Tutorials
The role of the tutorials is to provide a platform for a more intensive scientific exchange amongst researchers interested in a particular topic and as a meeting point for the community. Tutorials complement the depth-oriented technical sessions by providing participants with broad overviews of emerging fields. A tutorial can be scheduled for 1.5 or 3 hours.
Tutorial proposals are accepted until:
January 17, 2025
If you wish to propose a new Tutorial please kindly fill out and submit this
Expression of Interest form.
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TUTORIALS LIST
Tutorial on Self-Governing Systems
Instructor : Jeremy Pitt and Asimina Mertzani
Tutorial on
Self-Governing Systems
Instructors
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Jeremy Pitt
Imperial College London
United Kingdom
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Brief Bio
Jeremy Pitt is Professor of Intelligent and Self-Organising Systems in the Department of Electrical and Electronic Engineering at Imperial College London (UK). His research interests focus on developing formal models of social processes using computational logic, and their application in self-organising multi-agent systems for engineering cyber-physical and socio-technical systems; some of this work won two Best Paper awards from the original SASO Conference. He has been an investigator on more than 30 national and European research projects and has published more than 150 articles in journals and conferences; his book "Self-Organising Multi-Agent Systems: Algorithmic Foundations of Cyber-Anarcho-Socialism" was published by World Scientific in 2021. He is a Fellow of the BCS and a Fellow of the IET, and a member of the IEEE. He completed his second and final term three-year term as Editor-in-Chief of IEEE Technology and Society Magazine in 2023, where he wrote extensively on the societal impact and ethical implications of unrestricted Artificial Intelligence.
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Asimina Mertzani
Imperial College London
United Kingdom
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Brief Bio
Asimina Mertzani recently completed her PhD in Learning and Innovation in Self-Governing Systems at Imperial College London. She holds an MSc in Applied Machine Learning from Imperial College London, graduating with distinction, and an MEng degree in Electrical and Computer Engineering from the National Technical University of Athens, graduating with a GPA of 8.7/10 (top 10% of the class of 2019) while working part-time as an Implementation Consultant at ORTEC, a global leader in supply chain optimisation. In this role, she managed complex projects for major clients, including leading supermarket chains and oil and gas companies in Greece.
ombination of artificial intelligence with social influence for collective decision making in socio-technical systems, e.g. assemblies of people, sensor networks or community energy systems. I am from Greece, I grew up in Athens and graduated from the National Technical University of Athens with an Integrated MEng in Electrical and Computer Engineering.
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Abstract
Abstract
As the transition to the Digital Society continues, socio-technical systems in future will be increasingly hybrid, in the sense that they will involve ``meaningful interactions'' in the context of socially-constructed ``social arrangements'', between humans (natural intelligence, or NLife) and computational processes (artificial intelligence, or ALife). These social arrangements are the over-arching set of mutually-agreed conventional rules that the agents (both ALife and NLife) voluntarily comply with in order to achieve both cooperative and conflicting individual and collective goals. Moreover, these meaningful interactions involve deliberation and produce decisions which determine both these goals and the selection, modification, application and enforcement of the conventional rules themselves. In the absence of any external or centralised authority, these socio-technical systems are self-governing systems; in other words, they are societies of ALife and NLife agents. While we are accustomed to human societies of NLife-only agents, and agent societies of ALife-only agents (i.e. multi-agent systems), this new paradigm presents engineers with a new set of challenges and opportunities in the design and operationalisation of such systems. This course provides an a broad and deep introduction to self-governing systems: theoretical foundations, algorithmic design specification, and interactive operationalisation.
Keywords
Self-Governance; Self-Organisation; Multi-Agent Systems; Human-Computer Interaction; Sustainability; Justice; Legitimacy; Influence; Learning; Innovation
Aims and Learning Objectives
In this inter-disciplinary course, students will be presented with a detailed definition of the features of self-governing systems, and learn about deep issues with self-governance, as exposed by psychology, economics and political science. Students will learn how these issues can be addressed through the formalisation of hybrid algorithms combining both machine reasoning and machine learning. Some practical applications of this work will be considered, especially in the context of recent developments such as ``Agentic AI'', and it will highlight the importance of qualitative human values in design if Aristotelian ideals of human flourishing and human agency are to be maintained in a society of humans, bots and LLMs.
Target Audience
Primarily PhD students and Early Career researchers; but essentially any academic generally interested in inter-disciplinary and AI from socio-technical and socio-political perspectives
Prerequisite Knowledge of Audience
Up to Master's Level in Machine Reasoning and Machine Learning.
Detailed Outline
This 6-hour course will be divided into eight lectures each of 45 minutes.
1. Introduction to Self-Governing Systems. Definition, features and examples; relation to self-organisation, multi-agent systems and ``Agentic AI'.
2. Institutions. The role of institutions, as studied in economic science, for selecting modifying and enforcing social arrangements, and their contribution to sustainable self-governance.
3. Justice. The use of concepts from philosophy such as distributive, procedural and interactional justice in producing `fair' self-governance.
4. Knowledge. The role of effective knowledge management processes identified in political science for supporting deliberation, decision-making and collective action for legitimate self-governance.
5. Influence. The bi-directional relationship between sources and targets of social influence which establish requisite pathways to balanced self-governance.
6. Learning. The value of learning from dissent and compromise in repeated deliberative processes to produce consensual self-governance.
7. Innovation. The generation of new social arrangements to support continuous self-improvement of self-governance.
8. Applications and Implications. Summary and conclusions, considering the social, legal, political and ethical dimensions of self-governing socio-technical systems.