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DiODe Report Summary

Project ID: 647704
Funded under: H2020-EU.1.1.

Periodic Reporting for period 1 - DiODe (Distributed Algorithms for Optimal Decision-Making)

Reporting period: 2015-08-01 to 2017-01-31

Summary of the context and overall objectives of the project

Engineers and life scientists face common problems in understanding how a collective of agents working with only local information can implement a robust and optimal collective decision-making mechanism. The DiODe project aims to contribute to the principled understanding of distributed optimal decision-making algorithms through a combination of theoretical model building, interaction with life scientists, and implementation of algorithms on a highly-scalable collective robotics platform.

The detailed objectives of this project are fivefold:

1. To study optimal speed-value trade-offs in models of distributed consensus decision-making, developing novel decision theory to guide the engineering and optimisation of artificial distributed decision-making systems.

2. To study theoretically the compromise between sampling and decision-making, to predict specific mechanisms and behavioural patterns associated with solutions to such problems, and to extend the value-sensitive mechanism developed in objective 1 accordingly.

3. To develop the theory of individual ‘confidence’ in privately-held information as part of the theory of consensus decision-making, and to design distributed mechanisms able to instantiate this theory.

4. To apply the theory developed in the other objectives to the specification of individual behavioural rules for groups of hundreds of micro-robots, both to test and inform the theory, and as a new technology for robust, efficient, completely decentralised decision-making in collective robotics.

5. To test or instantiate, with named collaborators, the developed theory in natural systems including intracellular regulatory networks, neural circuits, and human groups, and to develop tools to facilitate development of consensus decision-making models by life scientists. Such tools will also be of use to engineers working with decentralised systems.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

* Generalisation of existing value-sensitive collective decision-making results to more than 2 alternatives (objective 1.2); submitted to arXiv and under review at Physical Review E (see publications).

* Studies into individual confidence and collective decision-making (objective 3); review article submitted to Trends in Ecology and Evolution (Impact Factor 16.735).

* Implementation of open-source hardware and software for augmented reality with the Kilobot swarm robotics platform (objective 4); achieved through the short-term (3 months) recruitment of two additional postdoctoral researchers, Dr Alex Cope and Dr Chelsea Sabo. Submitted for publication to IEEE/RSJ International Conference on Intelligent Robots and Systems / Robotics Automation Letters, shortly after the reporting period.

* Value-sensitive decision-making in Kilobot swarms (objective 4); presented at Distributed and Autonomous Robotics Systems 2016 (see publications)

* Development started on open-source modelling tool for engineers and life scientists (objective 5.1); presented in plenary lecture at Multiscale Modelling in Biological Systems, Institut Curie (TODO include YouTube link here?)

* Recruitment of an externally-funded (CONACYT) PhD student, Aldo Segura, to work on validating value-sensitive decision theory in microbial decision networks (objective 5.2.2), in collaboration with external project partners (Edinburgh, Groningen).

* Invited review article on ‘collective decision-making’ accepted for Current Opinion in Behavioral Sciences.

* Keynote presentation on project work, by the PI, at Distributed and Autonomous Robotics Systems 2016.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

Understanding how collectives robustly make optimal decisions has significant potential scientific and societal benefits. Improving our understanding of distributed decision-making algorithms can inform the study of decision-making motifs in diverse organisms, from intracellular decision circuits to the human brain; such understanding will be at the level of basic research, but given the importance of microbial behaviour for human health, and decision dynamics for human brain pathology, for example, the eventual societal benefit from new knowledge is potentially very large. The proposed work on optimising human committee structure could also have substantial societal benefits, for example through improving organisation of medical diagnostic committees. The most direct impact, however, is expected be in the development of a new class of optimal and robust decentralised decision-making algorithms, deployed on a collective robotics testbed. Collective robotics has long been proposed as the next stage in autonomous robotics, but designing and guaranteeing desired behaviours have presented obstacles to translation from theory to practice. Providing a ‘swarm engineering’ methodology could begin to realise the promise of swarm robotics for diverse applications, with both societal benefits and economic benefits in areas such as exploration, agriculture, and any other area in which single highly-competent robots are currently deployed or being developed.

Related information

Record Number: 198184 / Last updated on: 2017-05-17
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