[Wecount] exploring Distributed AI

Clayton H Lewis clayton.lewis at colorado.edu
Tue May 19 03:09:39 UTC 2020

thank you!!

On May 18, 2020, at 7:13 PM, Jutta Treviranus <jtreviranus at ocadu.ca<mailto:jtreviranus at ocadu.ca>> wrote:

I may have written this too quickly and see I wasn’t very clear what it makes manifest. Deep learning makes manifest the risks of exclusion, ignoring the edges and weak signals, prediction based on probability, using data from the past, etc. … all of which are counter to inclusive design.

Similarly I heard from several DAI researchers that principles of collaboration, diversification of agents, organic self-organization etc. are better at solving complex problems in DAI. These are some of the things we have been exploring with respect to human decision making and problem solving.

What triggered this line of thought was notes I wrote in the late 80’s when these articles came out:

Decker, K. S. (1987). “Distributed problem solving: A survey”. IEEE Transactions on Systems, Man, and Cybernetics, 17 (5): 729–740.

Durfee, E. H., Lesser, V. R., Corkill, D. D. (1989). “Trends in Cooperative Distributed Problem Solving”. IEEE Transactions on Knowledge and Data Engineering, 1 (1): 63–83.

I then started to explore where this had gone lately.

I found things like this:

Yeoh, W., Yokoo, M. (2012). “Distributed Problem Solving”. AI Magazine, 33 (3): 53–65

Mguni, D., Jennings, J., Munoz de Cote, E. (2018). “Decentralised Learning in Systems with Many, Many Strategic Agents”. arXiv:1803.05028v1.

Yang, Y., Luo, R., Li, M., Zhou, M., Zhang, W., Wang, J. (2018). “Mean Field Multi-Agent Reinforcement Learning”. arXiv:1802.05438<https://arxiv.org/abs/1802.05438>.

Waldrop, M. M. (2018). “Free Agents: Monumentally complex models are gaming out disaster scenarios with millions of simulated people”. Science, 360 (6385): 144–147.

Also this was interesting but not directly relevant:

Yang, X. S. (2009). “Firefly algorithms for multimodal optimization”. In: O. Watanabe, T. Zeugmann (Eds.), Stochastic Algorithms: Foundations and Applications, Vol. 5792 of Lecture Notes in Computer Science, Springer Berlin Heidelberg: 169–178.

I have a meeting with a group from UCLA who want to talk about applying inclusive design to DAI to address complex, edge problems.


On May 18, 2020, at 7:10 PM, Clayton H Lewis <clayton.lewis at colorado.edu<mailto:clayton.lewis at colorado.edu>> wrote:

That's an optimistic take, and encouraging to hear. I'd be interested in cites for some of the more open thinking you are seeing. What I've seen, which isn't much, works in a more formal framework, but I think you are right about what the bigger picture should be.

On May 18, 2020, at 8:25 AM, Jutta Treviranus <jtreviranus at ocadu.ca<mailto:jtreviranus at ocadu.ca>> wrote:

One of the things I noted when I began to explore AI again was that AI made manifest many of the topics we have explored in inclusive design in such a way that hard scientists could not dismiss inclusive design. One of the topics I’ve wanted to explore is Distributed Artificial Intelligence because it makes manifest the inclusion dimensions of the diversity and inclusion equilibrium. In Distributed Artificial Intelligence you need to construct the coordination, collaboration, competition or teamwork of multiple agents. You can work with small amounts of data. Design of a “social” system of sorts comes into play. DAI is also better able to handle non-linear, multi-variate learning spaces. Many of the DAI approaches are bottom up. One form, Agent Based Systems, is usually exploratory and descriptive rather than engineered and prescriptive. Approaches like Swarms self-organize and their robustness and self-repair functions happen because or in spite of the fact that they are decentralized and unsupervised.

There is even exploration of alternatives to majority rules decision making in this space.

The reason I want to explore these is not because of the potential applications but because of the way they can provide simulations of different forms of group organization and interaction that makes the points we have made manifest to hard scientists, engineers and others that do not trust the soft science of inclusive design.

I would love to hear your thoughts,


Clayton Lewis
Professor of Computer Science
Co-Director for Technology, Coleman Institute for Cognitive Disabilities
University of Colorado

Clayton Lewis
Professor of Computer Science
Co-Director for Technology, Coleman Institute for Cognitive Disabilities
University of Colorado

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