Mental Spaces from a Functional Perspective

This paper reviews motivations for introducing fuzzy sets and fuzzy logic to knowledge representation in artificial intelligence. First we consider some areas of successful application of conventional approaches to system analysis. We then discuss limitations of these approaches and the reasons behind these limitations.We introduce different levels of representation for complex systems and discuss issues of granularity and fuzziness in connection with these representation levels. We make a distinction between decomposable and integrated complex systems and discuss the relevance of this distinction for knowledge representation and reasoning. We also distinguish fuzzy relations between quantities of different granularity within one domain from fuzzy relations between two different domains and discuss the need of considering both in artificial intelligence.We distinguish methods for describing natural, artificial, and abstract systems and contrast the modeling of system function with the modeling of system behavior in connection with the representation of fuzziness. The paper briefly discusses recent criticism of the fuzzy system approach and concludes with a prospect on soft computing in AI.

Price, consumption, and production influence global food estimation. Food consumption and food production needs improving affordability, government policy and expert proposition in decision making. Expert have developed a scenario combining direct weather impact on key grain producing regions, indirect impact through crop pathogens, consequences for markets, and stock of global food. Global food production tended to increase, meanwhile consumption under production. In Indonesia, production is under consumption. It is anomaly by global situation and world condition. Government policy is tariff, export, and import. Export food influenced by price food commodity is not stable, meanwhile food import tended to increase because by national food consumption increasing demand. Government policy and expert proposition to modern society, need Expert System is increasing rapidly. In Artificial Intelligence, Expert System is a computer system that emulates the decision making ability of a human expert. There are two reasoning strategies in Expert System: Forward Chaining and Backward Chaining. The aim of this paper is to identify which reasoning strategy system (Forward Chaining). This paper focus on the concept of knowledge representation in Artificial Intelligence and implementing of Forward Chaining on Food Affordability. Food Affordability used to planning of food in the future. Finally, Inference using Forward Chaining on Food Affordability, the result it is good and can be used to planning and policy on national food.

Chapter 1 Knowledge Representation and Classical Logic

This article reviews approaches based on artificial intelligence (AI), which contributes to the security of cyber environments. We examine existing techniques using several indicators: explainability, performance and robustness. These indicators have been chosen based on their importance for user acceptance and interpretability of the approach. Indeed, the AI field is vast and is divided into several sub-domains. The two most well-known sub-domains are symbolic AI (representation of knowledge, rules and operations based on symbols) and numeric AI (calculations and algorithms using numeric information, focusing on the result, not the representation of knowledge). While most approaches investigated come from numeric AI, we conclude on the need for hybrid AI systems, combining the advantages of both AI sub-fields while maximising the protection provided against cyberattacks.

ABSTRACTIt has long been recognized that there is a natural dependence between theories of knowledge representation and theories of knowledge acquisition, with the idea that the right knowledge representation enables acquisition to happen as reliably as it does. Given this, a reasonable criterion for a theory of knowledge representation is that it be useful for acquisition, particularly in nontrivial learning situations. We propose quantitative learnability metrics meant to capture how useful a representation is for acquisition. We then apply these metrics to the case study of English metrical stress, a language that is notorious for having nonproductive aspects in its grammar and so being nontrivial to learn a productive grammar for. We examine three theories of metrical stress representation, assessing their learnability via these metrics from English child-directed speech at different stages of linguistic development. We find that while all three theories are only somewhat useful at the initial stages of stress acquisition, they are far more useful at later stages and define a grammar able to capture the vast majority of English children’s acquisitional intake. Interestingly, we also find that the proposed English grammars in each representational theory are not the grammars most easily learnable from English child-directed speech. Instead, minor changes to each English grammar yield an English-like grammar that is far more learnable, suggesting that these alternative English grammars should be given further theoretical, experimental, and computational consideration. We discuss implications for both theories of representation and theories of acquisition.

