Responsible Autonomy

As intelligent systems are increasingly making decisions that directly affect society, perhaps the most important upcoming research direction in AI is to rethink the ethical implications of their actions. Means are needed to integrate moral, societal and legal values with technological developments in AI, both during the design process as well as part of the deliberation algorithms employed by these systems. In this paper, we describe leading ethics theories and propose alternative ways to ensure ethical behavior by artificial systems. Given that ethics are dependent on the socio-cultural context and are often only implicit in deliberation processes, methodologies are needed to elicit the values held by designers and stakeholders, and to make these explicit leading to better understanding and trust on artificial autonomous systems.Comment: IJCAI2017 (International Joint Conference on Artificial Intelligence

Current and Future Challenges in Knowledge Representation and Reasoning

Knowledge Representation and Reasoning is a central, longstanding, and active area of Artificial Intelligence. Over the years it has evolved significantly; more recently it has been challenged and complemented by research in areas such as machine learning and reasoning under uncertainty. In July 2022 a Dagstuhl Perspectives workshop was held on Knowledge Representation and Reasoning. The goal of the workshop was to describe the state of the art in the field, including its relation with other areas, its shortcomings and strengths, together with recommendations for future progress. We developed this manifesto based on the presentations, panels, working groups, and discussions that took place at the Dagstuhl Workshop. It is a declaration of our views on Knowledge Representation: its origins, goals, milestones, and current foci; its relation to other disciplines, especially to Artificial Intelligence; and on its challenges, along with key priorities for the next decade

Using Multi-agent Systems to Pursue Autonomy with Automated Components

AbstractHumans have used tools to transform raw resources into valued outputs ever since society harnessed fire. The type of tool, amount of effort and form of energy required depends on the output or object being created. As tools evolved into machines, they enhanced operator productivity. Hence, industry continues to invest heavily in machines to assist people to do more with less physical control and/or interaction. This involves automating functions previously completed manually. Taylorism and the Hawthorn experiments all contributed to optimising industrial outputs and value engineers continue to promote a mecha- nized workforce in order to minimise business variations in human performance and their behaviour. Researchers have also pursued this goal using Computational Intelligence (CI) techniques. This process of transforming cognitive functionality into machine actionable form has encompassed many careers. Machine Intelligence (MI) is becoming more aspirational, with Artificial Intelligence (AI) enabling the achievement of numerous goals. More recently, Multi-Agent Systems (MASs) have been employed to provide a flexible framework for research and development. These frameworks facilitate the development of component interoperability, with coordination and cooperation techniques needed to solve real-world problems. However problems typically manifest in complex, dynamic and often hostile environments. Based on the effort to seek or facilitate human-like decision making within machines, it is clear that further research is required. This paper discusses one possible avenue. It involves future research, aimed at achieving a cognitive sub-system for use on-board platforms. The framework is introduced by describing the human-machine relationship, followed by the theoretic background into cognitive architectures and a conceptual mechanism that could be used to implement a virtual mind. One which could be used to improve automation, achieve greater independence and enable more autonomous behaviour within control systems

Foresight Africa: Top Priorities for the Continent 2020-2030

The new year 2020 marks the beginning of a promising decade for Africa. Through at least the first half of the decade, economic growth across Africa will continue to outperform that of other regions, with the continent continuing to be home to seven of the world's 10 fastest-growing economies. Collective action among African and global policymakers to improve the livelihoods of all under the blueprint of the Sustainable Development Goals and the African Union's Agenda 2063 is representative of the shared energy and excitement around Africa's potential. With business environments improving, regional integration centered around the African Continental Free Trade Agreement progressing, and the transformational technologies of Fourth Industrial Revolution spreading, never before has the region been better primed for trade, investment, and mutually beneficial partnerships. The recent, unprecedented interest of an increasingly diversified group of external partners for engagement with Africa highlights this potential. Despite the continent's promise, though, obstacles to success linger, as job creation still has not caught up with the growing youth labor force, gaps in good and inclusive governance remain, and climate change as well as state fragility threaten to reverse the hard-fought-for gains of recent decades.This special edition of Foresight Africa highlights the triumphs of past years as well as strategies from our experts to tackle forthcoming, but surmountable, obstacles to a prosperous continent by 2030

Workshop on multisensor integration in manufacturing automation

Journal ArticleMany people helped make the Workshop a success, but special thanks must be given to Howard Moraff for his support, and to Vicky Jackson for her efforts in making things run smoothly. Finally, thanks to Jake Aggarwal for helping to start the ball rolling

Developmental Bootstrapping of AIs

Although some current AIs surpass human abilities in closed artificial worlds such as board games, their abilities in the real world are limited. They make strange mistakes and do not notice them. They cannot be instructed easily, fail to use common sense, and lack curiosity. They do not make good collaborators. Mainstream approaches for creating AIs are the traditional manually-constructed symbolic AI approach and generative and deep learning AI approaches including large language models (LLMs). These systems are not well suited for creating robust and trustworthy AIs. Although it is outside of the mainstream, the developmental bootstrapping approach has more potential. In developmental bootstrapping, AIs develop competences like human children do. They start with innate competences. They interact with the environment and learn from their interactions. They incrementally extend their innate competences with self-developed competences. They interact and learn from people and establish perceptual, cognitive, and common grounding. They acquire the competences they need through bootstrapping. However, developmental robotics has not yet produced AIs with robust adult-level competences. Projects have typically stopped at the Toddler Barrier corresponding to human infant development at about two years of age, before their speech is fluent. They also do not bridge the Reading Barrier, to skillfully and skeptically draw on the socially developed information resources that power current LLMs. The next competences in human cognitive development involve intrinsic motivation, imitation learning, imagination, coordination, and communication. This position paper lays out the logic, prospects, gaps, and challenges for extending the practice of developmental bootstrapping to acquire further competences and create robust, resilient, and human-compatible AIs.Comment: 102 pages, 29 figure

