Author name cluster

Marco Lippi 0001

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3 papers

1 author row

ECAI Conference 2025 Conference Paper

Explainable Artificial Intelligence for Quality Estimation of MARSIS Observations

Benedetta Ferrari
Marco Lippi 0001
Giulio Ganzerli
Manuel Iori
Roberto Orosei

Planetary remote sensing missions are critical for advancing our understanding of extraterrestrial systems. They operate in highly uncertain environments where reliability and resolution are not always guaranteed, often compromising data analysis and scientific outcomes. In this paper, we consider the challenging task of estimating the quality of the signal acquired by MARSIS, the subsurface sounder aboard ESA’s Mars Express mission, which aims to map the presence of liquid water beneath the Martian surface. Quality estimation has a strategic impact on the scheduling of MARSIS observations, since the radar operates with strict constraints that greatly limit the number and size of observation opportunities available per day. Thus, maximizing the quality of scheduled observations becomes a crucial factor in reducing resource utilization and increasing the coverage of the target areas in search of liquid water. To this end, in a previous research we proposed a predict-then-optimize approach, which included a neural network regressor to predict signal quality achievable by future observation opportunities, based on contextual features. In this work, we advance the methodology by applying explainable artificial intelligence techniques that allow domain experts to interpret the results, by enhancing the comprehension of the physical phenomena that have an impact on signal acquisition. Specifically, we applied a SHAP analysis to the neural network predictions and trained an Explainable Boosting Machine (EBM) to provide interpretable models. We then analyzed and compared the results with existing domain knowledge, uncovering promising new avenues for investigation and highlighting limitations in the current dataset construction.

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NeSy Conference 2023 Conference Paper

The Challenge of Learning Symbolic Representations

Luca Salvatore Lorello
Marco Lippi 0001

Learning abstract representations from perceptual stimuli is a natural task for humans, but a real challenge for AI systems. In the vast majority of cases, in fact, systems that have to deal with symbol manipulation, like in reasoning or planning, do not need to also learn the symbols they operate on, but these are typically assumed to be given by a supervisor. Moreover, symbolic manipulation often implies compositional properties which are difficult to learn. In this paper, we consider the problem of learning symbolic representations that can be associated to abstract concepts, to be used in a variety of downstream tasks, and we analyze the many challenges related to this important problem, with a particular emphasis on paving the way towards composable symbolic representations learned by neural networks. We identify key properties for symbolic composable representations, such as non-ambiguity and purity, that suggest the need for different types of regularizations within the learning process, as well as new metrics for their evaluation.

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SoCS Conference 2012 Conference Paper

Efficient Single Frontier Bidirectional Search

Marco Lippi 0001
Marco Ernandes
Ariel Felner

The Single Frontier Bi-Directional Search (SBS) framework was recently introduced. A node in SBS corresponds to a pair of states, one from each of the frontiers and it uses front-to- front heuristics. In this paper we present an enhanced version of SBS, called eSBS, where pruning and caching techniques are applied, which significantly reduce both time and memory needs of SBS. We then present a hybrid of eSBS and IDA∗ which potentially uses only the square root of the memory required by A∗ but enables to prune many nodes that IDA∗ would generate. Experimental results show the benefit of our new approaches on a number of domains.

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