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Consider ethical and social challenges in smart grid research

Valentin Robu, David Flynn, Merlinda Andoni, Maizura Mokhtar
2019 Nature Machine Intelligence  
Artificial Intelligence and Machine Learning are increasingly seen as key technologies for building more decentralised and resilient energy grids, but researchers must consider the ethical and social implications  ...  Fair algorithmic decision-making Overall, AI in energy systems is being used to take increasingly complex decisions.  ...  Another trend is the use of AI techniques (such as those from multi-agent systems, computational game theory and decision making under uncertainty) to take autonomous allocation and control decisions.  ... 
doi:10.1038/s42256-019-0120-6 fatcat:pkohdsov2bdtfge2ng2rx2qswq

Possibilities at the Intersection of AI and Blockchain Technology

Integration of both technologies forms a decentralised AI which enables the process of decision making on digitally encrypted platform for secure data sharing without involvement of any Third Party.  ...  An Algorithm is proposed in two parts, based on one of the given issues, which predicts the action plan of AI for destructing malware blocks in blockchain  ...  Decentralised AI technology enables clever decentralized selfgoverning agents for fast and automatic approval of data for different stakeholders. VI. AI FOR BLOCKCHAIN A.  ... 
doi:10.35940/ijitee.a1030.1191s19 fatcat:mqdpgojefbenzfohv7eoigsduu

Smart Is a Matter of Context [chapter]

Julien Nigon, Nicolas Verstaevel, Jérémy Boes, Frédéric Migeon, Marie-Pierre Gleizes
2017 Lecture Notes in Computer Science  
OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible.  ...  Smart cities involve, in a large scale, a wide array of interconnected components and agents, giving birth to large and heterogeneous data flows.  ...  Decentralised Decision. Since every decision is made at the agent level, the structure of a multi-agent system decentralises the decision making process.  ... 
doi:10.1007/978-3-319-57837-8_15 fatcat:fu7mli2adfdjnhglty2ddnbwj4

Perspectives for using software agents in e-Government applications

Zbigniew Piotrowski
2008 Annales UMCS Informatica  
Governing a country includes a set of decentralised processes. Moreover, in the unions of countries with integrated economic space, the decentralisation issue gains the attention.  ...  In the conclusions of the paper, it is suggested that the agent-based technology for opening agents-ready virtual offices by agencies at all levels of government should be used.  ...  An agent can seek necessary resources in a heterogeneous environment.  ... 
doi:10.2478/v10065-008-0019-z fatcat:zyhahvteg5gy7ide2dui4xf4ry

Distributed knowledge management for autonomous access control in computer networks

A. Seleznyov, S. Hailes
2004 International Conference on Information Technology: Coding and Computing, 2004. Proceedings. ITCC 2004.  
Communicating between each other they make decisions on whether a certain user or device can be given access to a requested resource.  ...  It employs autonomous agents for distributed knowledge management and integrates them into an autonomic middleware component.  ...  By analogy with social communities [5] we identify, from an information flow perspective, two types of agent groupings: groups of practice and groups of interest.  ... 
doi:10.1109/itcc.2004.1286686 dblp:conf/itcc/SeleznyovH04 fatcat:yx4hb3zayrcrhh2bjzculv6znq

What Impact does Artificial Intelligence have on Corporate Governance?

Ilya Ivaninskiy, Irina Ivashkovskaya
2020 Korporativnye Finansy  
research indicates that AI improves corporate governance and lowers agency cost by automating decision making using real-time big data analysis.  ...  There is consensus that this environment calls for fundamental reconsideration of corporate governance and for the revision of regulatory models, moving towards decentralisation.  ...  Authors highlight that a narrow focus on shareholders' benefits is suboptimal in the long run, as it creates an environment in which conservative decision-making is prioritised.  ... 
doi:10.17323/j.jcfr.2073-0438.14.4.2020.19-30 fatcat:ncjxbb4zurhknkbpr4x3rjnpry

Introduction to Technology and Corporate Law [chapter]

Andrew Godwin, Pey Woan Lee, Rosemary Teele Langford
2021 Technology and Corporate Law  
Just as human judgment will continue to be relevant in decision-making in companies, human judgment will continue to be relevant in supervision and decision-making by corporate regulators.  ...  Whether a company is managed by algorithms or constrained human agents, liability for corporate wrongs in a technology-enabled environment should extend not only to the board but also beyond to employees  ... 
doi:10.4337/9781800377165.00007 fatcat:fmsqhikitzcrlfvmuzvsceho7y

Multiagent Deep Reinforcement Learning: Challenges and Directions Towards Human-Like Approaches [article]

Annie Wong, Thomas Bäck, Anna V. Kononova, Aske Plaat
2021 arXiv   pre-print
The combination of deep neural networks with reinforcement learning has gained increased traction in recent years and is slowly shifting the focus from single-agent to multiagent environments.  ...  solutions in multiagent reinforcement learning.  ...  These opponent models can be used by an agent to guide its decision-making.  ... 
arXiv:2106.15691v1 fatcat:7sy6cianq5dh5a7n6clvjdlrxy

