Even though RAM-neurons may have different discriminative reliability, DRASiW treats their contribution uniformly.In this work, we introduce the new metric RDA (RAM Discrimination Amplifier) that assigns to each RAM a class-specific amplification (or reduction) factor.This factor is calculated from divergence metrics applied to the RAM address distributions.RDA preserves the weightless nature of DRASiW while improving the quality of decision.Experiments on 41 datasets show consistent accuracy and f1-score gains on different evaluation protocols.

Common-sense rules represent a form of implicit knowledge derived from experience and observation, often used by both humans and artificial systems to guide decision-making. In this work, we introduce a novel extension of the DRASiW weightless neural network architecture that enables the automatic extraction of such rules from its internal knowledge representations, known as “Mental Images” (MIs). These rules, grounded in statistical patterns learned during training, are used to selectively inhibit a subset of discriminators prior to classification, effectively acting as a pre-filtering mechanism. The inhibition process is designed to reduce the computational burden and improve classification plausibility by narrowing the focus to the most promising classes. Unlike previous approaches that use MIs solely for interpretation or symbolic conversion, our method exploits this implicit knowledge to enhance the system’s own performance. The extracted rules reflect structural regularities within the input space and serve as a proxy for common sense reasoning embedded within the trained model. This work highlights how internal representations in weightless neural systems can be explicitly leveraged to support interpretable and efficient decision processes, bridging the gap between subsymbolic learning and symbolic reasoning through rule-based inhibition. • A novel extension of the DRASiW weightless neural network is proposed to extract common-sense rules from internal representations (“Mental Images”). • The extracted rules are used to inhibit subsets of discriminators during inference, improving classification performance and reducing computational cost. • Unlike previous approaches, the rules are not only interpretable but directly contribute to performance enhancement. • Extensive experiments on 77 datasets show measurable improvements in F1-score and accuracy across multiple WiSARD variants. • The proposed rule-guided inference bridges sub-symbolic and symbolic reasoning, offering a transparent and efficient hybrid classification model.

Although Hierarchical Decomposition Methods (HDMs) are crucial for simplifying complex multi-class problems, most of them lack a mechanism for optimally structuring the class space based on intrinsic data properties. To overcome this issue, we present the Mental Image Decomposition ( MID ) framework, a new HDM that uses the information content of DRASiW “Mental Images” to create an optimal hierarchical tree aligned with data. We rigorously validated the MID method through extensive experiments on 26 standard datasets. We compared its performance against flat classification approaches and 12 diverse machine learning (ML) classifiers, as well as seven established HDMs. Our comprehensive analysis confirmed that MID is the leading method, with results demonstrating that it secured the global ranking of 1 across all comparative performance and stability metrics ( F1-score and accuracy gain) compared with all competing HDMs. Furthermore, we showed that MID significantly improves ensemble classifiers such as AdaBoost and provides the optimal balance between performance and interpretability when integrated with the r DAB r system (a variant of the DRASiW architecture). This work establishes MID as a highly effective and robust contribution in the field of hierarchical classification. • A novel Hierarchical Decomposition Method (HDM) driven by DRASiW “Mental Image” is proposed: Mental Image Decomposition ( MID ). • MID guides nested dichotomy construction by maximizing inter-group separability and preserving intra-group class cohesion. • Extensive experiments on 26 datasets show MID consistently outperforms 12 classifiers and 7 HDMs.

Hierarchical Decomposition Methods (HDMs) are techniques that handle multi-class classification problems by breaking them down into smaller, more manageable binary classification tasks, typically achieving better accuracy than flat classification approaches.In this work, a new HDM based on the exploitation of DRASiW "Mental Images" to construct the optimal tree model is presented.Through experiments performed on 26 standard datasets, we show how this approach improves the system classification performance with respect to the classical flat classification.

Common sense rules are a form of implicit knowledge acquired through experience and observation of the world around us, and used by both humans and machines to reason and to make decisions about the surrounding environment.Artificial Intelligence systems can extract these rules by mining data and apply them to many predictive tasks.Herein, we first present a new method for extracting rules from DRASiW "Mental Images" (MI) and then how to exploit them to improve the classification performance of the system.The latter is confirmed by the obtained results.

Several improvements have been proposed in the literature for the Weightless Neural Networks (WNNs), in particular the DRASiW extension of the WiSARD model with the introduction of mental imagery and bleaching procedure.We propose a new bleaching procedure called Dynamic Adaptive Bleaching (DAB) and its variant, refined Dynamic Adaptive Bleaching (r DAB), to improve the WNNs performance in terms of computational time and classification capabilities.

