Proceedings of the ISSEK94 Workshop on Mathematical and Statistical Methods in Artificial Intelligence

In this paper, we investigate what kind of modal logics can be constructed into the Gentzen-type formulation which enjoys the Cut elimination theorem. We treat normal, regular and monotonic modal logics. A normal modal logic is the modal logic with the Rule of Necessitation and the axiom K:□(A⊃B)⊃(□A⊃□B), a regular modal logic with the rule of inference RR :A,Г→B□A,□Г→□B and a monotonic modal logic with the rule of inference RM :A→B□A→□B. We provide Gentzen-type formulation and prove the cut elimination theorem for the following modal logics: RT4 (a regular modal logic with the axioms T:□A⊃A and 4:□A⊃□□A), MT4 (a monotonic modal logic with the axioms T and 4), MNT4 (a monotonic modal logic with the Rule of Necessitation and the axioms T and 4), RT (a regular modal logic with the axiom T), MT (a monotonic modal logic with the axiom T), MNT (a monotonic modal logic with the Rule of Necessitation and the axiom T), KL (a normal modal logic with the axiom L:□A⊃(A∨□B)) and RTA (a regular modal logic with axioms T and A:□A&B⊃□B).

Mathematical theories of uncertainty in which measures of uncertainty are now well established are fuzzy set theory, evidence theory, and possibility theory, in addition to classical set theory and probability theory. Some of these theories are connected. Classical set theory is subsumed under fuzzy set theory. Probability theory and possibility theory are branches of evidence theory, while evidence theory is, in turn, a branch of fuzzy measure theory. Although information aspects of fuzzy measure theory have not been investigated as yet, the theory is briefly introduced in this chapter because it represents a broad framework for future research.

In this study, we leverage the fusion of edge computing, artificial intelligence (AI) methods, and facilities provided by B5G to build a heterogeneous set of AI techniques for COVID-19 outbreak prediction. Advancement in the areas of AI, edge computing, the Internet of Things (IoT), and fast communication networks provided by beyond 5G (B5G) networks has opened doors for new possibilities by fusing these technologies and techniques. In a pandemic outbreak, such as COVID-19, the need for rapid analysis, decision making, and prediction of future trends becomes paramount. On a global map, the distributed processing and analysis of data at the source is now possible and much more efficient. With the features provided by B5G, such as low latency, larger area coverage, higher data rate, and realtime communication, building new intelligent and efficient frameworks is becoming easier. In this study, our aim is to achieve higher accuracy in prediction by fusing multiple AI methods and leveraging the B5G communication architecture. We propose a distributed architecture for training AI methods on edge devices, with the results of edge-trained models then propagated to a central cloud AI method, which then combines all the received edge-trained models into a global and final prediction model. The experimental results of five countries (United States, India, Italy, Bangladesh, and Saudi Arabia) show that the proposed distributed AI on edges can predict COVID-19 outbreak better than that of each individual AI method in terms of correlation coefficient scores.

<p>Artificial intelligence (AI) methods, especially machine learning and natural language processing, are increasingly affecting health professions education (HPE), including the medical school application and selection processes, assessment, and scholarship production. The rise of large language models over the past 18 months, such as ChatGPT, has raised questions about how best to incorporate these methods into HPE. The lack of training in AI among most HPE faculty and scholars poses an important challenge in facilitating such discussions. In this commentary, the authors provide a primer on the AI methods most often used in the practice and scholarship of HPE, discuss the most pressing challenges and opportunities these tools afford, and underscore that these methods should be understood as part of the larger set of statistical tools available.Despite their ability to process huge amounts of data and their high performance completing some tasks, AI methods are only as good as the data on which they are trained. Of particular importance is that these models can perpetuate the biases that are present in those training datasets, and they can be applied in a biased manner by human users. A minimum set of expectations for the application of AI methods in HPE practice and scholarship is discussed in this commentary, including the interpretability of the models developed and the transparency needed into the use and characteristics of such methods.The rise of AI methods is affecting multiple aspects of HPE including raising questions about how best to incorporate these models into HPE practice and scholarship. In this commentary, we provide a primer on the AI methods most often used in HPE and discuss the most pressing challenges and opportunities these tools afford.</p> <p><br></p> <p><br></p>

1 Modal logic: a semantic perspective

Artificial intelligence-based suicide prevention and prediction: A systematic review (2019–2023)

