, Eleonora and Uncini, Aurelio and Comminiello, Danilo},
booktitle={2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP)},
title={{PHYDI: I}nitializing Parameterized Hypercomplex Neural Networks as Identity Functions},
year={2023},
organization={IEEE},
pages={1--6},
doi={10.1109/MLSP55844.2023.10285926}
}