Automating Automated Reasoning

We introduce a generalized notion of inference system to support more flexible interpretations of recursive definitions. Besides axioms and inference rules with the usual meaning, we allow also coaxioms, which are, intuitively, axioms which can only be applied "at infinite depth" in a proof tree. Coaxioms allow us to interpret recursive definitions as fixed points which are not necessarily the least, nor the greatest one, whose existence is guaranteed by a smooth extension of classical results. This notion nicely subsumes standard inference systems and their inductive and coinductive interpretation, thus allowing formal reasoning in cases where the inductive and coinductive interpretation do not provide the intended meaning, but are rather mixed together.Comment: This is a corrected version of the paper (arXiv:1808.02943v4) published originally on 12 March 2019

This study focuses on the Modus Tollens (MT) property induced by discrete uninorms. Specifically, we identify the set of necessary and sufficient criteria for a discrete implication function to comply with this logical property. This rule of inference is studied by using discrete residual implication functions derived from uninorms of two of the most important families of these discrete operators (Umin, idempotents), exploring which properties these operators must satisfy, as well as providing some characterizations of the Modus Tollens in this domain of definition. Our findings contribute to a deeper understanding of reasoning mechanisms in fuzzy logic, particularly in discrete settings.

We present the disconnection tableau calculus, which is a free-variable clausal tableau calculus where variables are treatedin a nonrigidmanner. The calculus essentially consists of a single inference rule, the so-called linking rule, which strongly restricts the possible clauses in a tableau. The method can also be viewed as an integration of the linking rule as used in Plaisted’s linking approach into a tableau format. The calculus has the proof-theoretic advantage that, in the case of a satisfiable formula, one can characterise a model of the formula, a property which most of the free-variable tableau calculi lack. In the paper, we present a rigorous completeness proof and give a procedure for extracting a model from a finitely failed branch.

Completely automatic generation of tests from formal executable test models of industrial size still looks like a “holy grail”, in spite of significant progress in model-based testing research and tool development. Realizing this, we follow a more down-to-earth approach by assuming that, even if a test model is available, the test expert manually derives powerful test fragments and what remains to be automated is chaining them into an optimal test. Focusing on this task, we develop a test optimization framework using an FSM extended with input variables and clocks, which reflects important features of Simulink/Stateflow statecharts. The test optimization is expressed as the Asymmetric Travelling Salesman Problem (ATSP). We show how this approach can be used for solving some testing problems specific to automotive controllers. We describe a proof-of-concept prototype, implementing the proposed approach, which we tested on a case study of a particular controller available along with some tests. Experiments with the prototype indicate that the approach scales well for hundreds of tests.

Background Simulated Consultation Assessment (SCA) preparation is critical for GP trainees, with the traditional approach relying on trained medical actors. However, limited actor availability, scheduling constraints and high costs often restrict practice opportunities. This evaluation aimed to assess a conversational artificial intelligent (AI) system as an educational tool for GP consultation skills training, examining its acceptability, perceived educational impact and implementation considerations for training. Methods We conducted an educational innovation evaluation with GP trainees and educators (n = 22) following implementation of a conversational AI simulation system for SCA preparation. The evaluation employed a mixed-methods design incorporating validated educational assessment measures across four domains: clinical authenticity, educational utility, user experience and technical performance. Participants’ perspectives were captured through structured questionnaires and open-ended feedback. Implementation considerations were assessed through cost-comparison analysis with traditional training methods. Results This educational innovation demonstrated strong acceptability among users, with particularly positive evaluations for clinical content authenticity (median 4.5, IQR 4–5) and educational value (median 4.5, IQR 4–5). Qualitative feedback revealed that the innovation successfully addressed key training needs, particularly around accessibility and practice frequency. More experienced educators (11+ years) rated the innovation significantly higher for educational utility (p < 0.05), suggesting potential for curriculum integration. The implementation analysis revealed resource advantages, with 24–84% cost reductions compared to traditional methods. Conclusion This conversational AI innovation offers GP educators a practical, cost-effective solution to training challenges, potentially addressing the modality mismatch between face-to-face training and video-based examinations. Implementation should focus on integration as a complementary curriculum tool, with ongoing evaluation of educational outcomes.

