Symbolic Artificial Intelligence Research Articles

THE INNOVATIVE IMPACT OF GENERATIVE ARTIFICIAL INTELLIGENCE ON DIGITAL BUSINESS TRANSFORMATION

In the context of intensifying competition and evolving market dynamics, the deployment of cutting-edge technologies has become not merely a discretionary choice, but an indispensable imperative for any enterprise aspiring to achieve successful growth. Generative artificial intelligence, with its substantial potential for automation, personalisation and optimisation of business processes, is emerging as a highly promising avenue of digital transformation. This study is dedicated to investigating approaches and delineating strategies for aligning generative artificial intelligence with the requirements of digital business transformation. The research examines the development of artificial intelligence, with a focus on symbolic artificial intelligence, machine learning, deep learning and generative artificial intelligence. In addition, it considers the impact of these developments on business processes. The article identifies the potential benefits and challenges associated with the adaptation of generative artificial intelligence to the needs of modern business, in the areas of marketing, sales and data analysis. The utilisation of diverse methodologies and techniques, including prompts, fine-tuning, and the incorporation of interactive guidance systems, can enhance the efficacy and precision of generative AI in a business setting, thereby facilitating optimal outcomes in a multitude of tasks. The authors put forth the proposition of employing generative artificial intelligence technology in conjunction with Retrieval-Augmented Generation, with the objective of enhancing the quality and relevance of responses to user queries. Additionally, they advocate for the utilisation of agents or orchestration tools to provide guidance to models. The successful implementation of generative artificial intelligence hinges on three key factors: the clear definition of objectives, the selection of suitable tools and technologies, and the assurance of managerial and staff support. The implementation of generative artificial intelligence will contribute to increased efficiency through the automation of routine tasks, enhanced competitiveness through personalisation and innovation, optimised cost structures that increase profitability, and expanded opportunities for research and development.

Does symbolic AI-related disclosure influence CEO promotion? Evidence from state-owned enterprises in China

ABSTRACT This paper considers firms’ artificial intelligence (AI)-related discretionary disclosure to examine whether symbolic communications of AI influences a CEO’s chance of career advancement in Chinese state-owned enterprises (SOEs). We find a positive relationship between symbolic AI disclosure and the possibility of managers getting promoted. Our main finding is more salient when a firm’s achievement of policy goals is more crucial, its financial performance is relatively poor, and a manager has more urgent promotion needs. Results imply that CEOs engage in impression management via managerial discretionary disclosure to strengthen organizational legitimacy and build a favourable impression, which may increase their probability of getting promoted.

Harnessing Fuzzy Cognitive Maps for Advancing AI with Hybrid Interpretability and Learning Solutions

Advancements that merge the clarity of symbolic AI with the adaptive learning traits of subsymbolic AI show great potential at the intersection of these two AI forms. This study introduces Fuzzy Cognitive Maps (FCMs). This hybrid model integrates the optimal characteristics of both frameworks to address the challenges of interpretability and explainability in artificial intelligence (AI) systems. FCMs provide a robust framework for logically and intuitively supporting decision-making processes and representing causal relationships. Their capacity to handle the inherent vagueness and uncertainty of real-world scenarios enables a more natural and flexible approach to problem-solving. Due to their intrinsic adaptability and learning capabilities derived from sub-symbolic AI, FCMs are particularly suited for applications demanding high levels of interpretability and explainability.

Machine Learning upon RDF Knowledge Graphs for Drug Safety: A Case Study on Reactome Data.

Artificial Intelligence (AI), particularly Machine Learning (ML), has gained attention for its potential in various domains. However, approaches integrating symbolic AI with ML on Knowledge Graphs have not gained significant focus yet. We argue that exploiting RDF/OWL semantics while conducting ML could provide useful insights. We present a use case using signaling pathways from the Reactome database to explore drug safety. Promising outcomes suggest the need for further investigation and collaboration with domain experts.

A Critical Review of Inductive Logic Programming Techniques for Explainable AI.

