Architectural aspects of self-aware and self-expressive computing systems: from psychology to engineering

Work on human self-Awareness is the basis for a framework to develop computational systems that can adaptively manage complex dynamic tradeoffs at runtime. An architectural case study in cloud computing illustrates the framework's potential benefits

Rebranding Originality for the Age of AI

Originality has been a longstanding focal point within the college classroom, with students being encouraged to embrace creativity and boldness. The traditional view of originality, relying solely on one\u27s wit and imagination, has lost its effectiveness in the present era. The concept of learning has undergone a significant transformation, no longer resembling the isolated ivory tower of the past where individuals would immerse themselves in books, hoping to be inspired. Instead, modern learning has become more social and collaborative. Students compare and contrast class material with online resources, engaging in conversations, both in person and virtually, to solidify their understanding. The author of the presentation contends that the future of higher education lies in collaborative originality. Collaboration goes beyond the mere sharing of ideas; it serves as a means of generating innovative concepts, thriving in the dissolution of traditional boundaries. The delineation between humans and machines, disciplines, and formal and informal learning has become increasingly blurred. In this context, modern originality emerges as a collaborative and interdisciplinary process. The advent of AI, notably exemplified by technologies like ChatGPT and Hyperwrite, has further accelerated this trend. Utilizing prompt engineering, individuals can seamlessly collaborate with virtual assistants to foster new ideas, revolutionizing the conventional notion of originality

Self-aware computing systems:from psychology to engineering

At the current time, there are several fundamental changes in the way computing systems are being developed, deployed and used. They are becoming increasingly large, heterogeneous, uncertain, dynamic and decentralised. These complexities lead to behaviours during run time that are difficult to understand or predict. One vision for how to rise to this challenge is to endow computing systems with increased self-awareness, in order to enable advanced autonomous adaptive behaviour. A desire for self-awareness has arisen in a variety of areas of computer science and engineering over the last two decades, and more recently a more fundamental understanding of what self-awareness concepts might mean for the design and operation of computing systems has been developed. This draws on self-awareness theories from psychology and other related fields, and has led to a number of contributions in terms of definitions, architectures, algorithms and case studies. This paper introduces some of the main aspects of self-awareness from psychology, that have been used in developing associated notions in computing. It then describes how these concepts have been translated to the computing domain, and provides examples of how their explicit consideration can lead to systems better able to manage trade-offs between conflicting goals at run time in the context of a complex environment, while reducing the need for a priori domain modelling at design or deployment time

Engineering self-organizing urban superorganisms

Progresses in ubiquitous, embedded, and social networking and computing make possible for people in urban areas to dynamically interact with each other and with ICT devices around. This can result in a system with a very large number of agents working together in an orchestrated and self-organizing way to achieve specific urban-level goals, i.e., as if they were a “superorganism”. In this paper, we sketch the future vision of urban superorganisms and overview some emerging application areas heading towards the vision. Following, we identify the key challenges in engineering self-organizing multi-agent systems that can work as a superorganism, i.e., seamlessly involving ICT agents and human agents so to achieve some required urban level goals. Finally, we introduce the reference architecture for an infrastructure to support our future vision of self-organizing urban superorganisms

Artificial situational awareness assessment of a novel ATC support system

This paper presents the application of an existing situational awareness framework to a newly developed artificial intelligence system to determine its awareness level. The system incorporates diverse automation techniques - knowledge graph, expert rules, machine learning - for gaining situational awareness and applying it in the field of air traffic control. Since the system was developed to serve as a foundation for exploring automation and artificial situational awareness, the primary result of this work is the system’s overall awareness level assessment and the identification of sub-systems that may be improved for additional awareness. The framework used was chosen in the fundamental project documents and its use proved beneficial as it enabled the demonstration of how general guidelines can be interpreted for a specific system. It also informed possible routes for improvement of the process. Highest priority awareness-related improvements are those dealing with robustness, whose implementation would substantiate the current awareness assessment. The system is shown to be on the highest awareness level conditionally, considering its proof-of-concept level. The high level reached by the system is contingent on awareness concept and condition interpretations. With the appropriate assessment of the system, implementation in an operational environment is more feasible