Weightless Neural Networks (WNN) showed good results in various classification problems in different domains where a significant number of instances for each class was available.In this work, we present different WiSARD classifiers facing a quite difficult problem from both the clinical and the machine learning point of views: the classification of preclinical markers in Alzheimer's disease continuum patients.The four domain classes show overlapping molecular features and each has few instances (around 40).Together with improved class separation, the confirmation of the goodness of the results is given by a series of experiments that have compared the WiSARD classifiers to many state-of-the-art classifiers, even those ensembles, showing that the obtained results are very close to the top best models. 43

While Weightless Neural Networks (WNN) have been proven effective in Natural Language Processing (NLP) applications, they require the use of highly customized features as they work on binary inputs.However, recent advancements have brought methodologies able to adapt WNN to real numbers showing competitive results on many classification tasks, but they often struggle on sparse data.In this paper, we show that WNN can successfully use sparse linguistic features, like tf-idf, using appropriate transformations.We also show that WNN can be used to improve the performances of existing models for Mixed Language Sentiment Analysis and that it has competitive performances for news categorization. The WiSARD ClassifierThe WiSARD, originally conceived as a pattern recognition device mainly focusing on binary image processing [6], belongs to the class of weightless neu-239

Automatic movement-prothesis control aims to increase the quality of life for patients with diseases causing temporary or permanent paralysis or, in the worst case, the lost of limbs. This technology requires the interaction between the user and the device through a control interface that detects the user's movement intention. Basing on the Motor-Imagery theory, many researchers have explored a wide variety of Classifiers to identify patients' physiological signals from many different sources in order to detect patients' moves intentions. We here propose a novel approach relying on the use of a Weightless Neural Network-based classifier, whose design lends itself to an easy hardware implementation. Additionally, we employ a non-invasive light weight and easy donning EEG-helmet in order to provide a portable controller interface. The developed interface is connected to a robotic hand for controlling open/close actions. We compared the proposed classifier with state of the art classifiers by showing that the proposed method achieves similar performance and contemporaneously represents a viable and practicable solution due to its portability on hardware devices, which will permit its direct implementation on the helmet board.

In many real life scenarios, which span from domestic interactions to industrial manufacturing processes, the objects to be manipulated are non-rigid and deformable, hence, both the location of the object and its deformation have to be tracked. Different methodologies have been applied in literature, using different sensors and techniques for addressing this problem. The main contribution of this paper is to propose a Weightless Neural Network approach for non-rigid deformable object tracking. The proposed approach allows deploying an on-line training on the shape features of the object, to adapt in real-time to changes, and to partially cope with occlusions. Moreover, the use of parallel classifiers trained on the same set of images allows tracking the movements of the objects. In this work, we evaluate the filtering/segmentation performance that is a fundamental step for the correct operation of our approach, in the scenario of pizza making.

People detection and tracking are essential capabilities in human-robot interaction (HRI). Typically, a tracker performance is evaluated by measuring objective data, such as the tracking error. However, in HRI applications, human- tracking performance does not have to be evaluated by considering it as a passive sensing behavior, but as an active sensing process, where both the robot and the human are involved within-the-loop. In this context, we foresee that the robotic non-verbal feedback, such as the head movement, plays an important role in improving the system tracking performance, as well as in reducing the human effort in the interactive tracking process. In order to verify this assumption, we evaluate a tracker performance in a joint task between a human and a robot, modeled as a game, and in three different settings. We adopt common HRI performance measures, such as the robot attention demand or the human effort, to evaluate the HRI human tracking performance scaling up with respect to the used robot feedback channels.

In this paper we propose a new approach to active video surveillance intelligence systems based on the integration of artificial neural networks (ANN) and symbolic Artificial Intelligence (AI). In particular, the neurosymbolic hybrid system here presented is formed by virtual neural sensors (WiSARD--like systems) and BDI agents. The coupling of virtual neural sensors with symbolic reasoning for interpreting their outputs, makes this approach both very light from the computational and hardware point of view and quite robust in performances.

The reproduction of the movements of a ship by automated platforms, without the use of sensors providing exact data related to the numeric variables involved, is a non-trivial matter. The creation of an artificial vision system that can follow the cadence of said ship, in six axes of freedom, is the goal of this research. Considering that a real time response is a requisite in this case, it was decided to adopt a Boolean artificial neural network system that could identify and follow arbitrary interest points that could define, as a group, a model of the movement of an observed vessel. This paper describes the development of a prototype based on the Boolean perceptron model WiSARD (Wilkie, Stonham and Aleksander's Recognition Device), that is being implemented in the C programming language on a desktop computer using a regular webcam as input.

In this paper a neurosymbolic hybrid system for robot self-localization is proposed. This crucial issue in autonomous robotic research field has been tackled with slightly different approaches depending mainly on robot sensors and actuation devices, its available computational resources, and the environment in which it acts. The system we present has been designed for robot endowed with poor sensors (a 16-element sonar array, a camera just performing very light computation, odometric sensors) which acts in an office-like environment. In the proposed landmark based approach, we have considered as "natural" environmental features the corners formed by wall intersections, and corners formed by doors that are present in the scene. The whole problem, from landmark recognition to position estimation, is carried out by a unified neurosymbolic system.

In this paper we propose a new approach to active video surveillance intelligence systems based on the integration of artificial neural networks (ANN) and symbolic Artificial Intelligence (AI). In particular, the neurosymbolic hybrid system here presented is formed by virtual neural sensors (WiSARD-like systems) and BDI agents. The coupling of virtual neural sensors with symbolic reasoning for interpreting their outputs, makes this approach both very light from the computational and hardware point of view and quite robust in performances.

A Neuro-Symbolic Language for monotonic and non-monotonic parallel logical inference by means of artificial neural networks (ANNs) is presented. Both the language and its compiler have been designed and implemented in order to translate the neural representation of a given problem into a VHDL software, which in turn can set devices such as FPGA. The result of this operation leads to an electronic circuit that we call NSP (Neuro-Symbolic Processor).