Despite recent advances in modern machine learning algorithms, the opaqueness of their underlying mechanisms continues to be an obstacle in adoption. To instill confidence and trust in artificial intelligence (AI) systems, explainable AI (XAI) has emerged as a response to improve modern machine learning algorithms' explainability. Inductive logic programming (ILP), a subfield of symbolic AI, plays a promising role in generating interpretable explanations because of its intuitive logic-driven framework. ILP effectively leverages abductive reasoning to generate explainable first-order clausal theories from examples and background knowledge. However, several challenges in developing methods inspired by ILP need to be addressed for their successful application in practice. For example, the existing ILP systems often have a vast solution space, and the induced solutions are very sensitive to noises and disturbances. This survey paper summarizes the recent advances in ILP and a discussion of statistical relational learning (SRL) and neural-symbolic algorithms, which offer synergistic views to ILP. Following a critical review of the recent advances, we delineate observed challenges and highlight potential avenues of further ILP-motivated research toward developing self-explanatory AI systems.

Impact of artificial intelligence (AI) decision support on clinical trial participation: A before-after implementation study on a nationwide molecular tumor board.

1557 Background: Molecular Tumor Boards (MTB) have become the standard for deriving consensus therapy (Rx) recommendations (rec) for patients (pts) with advanced cancer undergoing comprehensive genomic profiling (CGP). However, finding matched Rx and trial options remains laborious, needing informatics strategies. Methods: We examined the outcomes before and after implementation of a decision support system (DSS) at the MTB of the Australian Molecular Screening & Therapeutics program (MoST, ACTRN12616000908437), enrolling pts with refractory solid tumors and linking CGP results to Rx and trials through rec on clinical reports. The DSS comprises a variant interpretation pipeline, a symbolic AI system integrating the TOPOGRAPH database and an annotated trials registry, and a web platform used by the MTB for producing reports shortlisting matching Rx and therapeutic trials. The primary endpoint is the cumulative participation rate (CPR) in genomic matched trials (excluding MoST-related trials) in pts who received Rx after CGP. The secondary endpoints are the CPR in any trial, and overall survival (OS) from MTB. The Fine-Gray model was used to estimate the subdistribution hazard ratios (sHR) for between-group differences in participation. Kaplan-Meier and Cox regression were used for OS analysis. The time-to-event analyses were adjusted for age, ECOG, cancer type, lines of prior rx, and lead-time bias (OS only). Results: In 2203 of 5186 pts (42.5%) received ≥ 1 Rx after CGP, 971 pts were reviewed at MTB before and 1272 after DSS implementation in Sep 2020. At 3, 6 and 12 months (m) after enrolment, CPR in matched trials were 3.2%, 5.0%, and 6.8% before implementation and 3.6%, 5.8%, and 8.9% afterwards (sHR 1.19, 95% CI 0.90–1.57, p=0.23). Pts who received an MTB rec were more likely to participate in a matched trial (sHR 4.75, 3.13–7.23, p<0.001). Notably, DSS specifically increased the likelihood of trial participation following MTB rec (interaction sHR 2.69, 1.02–7.07, p=0.045), with 12m CPR increasing from 10.0% to 12.2% after implementation of the DSS in pts with a MTB rec, whilst for pts without a MTB rec, the CPR fell from 3.1% to 1.6% after implementation. No association was seen between participation in any trials and MTB recs (sHR 1.12, 0.95–1.32) or implementation (sHR 0.95, 0.81–1.11; CPR pre 21.6% v post 23.7% at 12m). No OS difference was seen comparing before with after implementation of the DSS (median 16.7 v 15.3m, adjusted HR 0.97, 0.87–1.08). However, pts receiving genomic matched Rx had a longer OS (median 20.1 v 15.0m unmatched, HR 0.76, p=0.002). Conclusions: The observed interaction between the implementation of DSS and MTB rec suggests there is effect modification in participation rates of pts in genomic matched trials, warranting exploration into how AI might enhance trial participation among pts with incurable cancers undergoing CGP.

Enhancing the quality of banking technology platforms through a hybrid AI testing approach

In the dynamic landscape of the banking sector, marked by increased operational complexity and regulatory scrutiny, the pursuit of innovation demands a strategic orientation. While offering advantages in cost, quality and speed, this strategic approach to innovation should mandate a cautious consideration of the risks inherent in the integration of emerging technologies. With the continuous advancement of artificial intelligence (AI), particularly generative AI (GenAI), as a method for emerging banking technologies, its wider adoption in the financial industry demands careful consideration of associated risk implications. This paper advocates for a software testing approach that delves into the complexities of banking technology platforms by leveraging generative algorithms to attain extensive test coverage and simultaneously employs rule-based analytics to refine the generated datasets, optimising coverage for faster execution and efficient resource utilisation. Such an approach is in line with a risk-averse innovation strategy, as it balances out the smart creativity of novel GenAI methods with a rule-based discriminative potential of more established symbolic AI methods. This paper serves as a position paper, contributing to the discourse on the integration of innovative testing methodologies in the banking domain.