An Introduction to Ethics in Robotics and AI

This open access book introduces the reader to the foundations of AI and ethics. It discusses issues of trust, responsibility, liability, privacy and risk. It focuses on the interaction between people and the AI systems and Robotics they use. Designed to be accessible for a broad audience, reading this book does not require prerequisite technical, legal or philosophical expertise. Throughout, the authors use examples to illustrate the issues at hand and conclude the book with a discussion on the application areas of AI and Robotics, in particular autonomous vehicles, automatic weapon systems and biased algorithms. A list of questions and further readings is also included for students willing to explore the topic further

The law in the age of artificial intelligence and robotics: a case study of Atlas from a Tort Law perspective

The law in the age of artificial intelligence and robotics faces many challenges to which our current legal systems may not yet have the proper tools to tackle them with. In recent years, we have seen the development of technologies which just ten or twenty years ago we wouldn’t have thought were even yet possible. From advanced AI-powered language models such as ChatGPT to driverless autonomous vehicles such as Tesla’s, it is undeniable that the science of intelligent machines advances at a pace that far outmatches that of its legal counterpart. The present study will seek to analyse both current and future regulatory proposals regarding the use of autonomous humanoid robots from a Tort Law perspective, both through the Spanish and European regulatory frameworks, in order to establish efficient legal solutions that offer satisfactory outcomes both for manufacturers and consumers.El Derecho en la era de la inteligencia artificial y la robótica enfrenta muchos desafíos para los cuales nuestros sistemas legales actuales tal vez aún no tengan las herramientas adecuadas para abordarlos. En los últimos años hemos visto el desarrollo de tecnologías que hace apenas diez o veinte años ni siquiera hubiéramos pensado que fueran posibles. Desde modelos de lenguaje avanzados impulsados por IA, como ChatGPT, hasta vehículos autónomos sin conductor como el de Tesla, es innegable que la ciencia de las máquinas inteligentes avanza a un ritmo que supera con creces el de su contraparte legal. El presente estudio buscará analizar las propuestas regulatorias actuales y futuras sobre el uso de robots humanoides autónomos desde una perspectiva del Derecho de Daños, tanto a través del marco regulatorio español como europeo, con el fin de establecer soluciones jurídicas eficientes que ofrezcan resultados satisfactorios tanto para los fabricantes como para los consumidores. consumidores.La llei a l'era de la intel·ligència artificial i la robòtica s'enfronta a molts reptes als quals els nostres sistemes legals actuals potser encara no disposen de les eines adequades per afrontar-los. En els darrers anys hem assistit al desenvolupament de tecnologies que fa només deu o vint anys no ens pensàvem que encara fossin possibles. Des de models de llenguatge avançats amb intel·ligència artificial com ChatGPT fins a vehicles autònoms sense conductor com el de Tesla, és innegable que la ciència de les màquines intel·ligents avança a un ritme que supera amb escreix el del seu homòleg legal. El present estudi pretén analitzar les propostes normatives actuals i futures sobre l'ús de robots humanoides autònoms des de la perspectiva de la Llei de danys, tant a través del marc normatiu espanyol com europeu, per tal d'establir solucions legals eficients que ofereixin resultats satisfactoris tant per als fabricants com per als consumidors

A practical guide to multi-objective reinforcement learning and planning

Real-world sequential decision-making tasks are generally complex, requiring trade-offs between multiple, often conflicting, objectives. Despite this, the majority of research in reinforcement learning and decision-theoretic planning either assumes only a single objective, or that multiple objectives can be adequately handled via a simple linear combination. Such approaches may oversimplify the underlying problem and hence produce suboptimal results. This paper serves as a guide to the application of multi-objective methods to difficult problems, and is aimed at researchers who are already familiar with single-objective reinforcement learning and planning methods who wish to adopt a multi-objective perspective on their research, as well as practitioners who encounter multi-objective decision problems in practice. It identifies the factors that may influence the nature of the desired solution, and illustrates by example how these influence the design of multi-objective decision-making systems for complex problems. © 2022, The Author(s)

Artificial Intelligence and Ambient Intelligence

This book includes a series of scientific papers published in the Special Issue on Artificial Intelligence and Ambient Intelligence at the journal Electronics MDPI. The book starts with an opinion paper on “Relations between Electronics, Artificial Intelligence and Information Society through Information Society Rules”, presenting relations between information society, electronics and artificial intelligence mainly through twenty-four IS laws. After that, the book continues with a series of technical papers that present applications of Artificial Intelligence and Ambient Intelligence in a variety of fields including affective computing, privacy and security in smart environments, and robotics. More specifically, the first part presents usage of Artificial Intelligence (AI) methods in combination with wearable devices (e.g., smartphones and wristbands) for recognizing human psychological states (e.g., emotions and cognitive load). The second part presents usage of AI methods in combination with laser sensors or Wi-Fi signals for improving security in smart buildings by identifying and counting the number of visitors. The last part presents usage of AI methods in robotics for improving robots’ ability for object gripping manipulation and perception. The language of the book is rather technical, thus the intended audience are scientists and researchers who have at least some basic knowledge in computer science