Neural tracking of linguistic and acoustic speech representations decreases with advancing age

Artificial intelligence and robots may progressively take a more and more prominent place in our daily environment. Interestingly, in the study of how humans perceive these artificial entities, science has mainly taken an anthropocentric perspective (i.e., how distant from humans are these agents). Considering people’s fears and expectations from robots and artificial intelligence, they tend to be simultaneously afraid and allured to them, much as they would be to the conceptualisations related to the divine entities (e.g., gods). In two experiments, we investigated the proximity of representation between artificial entities (i.e., artificial intelligence and robots), divine entities and natural entities (i.e., humans and other animals) at both an explicit (Study 1) and an implicit level (Study 2). In the first study, participants evaluated these entities explicitly on positive and negative attitudes. Hierarchical clustering analysis showed that participants’ representation of artificial intelligence, robots and divine entities were similar, while the representation of humans tended to be associated with that of animals. In the second study, participants carried out a word/non-word decision task including religious semantic-related words and neutral words after the presentation of a masked prime referring to divine entities, artificial entities and natural entities (or a control prime). Results showed that after divine and artificial entity primes, participants were faster to identify religious words as words compared to neutral words arguing for a semantic activation. We conclude that people make sense of the new entities by relying on already familiar entities and in the case of artificial intelligence and robots, people appear to draw parallels to divine entities.

Urgency of the research of artificial intelligence research in a digital society presents numerous causal changes, among them: space research, biotechnology, nanotechnology, digital technology, etc. The implications for human beings and human civilisation are significant as they affect a number of already existing contemporary issues such as economics, politics, ethics, law, psychology, conflictology, sociology, and ecology. Anthropology has to return to the existence of human beings in a society of artificial intelligence, consciousness and even the physical body. The study conceptualises artificial intelligence as a factor of ecosystem growth in the context of technological, ontological, and anthropological dimensions. The research objectives: 1) to theorise artificial intelligence as a potential and resource for use in all sectors of the socioeconomic sphere in the context of technological dimensions; 2) to clarify the convergence of artificial intelligence and its main components in the context of ontological dimensions; 3) to reveal the role of artificial intelligence in enhancing the digitalisation of society and humans in the context of anthropological dimensions. In the research we are guided by the works of scholars who have studied and are studying the problems of artificial intelligence as a complex social, economic, and cultural phenomenon: R. Andriukaitienė, N. Boström, A. Vance, P. Diamandis, S. Kotler, P. Dickson, L. Kai-Fu, and K. Kell, K. Kell, and others. Research methodology. The theory of artificial intelligence is a complex multidisciplinary science, which is located at the junction of economics, sociology, computer science, mathematics, psychology. Summarizing the processes of interdisciplinary interaction of artificial intelligence, we note that its study requires an interdisciplinary approach based on the synthesis of scientific knowledge, as revealing the content and principles of functioning of a complex invariant system of artificial intelligence. To analyze the modern model of artificial intelligence, it is necessary to apply a systematic institutional-evolutionary approach. By analysing different methodological approaches, it is necessary to acknowledge the presence of an anthropogenic component, as the methodology of scientific research is based on the method of information-anthropogenic analysis based on the fundamental position that no artificial intelligence processes are possible without information/data as an object and human as a subject of cognition. In addition, the following methods were used: induction, deduction, sublimation, synergetics, evolutionary historicism, modelling, and forecasting. The result of the research. Artificial Intelligence (AI) is a broad term used to define technologies/ engineering systems that emulate human intelligence, linking to neurophysiology, robotics, psychology (pattern recognition, modelling of psychological processes), transhumanism, cybernetics (computing power to find patterns in large data sets). Artificial Intelligence has been defined as a software-engineered system that is capable of influencing the environment with varying degrees of autonomy, producing results (predictions, recommendations, decisions) for a specific set of goals for the digitalisation of society. On the other hand, artificial intelligence causes many problems that are seen as cyber/virtual addiction, loss of reality, increased surveillance, loss of protocol control, privacy issues, cybersecurity, etc. The convergence of large data with artificial intelligence has become the single most important development shaping the future of social institutions in the digital society and the prospects for realising potential opportunities. Thanks to the latest digital technologies, advances in software engineering, and evolving artificial intelligence, there are both new perspectives for solving the most complex problems of modern civilization and new challenges in the technological, ontological, and anthropological dimensions.