What Can Computers Do Now? Dreyfus Revisited for the Third Wave of Artificial Intelligence

In recent years, artificial intelligence (AI) has seen significant advances that have in fact exceeded even optimistic prognoses. Using data-driven AI, namely deep learning techniques, it has been demonstrated that computers may now be equipped with abilities of remarkable scope and quality, such as solving image and text processing tasks at human level. Large language models, in particular, have sparked debates regarding opportunities and challenges of this rapidly developing area. Will remaining fundamental challenges of data-driven AI, such as factual or logical mistakes, be overcome for good if complemented and hybridized with symbolic AI techniques, such as knowledge representation and reasoning? Will systems of artificial general intelligence (AGI) emerge from this, possessing common sense and in fact completing the decades-old quest for AI that motivated the raise of the field in the 1950s? In the light of these questions, we review the likewise, decades-old philosophical debate about capabilities and limitations of computers from a hybrid AI point of view. Here, we discuss how hybrid AI is coming closer to disproving Hubert Dreyfus’ famous statements regarding what computers can not do. At the same time, we shed light on a lesser discussed challenge for hybrid AI: the possibility that its developers might be its biggest limiters.

Semantic Web Practices: Infrastructural Politics and the Future of the Web

Semantic Web Practices: Infrastructural Politics and the Future of the Web

Hybrid Intelligence Systems Combining Human Expertise and AI/RPA for Complex Problem Solving

Hybrid Intelligence Systems (HIS) represent a paradigm shift in problem-solving methodologies by integrating human expertise with Artificial Intelligence (AI) and Robotic Process Automation (RPA). This paper explores the mechanisms, applications, benefits, challenges, and future directions of HIS in the context of complex problem-solving. Through collaborative synergies between human cognition and machine intelligence, HIS enhances decision-making accuracy, efficiency, and innovation. Human experts contribute domain knowledge, contextual understanding, and ethical reasoning, while AI algorithms and RPA systems offer data-driven insights, computational power, and process automation capabilities. HIS fosters inclusivity, diversity, and democratization in problem-solving processes by harnessing the collective intelligence of diverse teams and stimulating interdisciplinary collaboration. However, challenges such as privacy concerns, data security risks, and algorithmic biases must be addressed to realize the full potential of HIS. Looking ahead, the integration of Explainable AI (XAI), Edge AI, and Neuro symbolic AI holds promise for enhancing transparency, interpretability, and robustness in HIS architectures. Human-centered design principles and interdisciplinary research collaborations will shape the development and deployment of HIS, ensuring alignment with human values, preferences, and needs. Ultimately, HIS will continue to serve as a beacon of collaboration, creativity, and collective intelligence in shaping a better world for generations to come.

Architectures of modern intelligent systems: synergy of cybernetics and symbolic artificial intelligence, tools and technologies for the developmentof intelligent systems

Architectures of modern intelligent systems: synergy of cybernetics and symbolic artificial intelligence, tools and technologies for the developmentof intelligent systems

A scoping review of literature on deep learning and symbolic AI-based framework for detecting Covid-19 using computerized tomography scans

This scoping review aims to explore various Deep Learning and Symbolic Artificial Intelligence (AI) models that can be integrated into explainable hybrid AI for the purpose of detecting COVID-19 based on Computerized Tomography (CT) scans. We followed the PRISMA-ScR framework as the foundation for our scoping review protocol. Our approach included a thorough search across 13 databases, complemented by an additional random internet search for relevant articles. Due to the voluminous number of articles returned, the search was further narrowed using the keywords: Deep Learning, Symbolic AI and Hybrid AI. These keywords were used because they are more visible in the earmarked literature. A screening of all articles by title was performed to remove duplicates. The final screening process centered on the publication year, ensuring that all considered articles fell within the range of 2019 to 2023, inclusive. Subsequently, abstract or text synthesis was conducted. Our search query retrieved a total of 3,312 potential articles from the thirteen databases, and an additional 12 articles from a random internet search, resulting in a cumulative count of 3,324 identified articles. After the deduplication and screening steps, 260 articles met our inclusion criteria. These articles were categorized based on the year of publication, the type of aim, and the type of AI used. An analysis of the year of publication revealed a linear trend, indicating growth in the field of Hybrid AI. Out of the five aim categories identified, we deliberately excluded articles that lacked a specified aim. It's noteworthy that 3% of the articles focused on the integration of AI models. The low percentage value suggests that the integration aspect is overlooked, thereby transcripting the integration of Deep Learning and symbolic AI into hybrid AI as an area worth exploring. This scoping review gives an overview of how a Deep Learning and Symbolic AI-based framework has been used in the detection of COVID-19 based on CT scans.