Learning to Learn in Collective Adaptive Systems: Mining Design Patterns for Data-driven Reasoning

Engineering collective adaptive systems (CAS) with learning capabilities is a challenging task due to their multidimensional and complex design space. Data-driven approaches for CAS design could introduce new insights enabling system engineers to manage the CAS complexity more cost-effectively at the design-phase. This paper introduces a systematic approach to reason about design choices and patterns of learning-based CAS. Using data from a systematic literature review, reasoning is performed with a novel application of data-driven methodologies such as clustering, multiple correspondence analysis and decision trees. The reasoning based on past experience as well as supporting novel and innovative design choices are demonstrated

Reflective Artificial Intelligence

As Artificial Intelligence (AI) technology advances, we increasingly delegate mental tasks to machines. However, today's AI systems usually do these tasks with an unusual imbalance of insight and understanding: new, deeper insights are present, yet many important qualities that a human mind would have previously brought to the activity are utterly absent. Therefore, it is crucial to ask which features of minds have we replicated, which are missing, and if that matters. One core feature that humans bring to tasks, when dealing with the ambiguity, emergent knowledge, and social context presented by the world, is reflection. Yet this capability is completely missing from current mainstream AI. In this paper we ask what reflective AI might look like. Then, drawing on notions of reflection in complex systems, cognitive science, and agents, we sketch an architecture for reflective AI agents, and highlight ways forward

Una nueva taxonomía para las relaciones internacionales : repensando el sistema internacional como un sistema adaptativo complejo

The international system is a complex adaptive system with emergent properties and dynamics of self-organization and information processing. As such, it is better understood with a multidisciplinary approach that borrows methodologies from the field of complexity science and integrates them to the theoretical perspectives offered by the field of international relations (IR). This study is set to formalize a complex systems theory approach to the study of international affairs and introduce a new taxonomy for IR with the two-pronged aim of improving interoperability between different epistemological communities and outlining a formal grammar that set the basis for modeling international politics as a complex adaptive system.El Sistema internacional es un sistema adaptativo complejo con propiedades emergentes y dinámica de auto organización y procesamiento de información. Como tal, es mejor comprender con una aproximación multidisciplinaria que extraiga metodologías del campo de la ciencia de la complejidad y las integre a las perspectivas teóricas ofrecidas por el campo de las relaciones internacionales (RI). Este estudio existe para formalizar un acercamiento teórico de sistemas complejos para el estudio de los asuntos internacionales y presentar una nueva taxonomía para las RI con el objetivo bipartito de mejorar la interoperabilidad entre diferentes comunidades epistemológicas y esquematizar una gramática formal que ponga las bases para los modelos de las políticas internacionales como un sistema adaptativo complejo.国际系统是一个复杂适应系统，它具备自组织和信息处理的新兴性质和动态。照此，用多学科方法更能促进对国际系统的理解，因为前者借用复杂性科学领域中的方法论并将其合并到国际关系（international relations，IR）领域所提供的理论视角中。本研究致力为国际关系研究提出正式的复杂系统理论方法，并为国际关系引入一种新的分类法，此分类法有两个目标：一是提高不同认识论社区（epistemological communities）间的互操作性；二是概述一种形式语法，为国际政治作为一种复杂适应系统进行建模提供基础。中国語タイトル : 国际关系新分类 : 重新思考国际关系这一复杂适应系

A New Taxonomy for International Relations: Rethinking the International System as a Complex Adaptive System

Abstract: The international system is a complex adaptive system with emergent properties and dynamics of self-organization and information processing. As such, it is better understood with a multidisciplinary approach that borrows methodologies from the field of complexity science and integrates them to the theoretical perspectives offered by the field of international relations (IR). This study is set to formalize a complex systems theory approach to the study of international affairs and introduce a new taxonomy for IR with the two-pronged aim of improving interoperability between different epistemological communities and outlining a formal grammar that set the basis for modeling international politics as a complex adaptive system