This article presents a state-of-the-art review of recent advances aimed at transforming traditional Failure Mode and Effects Analysis (FMEA) into a more intelligent, data-driven, and semantically enriched process. As engineered systems grow in complexity, conventional FMEA methods, which are largely manual, document-centric, and expert-dependent, have become increasingly inadequate for addressing the demands of modern systems engineering. We examine how techniques from Artificial Intelligence (AI), including machine learning and natural language processing, can transform FMEA into a more dynamic, data-driven, intelligent, and model-integrated process by automating failure prediction, prioritisation, and knowledge extraction from operational data. In parallel, we explore the role of ontologies in formalising system knowledge, supporting semantic reasoning, improving traceability, and enabling cross-domain interoperability. The review also synthesises emerging hybrid approaches, such as ontology-informed learning and large language model integration, which further enhance explainability and automation. These developments are discussed within the broader context of Model-Based Systems Engineering (MBSE) and function modelling, showing how AI and ontologies can support more adaptive and resilient FMEA workflows. We critically analyse a range of tools, case studies, and integration strategies, while identifying key challenges related to data quality, explainability, standardisation, and interdisciplinary adoption. By leveraging AI, systems engineering, and knowledge representation using ontologies, this review offers a structured roadmap for embedding FMEA within intelligent, knowledge-rich engineering environments.

The thriving contemporary form of artificial intelligence (AI) called machine learning is often represented sensationally in popular media as a semi-mystical technology. Machine learning systems are frequently ascribed anthropomorphic capacities for learning, emoting and reasoning which, it is suggested, might lead to the alleviation of humanity’s woes. One critical reaction to such sensational proclamations has been to focus on the mundane reality of contemporary machine learning as mere inductive prediction based on statistical generalizations, albeit with surprisingly powerful abilities (Pasquinelli 2017). While the deflationist reaction is a necessary reply to sensationalist agitation, adequate comprehension of modern AI cannot be achieved while neglecting its material and social context. One does not have to subscribe wholeheartedly to the social construction of technology thesis1 to allow that the development and evolution of technologies are influenced by social factors. For AI, the most important aspect of the current social context is arguably capital, which increasingly dominates AI research and production. One former computer science professor describes a “giant sucking sound of [AI] academics going into industry” (Metz 2017). This paper introduces capital’s theory of AI as utility and initiates a discussion on its social consequences. First, I discuss utilities and their infrastructures and introduce a few critical thoughts on the topic. Second, I situate modern AI by way of a brief history. Third, I detail capital’s view of AI as a utility and the technical details underpinning it. Fourth, I sketch how AI as a utility frames a social problematic beyond the important issues of algorithmic bias and the automation of work. I do so by extrapolating from one consequence of AI as a utility which multiple capitalist firms predict: the curation of human subjectivities.

Previous research on the representation of artificial intelligence (AI) in science fiction (SF) film has largely focused on the Global North. Little is known about how filmmakers from the Souths envision and portray AI and how their creative processes and outputs relate to specific conditions of film production, socio-political realities, and global power imbalances, as well as personal experiences and attitudes toward AI. This paper aims to address this gap by presenting the findings of an exploratory interview study with Latin American filmmakers who have dealt with AI in their films. The qualitative text analysis of 10 in-depth, semi-structured interviews with 11 participants from 6 countries reveals that their cinematic visions of AI differ significantly from those prevalent in Western SF: The Latin American filmmakers predominantly associate AI with social control, surveillance, labor exploitation, data colonialism, and the abuse of power by (authoritarian) governments or powerful tech corporations—issues that decolonial scholars have emphasized in their critiques of AI. However, the participants’ visions are not primarily inspired by these academic debates but by their lived experiences. The filmmakers’ strong engagement with everyday life and socio-political conditions, rather than distant or unattainable technological scenarios, can, thus, be considered a distinctive feature of the Latin American approach. This is further reflected in their use of particular esthetic concepts and strategies, such as retrofuturism, which enable them to navigate budgetary constraints and other structural inequalities that limit their visibility and their potential to actively shape technological visions and futures through film.