Symbolic Artificial Intelligence to Diagnose Tuberculosis Using Ontology.

Pulmonary Tuberculosis (PTB) is an infectious disease caused by a bacterium called Mycobacterium tuberculosis. This paper aims to create Symbolic Artificial Intelligence (SAI) system to diagnose PTB using clinical and paraclinical data. Usually, the automatic PTB diagnosis is based on either microbiological tests or lung X-rays. It is challenging to identify PTB accurately due to similarities with other diseases in the lungs. X-ray alone is not sufficient to diagnose PTB. Therefore, it is crucial to implement a system that can diagnose based on all paraclinical data. Thus, we propose in this paper a new PTB ontology that stores all paraclinical tests and clinical symptoms. Our SAI system includes domain ontology and a knowledge base with performance indicators and proposes a solution to diagnose current and future PTB also abnormal patients. Our approach is based on a real database of more than four years from our collaborators at Pondicherry hospital in India.

A journey from AI to Gen-AI

The history of artificial intelligence (AI), from its conception to the creation of general AI (Gen-AI), is a fascinating story of human inventiveness, technical growth, and philosophical research. This article examines the historical milestones, major inventions, and transformational concepts that have influenced AI's trajectory. Beginning with early symbolic AI and rule-based systems, it investigates the shift to machine learning, highlighting discoveries in neural networks and deep learning that transformed disciplines such as computer vision and natural language processing. The introduction of generative models, such as GANs and VAEs, resulted in a considerable increase in AI capabilities, paving the path for Gen-AI. Unlike narrow AI, Gen-AI strives to imitate human-like intelligence and adaptability across a wide range of jobs, bringing serious ethical and philosophical concerns. This essay also looks at the current state of Gen-AI, its problems, and possible applications in healthcare, education, finance, and other areas. It finishes by picturing a future in which human-machine collaboration and ethical AI development are prioritized, highlighting the need of continual learning and responsible innovation.

Advancements in software engineering using AI

The integration of Artificial Intelligence (AI) into the space of software engineering marks a transformative period that reshapes traditional development processes and propels the industry into a new era of innovation. This exploration delves into the multifaceted impact of AI, from its roots in early symbolic AI to the contemporary dominance of machine learning and deep learning. AI’s applications span various domains, but its significance in software engineering lies in its ability to enhance efficiency, improve software quality, and introduce novel approaches to problem-solving. From automating routine tasks to streamlining complex development workflows, AI acts as a virtual collaborator, allowing human developers to focus on higher-order thinking and creativity. This study introduces the application of AI in software engineering. reverse-engineering, and development environments. Moreover, ethical considerations, challenges, and future trends, including explainable AI, reinforcement learning, and human-AI collaboration, are presented.

Challenging the Boundary between Self-Awareness and Self-Consciousness in AI from the Perspectives of Philosophy

With the progression of technology, scientists are dealing with the challenges brought about by the creation of smart AI models designed for actual use. This paper aimed to explore the complex boundary between consciousness and self-awareness in artificial intelligence (AI) systems from a philosophical perspective. Methods - The literature review conducted as part of this study was based on materials obtained from such databases as WOS, Web of Science, Scopus, and Google Scholar. The findings of the study indicated that, in the present day, philosophy is analysing how technological advancements have led to a focus on conceptual frameworks that define consciousness as the capacity of artificial intelligence to identify its functional states. Self-awareness is also being viewed as the ability to reflect and recognise oneself within a larger context. Drawing on the principles of symbolic AI and connectionism, the study considers whether AI can have a form of self-awareness similar to biological entities and how this relates to philosophical debates about consciousness. The study delved into the symbolic representation of knowledge and logical reasoning as tools for AI to achieve a primary form of self-awareness. This is contrasted with a connectionist approach that models the emergent properties of neural networks, suggesting a pathway to more complex forms of self-awareness. The article discussed the limitations and potential of these approaches, including the ethical implications of creating systems that can mimic aspects of human self-awareness and self-concept. By thoroughly examining the current AI technologies and their underlying philosophical principles, the article aims to give a detailed look at the ongoing debate on the cognitive abilities of AI and whether it can move beyond basic self-awareness to a more profound level of self-awareness. The conclusion is that although AI may exhibit behaviours indicative of self-awareness, the subjective experience of self-awareness remains a uniquely human trait, which calls into question the boundary between human and machine cognition.

