From intelligent agents to trustworthy human-centred multiagent systems

Soorati, Mohammad Divband; Gerding, Enrico H.; Marchioni, Enrico; Naumov, Pavel; Norman, Timothy J.; Ramchurn, Sarvapali D.; Rastegari, Bahar; Sobey, Adam; Stein, Sebastian; Tarpore, Danesh; Yazdanpanah, Vahid; Zhang, Jie

doi:10.3233/aic-220127

Cited by 3 publications

(1 citation statement)

References 81 publications

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“…In sensor technology, many scholars have also developed flexible accelerometers and piezoresistive pressure sensors. The research on integrated AI technology in China started late and is still in the initial stage of development, but many scholars have explored and applied related fields [3][4]. Some scholars have proposed three methods based on neural network algorithm, particle swarm intelligence computing platform and artificial neural network method to detect the feature parameters contained in robot parts.…”

Section: Introductionmentioning

confidence: 99%

Engineering Machinery Parts Inspection System Based on AI Intelligent Inspection Technology

2022

KME

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The development of intelligent robot technology in today's society has become more and more mature, and has become a new industry. Among them, intelligent tools, including detection systems, are also improving with the level of science and technology. As one of the products of the new era, artificial intelligence is widely used in industrial production, military and other aspects. Based on AI computing, this paper studies and implements the design and verification process of an autonomous product sensor and signal processing module in an automatic deployment environment based on similar rules. At the same time, for the key part of robot technology, namely, the information acquisition unit, a corresponding algorithm is proposed to fuse and analyze its detection data and finally get the results. The test results show that the system can achieve 90% accuracy in detecting parts, and the detection time is fast, which can meet the user requirements.

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Section: Introductionmentioning

confidence: 99%

Engineering Machinery Parts Inspection System Based on AI Intelligent Inspection Technology

2022

KME

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Macro Ethics Principles for Responsible AI Systems: Taxonomy and Directions

Woodgate,

Ajmeri

2024

ACM Comput. Surv.

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Responsible AI must be able to make or support decisions that consider human values and can be justified by human morals. Accommodating values and morals in responsible decision making is supported by adopting a perspective of macro ethics, which views ethics through a holistic lens incorporating social context. Normative ethical principles inferred from philosophy can be used to methodically reason about ethics and make ethical judgements in specific contexts. Operationalising normative ethical principles thus promotes responsible reasoning under the perspective of macro ethics. We survey AI and computer science literature and develop a taxonomy of 21 normative ethical principles which can be operationalised in AI. We describe how each principle has previously been operationalised, highlighting key themes that AI practitioners seeking to implement ethical principles should be aware of. We envision that this taxonomy will facilitate the development of methodologies to incorporate normative ethical principles in reasoning capacities of responsible AI systems.

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Multi-agent systems research in the United Kingdom

Albrecht

Wooldridge

2022

AIC

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Multi-agent systems have been a core research topic in artificial intelligence for several decades. A multiagent system consists of multiple decision-making agents -which may be software-based AI systems, physicallyembodied robots, or humans -which must interact in a shared environment in pursuit of their goals. Multi-agent systems research spans a range of technical problems, such as how to design planning and learning algorithms which enable agents to achieve their goals; how to design multi-agent systems to incentivise certain behaviours in agents; how information is communicated and propagated among agents; and how norms, conventions, and roles may emerge in multi-agent systems. A vast array of applications have been addressed using multi-agent methodologies, including autonomous driving, multi-robot factories, automated trading, commercial games, automated tutoring, and robotic rescue teams.The purpose of this special issue is to showcase current multi-agent systems research led by university and industry groups based in the United Kingdom. Research groups and institutes in the UK which have significant activity in multi-agent systems research were invited to submit an article describing: (1) the technical problems in multi-agent systems tackled by the group (their core research agenda), including applications and industry collaboration; (2) the main approaches developed by the group and any key results achieved; and (3) important open challenges in multi-agent systems research from the perspective of the group.A large number of high-quality submissions were received, of which 14 were included for publication in the special issue. These articles represent a broad set of research topics within the field of multi-agent systems, showcasing the strength of contributions made by UK-based research groups in both universities and industry. We believe the open research problems discussed in each of the articles will provide a rich resource for researchers in this field, both new and old.Research groups from the following organisations are represented in the special issue (ordered alphabetically

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From intelligent agents to trustworthy human-centred multiagent systems

Cited by 3 publications

References 81 publications

Engineering Machinery Parts Inspection System Based on AI Intelligent Inspection Technology

Engineering Machinery Parts Inspection System Based on AI Intelligent Inspection Technology

Macro Ethics Principles for Responsible AI Systems: Taxonomy and Directions

Multi-agent systems research in the United Kingdom

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