This study aims to develop AI․SW liberal arts subjects for university learners based on the analysis of learners' needs for artificial intelligence and software education. To analyze learners' educational needs, a survey was conducted on learners at A University. Based on the analysis, the AI․SW liberal arts subjects was designed to combine learning theory of artificial intelligence and Python practice. The theory of artificial intelligence focuses on the development process and service cases to increase the interest of learners. It helps learners explore ways to use artificial intelligence in their field. And Python practice is to create results by modifying the source code to reduce the burden of beginners. In the long term, it helps learners integrate artificial intelligence into their majors. This study presents basic research for a liberal arts curriculum that creates talents who can converge artificial intelligence in each academic field.

The research focus was motivated by the limited understanding of cognitive technologies and the growing gap between artificial intelligence (AI) and human intelligence. The research is a literature review, and its purpose is to simplify the meaning and processes behind cognitive technologies, notably, the fundamentals of machine learning (ML) and computer vision with the intention to briefly address the alleged threat of AI taking over the job market. The research is a review of peer-reviewed articles retrieved from comparative studies, systematic reviews, meta-analysis, service research, reports, conference proceedings, experimental studies, literature reviews, scientometric analyses, books, and multi-case studies, dating from the years of 2018 to 2024. This literature review defines machine learning (ML), artificial intelligence (AI), computer vision, and convolutional neural networks (CNNs). It also compares machine learning to traditional programming and reveals the types of learning in ML models’ training. ML and its correlation with AI are also discussed and details about theory of mind, self-aware AI, reactive machines, and limited memory AI are shared. The literature expounds computer vision, particularly convolutional neural network (CNN) and CNN layers. Recent cutting-edge applications of artificial intelligence including generative AI models and autonomous systems are also incorporated. Finally, the literature briefly addresses the alleged threat of AI taking over the job market. The findings of this literature review reveal that AI is becoming the new way of operating. The conclusion shows that AI models require significant computation to allow computers to learn autonomously. Thus, understanding mathematical models of data and perfecting the process of writing software could be the key to remaining employable as more jobs are expected to be shifted due to AI and tasks automation. Keywords: Cognitive technology, artificial intelligence, machine learning, computer vision, convolutional neural networks

The study is highly relevant due to by the rapid development of artificial intelligence and its growing presence in culture, literature, and audiovisual practices. Contemporary fiction not only reflects technological transformations but also shapes public perceptions of the possibilities and risks associated with artificial intelligence. The aim of the article is to provide a comprehensive examination of artificial intelligence representations in contemporary literature and related digital practices, as well as to identify its key narrative models, ethical issues, and cultural strategies. The main findings reveal several tendencies characteristic of contemporary English language prose: an emphasis on the boundary between the human and the machine, the rethinking of the agency of artificial beings, the modelling of future social systems, and a critical reflection on individual dependence on technological environments. Comparative analysis demonstrates that the works of K. Ishiguro, I. McEwan, M. Wells, D. Eggers, and N. T. Stephenson propose diverse scenarios of human and artificial intelligence coexistence – from humanistic interpretations to more pessimistic models of control and loss of autonomy. The inclusion of Ukrainian research in the fields of cultural studies and media art further shows that the national academic discourse actively integrates the issues related to artificial intelligence into the broader context of postmodern and digital culture. The conclusion highlights that artificial intelligence representations serve as an essential mechanism for understanding the technocultural challenges of the present and contribute to forming a critical vision of future relationships between humans and technologies. The study outlines prospects for further interdisciplinary research in digital humanities and literary studies.

Clues to the way behaviour is integrated and controlled in the human mind have emerged from cognitive psychology and neuroscience. The picture that is emerging mirrors solutions (driven primarily by engineering concerns) to similar problems in the rather different domains of mobile robotics and intelligent agents in artificial intelligence (AI). This chapter looks in detail at the relationship between a psychological theory of willed and automatic control of behaviour, the Norman and Shallice framework, and three types of engineering-based theory in AI. As well as being a promising basis for a large-scale model of cognition, the Norman and Shallice framework presents an interesting example both of apparent theoretical convergence between AI and empirical psychology, and of the way in which theoretical work in both fields can benefit from interaction between them.