Ivan Serina

This paper introduces LeapNP (Learning and Planning Framework for Numeric Problems), a lightweight, Python-native framework engineered to support both classical and numeric planning tasks. Designed with a fully modular interface, it specifically aims to facilitate the seamless integration of deep learning methodologies. The design philosophy of LeapNP stems from the observation that traditional planners, while highly efficient, lack the necessary flexibility for experimental research, particularly at the intersection of learning and planning. Most state-of-the-art engines are built as highly optimized, rigid executables that are resistant to internal modification. LeapNP disrupts this paradigm by offering a framework where the entire planning stack is accessible and mutable. Users can seamlessly plug in custom implementations for grounding, define novel state representations, or design bespoke search strategies, thereby enabling a level of integration with learning models that is currently impractical with standard tools. By significantly lowering the engineering barrier, our planner fosters rapid experimentation and accelerates research in neuro-symbolic planning. We also present a comprehensive suite of search algorithms, designed to evaluate different properties of learned heuristics. These include two algorithms designed to exploit batching to maximize inference throughput, and a greedy algorithm meant to test the intrinsic robustness of the learned models, running them as general policies.

In real-world scenarios, the successful execution of an agent’s planned actions is not always guaranteed, as actions may fail in unpredictable ways that are not explicitly modeled. To address this challenge, the concept of Resilient Planning and the RESPLAN framework were introduced focusing on the generation of k-resilient plans that enable an agent to reach its goals even in the presence of up to k execution failures. In this paper, we propose a new version of the RESPLAN planning algorithm based on two significant enhancements. The first incorporates landmarks into a pruning strategy, enabling the planner to avoid unnecessary explorations and yielding substantial performance gains, especially when no resilient plan exists. The second introduces a planning adaptation strategy exploiting regressed state formulas to support the search process during (re)planning, reducing the number of iterations required when a resilient plan does exist. We compare our methods against RESPLAN and other baselines, demonstrating substantial improvements across multiple domains.

In recent years, various studies have been carried out to assess whether Large Language Models (LLMs) possess different reasoning capabilities, including those required in automated planning. Typically, these studies provide the LLM with a planning domain and a problem, specified by an initial state and a goal, and require the LLM model to generate a plan solving the problem. Despite this common configuration, such studies significantly differ in the used models, the information provided to the model, the possible involvement of symbolic planners, and the experimental approaches used for the evaluation. Motivated by the growing interest in LLMs and in the understanding of their reasoning abilities, in this work we offer a concise review of recent studies on using LLMs for planning. We outline the main research trends and discuss their most notable findings. Furthermore, we identify key challenges and highlight critical aspects to consider when evaluating a LLM in terms of learning to plan and generating solution plans.

In this paper, we investigate the application of heuristics based on Graph Neural Networks (GNNs) to lifted numeric planning problems, an area that has been relatively unexplored. Building upon the GNN approach for learning general policies proposed by Staahlberg et al., we extend the architecture to make it sensitive to the numeric components inherent in the planning problems we address. We achieve this by observing that, although the state space of a numeric planning problem is infinite, the finite subgoal structure of the problem can be incorporated into the architecture, allowing for the construction of only a finite number of nodes. Instead of learning general policies, we train our models to function as a heuristic within a best-first search algorithm. We explore various configurations of this architecture and demonstrate that the resulting heuristics are highly informative and, in certain domains, offer a better trade-off between guidance and computational cost compared to other inductive and deductive heuristics.

Land cover mapping is essential to understanding global land-use patterns and studying biodiversity composition and the functioning of eco-systems. The introduction of remote sensing technologies and artificial intelligence models made it possible to base land cover mapping on satellite imagery in order to monitor changes, assess ecosystem health, support conservation efforts, and reduce monitoring time. However, significant challenges remain in managing large, complex satellite imagery datasets, acquiring specialized datasets due to high costs and labor intensity, including a lack of comparative studies for the selection of optimal deep learning models. No less important is the scarcity of aerial datasets specifically tailored for agricultural areas. This study addresses these gaps by presenting a methodology for semantic segmentation of land covers in agricultural areas using satellite images and deep learning models with pre-trained backbones. We introduce an efficient methodology for preparing semantic segmentation datasets and contribute the “Land Cover Aerial Imagery” (LICAI) dataset for semantic segmentation. The study focuses on the Franciacorta area, Lombardy Region, leveraging the rich diversity of the dataset to effectively train and evaluate the models. We conducted a comparative study, using cutting-edge deep-learning-based segmentation models (U-Net, SegNet, DeepLabV3) with various pre-trained backbones (ResNet, Inception, DenseNet, EfficientNet) on our dataset acquired from Google Earth Pro. Through meticulous data acquisition, preprocessing, model selection, and evaluation, we demonstrate the effectiveness of these techniques in accurately identifying land cover classes. Integrating pre-trained feature extraction networks significantly improves performance across various metrics. Additionally, addressing challenges such as data availability, computational resources, and model interpretability is essential for advancing the field of remote sensing, in support of biodiversity conservation and the provision of ecosystem services and sustainable agriculture.

The automatic identification of plant species using unmanned aerial vehicles (UAVs) is a valuable tool for ecological research. However, challenges such as reduced spatial resolution due to high-altitude operations, image degradation from camera optics and sensor limitations, and information loss caused by terrain shadows hinder the accurate classification of plant species from UAV imagery. This study addresses these issues by proposing a novel image preprocessing pipeline and evaluating its impact on model performance. Our approach improves image quality through a multi-step pipeline that includes Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN) for resolution enhancement, Contrast-Limited Adaptive Histogram Equalization (CLAHE) for contrast improvement, and white balance adjustments for accurate color representation. These preprocessing steps ensure high-quality input data, leading to better model performance. For feature extraction and classification, we employ a pre-trained VGG-16 deep convolutional neural network, followed by machine learning classifiers, including Support Vector Machine (SVM), random forest (RF), and Extreme Gradient Boosting (XGBoost). This hybrid approach, combining deep learning for feature extraction with machine learning for classification, not only enhances classification accuracy but also reduces computational resource requirements compared to relying solely on deep learning models. Notably, the VGG-16 + SVM model achieved an outstanding accuracy of 97.88% on a dataset preprocessed with ESRGAN and white balance adjustments, with a precision of 97.9%, a recall of 97.8%, and an F1 score of 0.978. Through a comprehensive comparative study, we demonstrate that the proposed framework, utilizing VGG-16 for feature extraction, SVM for classification, and preprocessed images with ESRGAN and white balance adjustments, achieves superior performance in plant species identification from UAV imagery.