An Analysis of Multi Agent Systems Agent Based Programming

The effectiveness of agent-based modeling as a simulation modeling methodology has resulted in its application in diverse settings, including the resolution of pragmatic business challenges, in recent times. The domain of symbolic artificial intelligence, which investigates intelligent and self-governing entities, is preoccupied with the mechanisms by which these entities arrive at determinations regarding their conduct in reaction to, or in expectation of, stimuli from the external environment. The scope of the methods employed encompasses a diverse array of techniques, spanning from negotiations to agent simulations, as well as multi-agent argumentation and planning. The present article scrutinizes the utilization of agent-based computing in multi-agent systems and provides an all-encompassing analysis of the relevant literature. This study delves into the examination of both traditional and contemporary agent programming languages, including their respective extensions, comparative analyses, and instances of their application in published literature.

ReactionDataExtractor 2.0: A Deep Learning Approach for Data Extraction from Chemical Reaction Schemes.

Knowledge in the chemical domain is often disseminated graphically via chemical reaction schemes. The task of describing chemical transformations is greatly simplified by introducing reaction schemes that are composed of chemical diagrams and symbols. While intuitively understood by any chemist, like most graphical representations, such drawings are not easily understood by machines; this poses a challenge in the context of data extraction. Currently available tools are limited in their scope of extraction and require manual preprocessing, thus slowing down the speed of data extraction. We present a new tool, ReactionDataExtractor v2.0, which uses a combination of neural networks and symbolic artificial intelligence to effectively remove this barrier. We have evaluated our tool on a test set composed of reaction schemes that were taken from open-source journal articles and realized F1 score metrics between 75 and 96%. These evaluation metrics can be further improved by tuning our object-detection models to a specific chemical subdomain thanks to a data-driven approach that we have adopted with synthetically generated data. The system architecture of our tool is modular, which allows it to balance speed and accuracy to afford an autonomous, high-throughput solution for image-based chemical data extraction.

Morpho-logic from a topos perspective – application to symbolic AI

Modal logics have proved useful for many reasoning tasks in symbolic artificial intelligence (AI), such as belief revision, spatial reasoning, among others. On the other hand, mathematical morphology (MM) is a theory for non-linear analysis of structures, that was widely developed and applied in image analysis. Its mathematical bases rely on algebra, complete lattices, topology. Strong links have been established between MM and mathematical logics, mostly modal logics. In this paper, we propose to further develop and generalize this link between mathematical morphology and modal logic from a topos perspective, i.e. categorial structures generalizing space, and connecting logics, sets and topology. Furthermore, we rely on the internal language and logic of a topos. We define structuring elements, dilations and erosions as morphisms. Then we introduce the notion of structuring neighborhoods, and show that the dilations and erosions based on them lead to a constructive modal logic, for which a sound and complete proof system is proposed. We then show that the modal logic thus defined (called morpho-logic here), is well adapted to define concrete and efficient operators for revision, merging, and abduction of new knowledge, or even spatial reasoning.

SECURE II: Unlocking the Potential of Artificial Intelligence for Entrepreneurial Success

This study investigates the potential of artificial intelligence (AI) techniques to aid novice entrepreneurs in evaluating their business models prior to launching new ventures. The proposed SECURE II framework integrates symbolic AI, neural AI and ensemble modeling to strengthen ex-ante assessment of business model designs and expected performance outcomes. Machine learning experiments demonstrate that AI modeling uncovers hidden patterns and relationships between pre-launch plans and post-launch results. This enables more informed entrepreneurial decision-making by providing data-driven evaluative feedback. Rather than relying solely on intuition, the interactive AI assessments offer entrepreneurs a simulation mechanism to systematically test assumptions and refine opportunities pre-launch. The ensemble approach combining complementary AI techniques outperforms individual models, underscoring the value of synthesized hybrid intelligence tailored to the entrepreneurial context. By compensating for limitations in human information processing, pattern recognition, and biases, the SECURE II framework augments entrepreneurial cognition during business model formulation. This study elucidates the mechanisms through which AI can expand mental capabilities for opportunity analysis and new venture creation. The proposed toolkit demonstrates strong predictiveness on real-world data, validating the utility of AI to minimize uncertainties and boost startup success.