Challenges for the cyber-physical manufacturing enterprises of the future

Hervé Panetto, Benoit Iung, Dmitry Ivanov, Georg Weichhart, Xiaofan Wang
2019 Annual Reviews in Control  
To make use of these, an environment is needed that allows the integration of the systems forming a System-of-Systems (SoS). The changing environment requires models which adapt over time.  ...  ., for decision-making). These systems are heterogeneous and build by different stakeholders.  ...  The authors would like also to thank the IFAC INCOM 2018 symposium organising committee who allowed to organise a panel session in Bergamo (June 2018) for brainstorming with the attendees on the future  ... 
doi:10.1016/j.arcontrol.2019.02.002 fatcat:giwvo3v3dzggpkhu7elcofsrva

Data Economy 2.0: From Big Data Value to AI Value and a European Data Space [chapter]

Sonja Zillner, Jon Ander Gomez, Ana García Robles, Thomas Hahn, Laure Le Bars, Milan Petkovic, Edward Curry
2021 The Elements of Big Data Value  
From a data economy point of view, AI means algorithm-based and data-driven systems that enable machines with digital capabilities such as perception, reasoning, learning and even autonomous decision making  ...  AbstractArtificial intelligence (AI) has a tremendous potential to benefit European citizens, economy, environment and society and already demonstrated its potential to generate value in various applications  ...  These are used by the reasoning and decision-making technologies to deliver: edge and cloud based decision making, planning, search and optimisation in systems and the multi-layered decision making necessary  ... 
doi:10.1007/978-3-030-68176-0_16 fatcat:n7fgc76zbfbznm4npmt3iis7si

Industry 4.0 Technologies Impact on Supply Chain Sustainability [chapter]

Mohammad Akhtar
2022 Supply Chain - Recent Advances and New Perspectives in the Industry 4.0 Era [Working Title]  
Supply Chain Operations Reference model (SCOR) defines basic processes of the supply chain (SC) into five categories as Plan, Source, Make, Delivery and Return.  ...  The advanced digital technologies of I40 such as big data analytics (BDA), artificial intelligence (AI), machine learning (ML), internet of things (IoT) and sensors, block chain technology (BCT), robotic  ...  , more flexible SC planning and effective decision making.  ... 
doi:10.5772/intechopen.102978 fatcat:ioajp6lphjdnvawfch5imtyc4a

A Survey of Deep Reinforcement Learning in Video Games [article]

Kun Shao, Zhentao Tang, Yuanheng Zhu, Nannan Li, Dongbin Zhao
2019 arXiv   pre-print
Besides, DRL plays an important role in game artificial intelligence (AI).  ...  We also take a review of the achievements of DRL in various video games, including classical Arcade games, first-person perspective games and multi-agent real-time strategy games, from 2D to 3D, and from  ...  Making decisions in these environments is challenging for DRL agents. A critical component of enabling effective learning in these environment is the use of memory.  ... 
arXiv:1912.10944v2 fatcat:fsuzp2sjrfcgfkyclrsyzflax4

Ontological Approach Towards E-business Process Automation

Baolin Wu, Li Li, Yun Yang
2006 2006 IEEE International Conference on e-Business Engineering (ICEBE'06)  
The domain knowledge of e-business processes is conceptualised as an e-business process ontology that enables agents' communication in e-business application sharing and reusing.  ...  An innovative e-business process modeling framework is proposed that outlines the building blocks required for Internet-based e-business in order to enable e-business process automation.  ...  Recently from the AI perspective, the ontological approach has been considered in dealing with heterogonous problems.  ... 
doi:10.1109/icebe.2006.75 dblp:conf/icebe/WuLY06 fatcat:ilx7k2twxnbtjpqr5ucknfzwsi

Introduction: Rethinking AI. Neural Networks, Biometrics and the New Artificial Intelligence

Mathias Fuchs, Ramón Reichert, Mediarep, Philipps Universität Marburg
2020 Digital Culture & Society  
Theoretical approaches that have been suggested to accomplish this redefine such basic and fundamentally vital operations as decision making, sensing, network control and agency.  ...  An important question for the purpose of this issue is: In which ways are the recent trends of AI deconstructing the limits of the human? (cf.  ... 
doi:10.25969/mediarep/13522 fatcat:gvro5uk76jf5rjgkq6d77h3pmm

Multiagent Model-based Credit Assignment for Continuous Control [article]

Dongge Han, Chris Xiaoxuan Lu, Tomasz Michalak, Michael Wooldridge
2021 arXiv   pre-print
However, agents in the real world often operate in a decentralised fashion without communication due to latency requirements, limited power budgets and safety concerns.  ...  By formulating robotic components as a system of decentralised agents, this work presents a decentralised multiagent reinforcement learning framework for continuous control.  ...  ã1 𝑡 , . . . , ã𝑛 𝑡 ), where (3) ã𝑖 𝑡 = 𝑎 𝑖 𝑡 if 𝑖 ∈ C 𝑎 default if 𝑖 ∈ 𝑁 \ C, where ã𝑖 𝑡 denotes the action of agent 𝑖 is replaced by a default one if 𝑖 is outside the coalition, a widely  ... 
arXiv:2112.13937v1 fatcat:npresojzhffcjhbtuzjmdwx2hy
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