graphical sentential logic, the system of alpha graphs, is much more than an historical curiosity. Although a large alpha graph is difficult to manipulate hand, the alpha graphs are admirably suited to automation. In particular, a simple decision procedure for the alpha graphs is tailor-made for the programming language LISP. This decision procedure is described in section I below and is implemented by a LISP program CSProve in section II. Section III gives examples of CSProve as it ran on a desktop computer and indicates how the program is easily adapted to parallel processing. The decision procedure differs from tableau methods and the widely used Robinson resolution method in at least two interesting ways: unlike resolution it does not require that a formula be put into a normal form before the method can be applied, and unlike both tableau methods and resolution it does not require any branching tree constructions. If the method were formulated as a proof procedure in the usual notation it would have only four onepremise inference rules of the form:

By a semi-formal system we understand a proof system which includes infinitary inference rules. The use of inference rules with infinitely many premises was already suggested by David Hilbert in his paper “Die Grundlegung der elementaren Zahlentheorie” [6] and was later systematically used by Kurt Schutte in his work on proof theory. The heigths of proof trees in a semi-formal system are canonically measured by ordinals. Therefore, in contrast to Gentzen’s original approach, ordinals enter proof theoretic research via semi-formal systems in a completely canonical way.

BackgroundGiven the magnitude and speed of SARS-CoV-2 transmission, achieving timely and effective manual contact tracing has been a challenging task. Early in the pandemic, contact tracing apps generated substantial enthusiasm due to their potential for automating tracing and reducing transmission rates while enabling targeted confinement strategies. However, although surveys demonstrate public interest in using such apps, their actual uptake remains limited. Their social acceptability is challenged by issues around privacy, fairness, and effectiveness, among other concerns.ObjectiveThis study aims to examine the extent to which design and implementation considerations for contact tracing apps are detailed in the available literature, focusing on aspects related to participatory and responsible eHealth innovation, and synthesize recommendations that support the development of successful COVID-19 contact tracing apps and related eHealth technologies.MethodsSearches were performed on five databases, and articles were selected based on eligibility criteria. Papers pertaining to the design, implementation, or acceptability of contact tracing apps were included. Articles published since 2019, written in English or French, and for which the full articles were available were considered eligible for analysis. To assess the scope of the knowledge found in the current literature, we used three complementary frameworks: (1) the Holistic Framework to Improve the Uptake and Impact of eHealth Technologies, (2) the Montreal model, and (3) the Responsible Innovation in Health Assessment Tool.ResultsA total of 63 articles qualified for the final analysis. Less than half of the selected articles cited the need for a participatory process (n=25, 40%), which nonetheless was the most frequently referenced item of the Framework to Improve the Uptake and Impact of eHealth Technologies. Regarding the Montreal model, stakeholder consultation was the most frequently described level of engagement in the development of contact tracing apps (n=24, 38%), while collaboration and partnership were cited the least (n=2, 3%). As for the Responsible Innovation in Health framework, all the articles (n=63, 100%) addressed population health, whereas only 2% (n=1) covered environmental considerations.ConclusionsMost studies lacked fundamental aspects of eHealth development and implementation. Our results demonstrate that stakeholders of COVID-19 contact tracing apps lack important information to be able to critically appraise this eHealth innovation. This may have contributed to the modest uptake of contact tracing apps worldwide. We make evidence-informed recommendations regarding data management, communication, stakeholder engagement, user experience, and implementation strategies for the successful and responsible development of contact tracing apps.

Edge computing represents a transformative paradigm shift in computational architecture, fundamentally altering how location-based services operate by processing data closer to its source rather than relying solely on distant cloud infrastructure. This comprehensive article explores the architectural principles, implementation considerations, and strategic advantages of edge computing specifically within the context of location-aware applications. By minimizing the physical and network distance data must travel, edge computing significantly enhances responsiveness, reliability, and efficiency—critical factors for applications where spatial context directly influences functionality and user experience. The article examines diverse use cases across urban mobility, emergency services, retail environments, autonomous vehicles, and healthcare, where edge-enabled location intelligence has demonstrated substantial improvements in operational capabilities. Technical challenges, including data synchronization, resource constraints, security considerations, and networking complexities, are explored alongside implementation strategies. Rather than positioning edge and cloud as competing models, the article emphasizes their complementary nature, advocating for hybrid architectures that strategically distribute computational workloads based on specific application requirements to maximize system performance while addressing inherent limitations.