Conjunction of Evidence and Fuzzy Logic

Knowledge formalization and reasoning automation are central within Artificial Intelligence. Classical logic has been traditionally used for such purposes. However, it is better suited to deal with complete knowledge in ideal circumstances. In real situations, in which the knowledge is partial, classical logic is not sufficient since it is monotonic. No monotonic logics have been proposed to better cope with practical reasoning. A successful formalization of no monotonic reasoning is Reiter's default logic which extends classical logic with default rules. Unfortunately, default logic is undividable. One reason for that is the use of classical logic as its monotonic basis. In this work, we change the default logic monotonic basis and propose a new default logic based on the description logic ALC. This new default logic is decidable and useful to formalize practical reasoning on hierarchical ontology's with exceptions, specially the ones that deals with legal knowledge and reasoning. On the default counterpart, we add some restrictions to the application of defaults in order to obtain nice properties such as coherence and elimination of anomalous extensions. We present the main algorithms used to build the extensions for this logic with its complexity analysis.

Artificial General Intelligence is the idea that someday an hypothetical agent will arise from artificial intelligence (AI) progresses, and will surpass by far the brightest and most gifted human minds. This idea has been around since the early development of AI. Since then, scenarios on how such AI may behave towards humans have been the subject of many fictional and research works. This paper analyzes the current state of artificial intelligence progresses, and how the current AI race with the ever faster release of impressive new AI methods (that can deceive humans, outperform them at tasks we thought impossible to tackle by AI a mere decade ago, and that disrupt the job market) have raised concerns that Artificial General Intelligence (AGI) might be coming faster that we thought. In particular, we focus on 3 specific families of modern AIs to develop the idea that deep neural networks, which are the current backbone of nearly all artificial intelligence methods, are poor candidates for any AGI to arise due to their many limitations, and therefore that any threat coming from the recent AI race does not lie in AGI but in the limitations, uses, and lack of regulations of our current models and algorithms. This article appears in the AI & Society track.

Full classical S5 in natural deduction with weak normalization

Application of artificial intelligence to forecast hydrocarbon production from shales

Biometric authentication has long been regarded as a foundational element of identity verification, leveraging unique physiological and behavioral traits to enhance security beyond traditional passwords. While it offers notable advantages such as convenience and resistance to identity theft, concerns are mounting regarding privacy, susceptibility to spoofing, and the irreversibility of compromised biometric identifiers. These weaknesses are becoming increasingly critical as digital infrastructures evolve into distributed, dynamic environments in which static trust models are no longer sufficient. Moreover, several traditional modalities- such as fingerprints, iris scans, and voice recognition- have already been breached. However, Artificial Intelligence (AI) methods are reshaping this landscape by introducing adaptive and context‑aware features into biometric systems. Machine Learning (ML) techniques enhance accuracy, enable continuous authentication, and support multimodal fusion, while anomaly‑detection mechanisms improve resilience against sophisticated attacks. Generative AI (GenAI) plays a particularly significant role, though it introduces a paradox: it empowers defenders through realistic attack simulations and robustness testing, yet simultaneously equips attackers with tools for producing deepfakes and synthetic identities, thereby expanding the attack surface. In this evolving security landscape, Zero‑Trust Architectures (ZTA) have gained prominence as a model that replaces assumptions of inherent trust with continuous verification mechanisms. The use of biometric data within ZTA can enhance the reliability of identity verification; however, it also intensifies several existing issues. Biometric identifiers must be handled and stored in ways that safeguard individual privacy and align with relevant legal requirements, and the incorporation of AI‑based assessment methods introduces additional concerns regarding potential bias, transparency, and oversight. Moreover, combining AI‑supported biometric systems with Zero‑Trust principles raises further questions about scalability, system compatibility, and the broader ethical consequences of more pervasive identity monitoring. This work therefore examines the convergence of biometrics, AI, and Zero‑Trust principles from a critical perspective. It highlights the dual role of AI as both a source of innovation and a generator of new threats, while identifying opportunities for adaptive security, real‑time threat detection, and improved user experience. By analyzing technical and operational dimensions, the work proposes a roadmap for integrating biometrics into ZTA that balances innovation with accountability and supports trustworthy, resilient cybersecurity frameworks.