Despite their potential in analytical requirements elicitation, goal-oriented approaches in Data warehouse (DW) projects have not been well exploited. The main difficulties in their adoption is the lack of assistance in goal elicitation and the lack of support in generating suitable information for decision-making, from the defined goals. To address these limitations, in this paper we introduce a decision-making ontology that formalizes the semantic relationships among the decision makers’ goals and explicitly relates the decision-making knowledge to the goals. In addition, this ontology is enriched with a set of queries and inference rules that provide for an automatic generation of suitable analytical requirements from the goals fulfilling any business process that the decision maker cares to analyze.

Automatic question generation aims to generate questions from a text passage where the generated questions can be answered by certain sub-spans of the given passage. Traditional methods mainly use rigid heuristic rules to transform a sentence into related questions. In this work, we propose to apply the neural encoder-decoder model to generate meaningful and diverse questions from natural language sentences. The encoder reads the input text and the answer position, to produce an answer-aware input representation, which is fed to the decoder to generate an answer focused question. We conduct a preliminary study on neural question generation from text with the SQuAD dataset, and the experiment results show that our method can produce fluent and diverse questions.

We describe ongoing work on building an environment to support reasoning in proof assistants that represent formal systems using higher-order abstract syntax (HOAS). We use a simple and general specification language whose syntax supports HOAS. Using this language, we can encode the syntax and inference rules of a variety of formal systems, such as programming languages and logics. We describe our tool, implemented in OCaml, which parses this syntax, and translates it to a Coq library that includes definitions and hints for aiding automated proof in the Hybrid system. Hybrid itself is implemented in Coq, and designed specifically to reason about such formal systems. Given an input specification, the library that is automatically generated by our tool imports the general Hybrid library and adds definitions and hints for aiding automated proof in Hybrid about the specific programming language or logic defined in the specification. This work is part of a larger project to compare reasoning in systems supporting HOAS. Our current work focuses on Hybrid, Abella, Twelf, and Beluga, and the specification language is designed to be general enough to allow the automatic generation of libraries for all of these systems from a single specification.

Automatic Derivation of the Irrationality of e

Federated Identity Management (FIM) emerges as a critical solution for organizations navigating the complexities of modern digital environments, where identity management across disparate systems presents significant security challenges. By establishing trust relationships between identity providers and service providers, FIM enables seamless authentication across organizational boundaries while maintaining robust security controls. This comprehensive framework eliminates redundant authentication processes, reduces credential proliferation, and addresses the fragmentation issues inherent in multi-cloud environments. The architecture encompasses identity providers, service providers, trust frameworks, and claims mechanisms working in concert through standardized protocols such as OAuth 2.0, OpenID Connect, SAML, and WS-Federation. FIM delivers transformative benefits including enhanced user experience through Single Sign-On capabilities, strengthened security posture via centralized authentication, and substantial operational efficiencies. While implementation considerations such as just-in-time provisioning, attribute mapping, session management, and trust chain security present notable challenges, various architectural patterns including hub-and-spoke, mesh federation, and broker models offer flexible deployment options to match organizational requirements. As digital transformation accelerates, emerging trends such as decentralized identity, continuous authentication, and Zero Trust integration are reshaping the federation landscape.

Event-Driven Architecture (EDA) has emerged as a powerful paradigm for building real-time, scalable, and highly responsive systems.By decoupling components and enabling asynchronous communication through events, EDA offers significant advantages over traditional architectures.This paper provides a comprehensive overview of EDA, exploring its core concepts, components, and benefits. It delves into practical applications across various industries, showcasing real-world examples of how EDA is transforming businesses and improving user experiences. The paper also discusses implementation considerations, potential challenges, and future trends in EDA, offering valuable insights for architects and developers looking to leverage this innovative approach.