BackgroundThrombolysis and mechanical thrombectomy represent the most successful stroke innovations over the last 30 years. Quantifying innovation in stroke is essential for identifying productive research lines and prioritizing funding, but health care lacks validated methods for measuring innovation.ObjectiveThis study aimed to systematically evaluate the relationship between stroke-related patents and publications, demonstrate the feasibility of using large language models (LLMs) in this process, and identify the most rapidly advancing innovations in stroke care by mapping them to a theoretical innovation life cycle.MethodsThe Open Patent Services (European Patent Office) and PubMed databases were searched between 1993 and 2023 for “stroke OR cerebrovascular.” In this bibliometric patent-publication analysis, a 13 billion–parameter Llama LLM was trained to identify patents related to stroke disease, as opposed to other references to the word “stroke,” on a manually labeled subset of 5000 patents and assessed using 5-fold cross-validation. The LLM filtered irrelevant results, and the resulting patent codes were grouped into innovation clusters. For each cluster, annual patent and publication counts were normalized to adjust for global trends. Cluster-specific growth curves were plotted to analyze the rates and characteristics of growth. The innovation life cycle stage for each innovation cluster was estimated by fitting a sigmoid curve to the patent and publication data consistent with the diffusion of innovations theory by Rogers.ResultsThe cross-validated accuracy of the LLM was 99.2%, with a sensitivity of 96.5% and a specificity of 99.6%. An initial bibliometric search retrieved 237,035 patents and 486,664 research publications. A manual review of a random sample of patents before filtering revealed that only 11.2% (56/500) were relevant to stroke. After LLM filtering, of the 237,035 patents, 28,225 (11.9%) stroke-related patents remained. These were grouped into 7 innovation clusters: pharmacological treatment, alternative medicine, rehabilitation devices, medical imaging, diagnostic testing, surgical devices, and artificial intelligence (AI) methods. Patent and publication counts were strongly correlated across clusters (Spearman rs=0.65-0.92; P<.006) except for pharmacological treatment (rs=0.09) and alternative medicine (rs=0.55). Pharmacological treatments were the top-performing cluster over the last 30 years, accounting for 49.3% (36,005/73,094) of all patents, but patent activity in this area has plateaued since the late 2000s. AI methods, rehabilitation devices, and medical imaging exhibited exponential rates of patent growth, with annual normalized increases of 39.2%, 15.9%, and 5.8% compared to 16.9%, 5.3%, and 2.2% for publications, respectively.ConclusionsApplying an LLM to publicly available patent and publication data provides a scalable way to quantify innovation in stroke. Pharmacological treatment appears to have entered a saturation phase, whereas AI methods, rehabilitation devices, and medical imaging remain in rapid growth, highlighting areas of greatest traction for future research and investment.

This paper investigates the simultaneous use of in-situ hydrologic measurements in combination with two different artificial intelligent (AI) methods, namely, Adaptive Neuro Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN), for developing enhanced long-term streamflow forecasting models. To enhance the reliability of the proposed models’ outputs, a sub-basin method using the regionalization approach is proposed. Furthermore, to accelerate the training process and achieve more accurate handling of seasonal changes, a parameter representing seasonal variations is introduced. The models are applied to the mountainous Talezang basin, southwestern Iran, for which there is a 14-year series of monthly in-situ data records and snow cover area data obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS). The results indicate that the use of the sub-basin approach significantly improves both AI methods’ performances. Moreover, it is deduced that the use of seasonal information and satellite data has a great impact on the model performance and accuracy. Comparing the long-term flow forecasts of both models showed that the ANFIS model is superior to the ANN.

In this article, we provide an evidence-based primer of current tools and evolvingconcepts in the area of intraprocedural artificial intelligence (AI) methods incolonoscopy and laparoscopy as a ‘procedure companion’, with specific focus on colorectalcancer recognition and characterisation. These interventions are both likely beneficiariesfrom an impending rapid phase in technical and technological evolution. The domains whereAI is most likely to impact are explored as well as the methodological pitfalls pertainingto AI methods. Such issues include the need for large volumes of data to train AI systems,questions surrounding false positive rates, explainability and interpretability as well asrecent concerns surrounding instabilities in current deep learning (DL) models. The areaof biophysics-inspired models, a potential remedy to some of these pitfalls, is exploredas it could allow our understanding of the fundamental physiological differences betweentissue types to be exploited in real time with the help of computer-assistedinterpretation. Right now, such models can include data collected from dynamicfluorescence imaging in surgery to characterise lesions by their biology reducing thenumber of cases needed to build a reliable and interpretable classification system.Furthermore, instead of focussing on image-by-image analysis, such systems could analysein a continuous fashion, more akin to how we view procedures in real life and makedecisions in a manner more comparable to human decision-making. Synergistical approachescan ensure AI methods usefully embed within practice thus safeguarding against collapse ofthis exciting field of investigation as another ‘boom and bust’ cycle of AI endeavour.