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Introduction(Narrator Voice, Morgan Freeman): Gnerk sat there thinking deeply about the recent swarm attacks. Gnerk might not be the smartest tool in the shed but he had pointed the dull tools he had at this problem for a long time. He had the tools of spectral graph theory at hand.
“Do you ever dream about information flow on top of graphs and how they relate to general agency?
Well, I do, probably to a worrying extent.
Many People say swarm scary, Gnerk Know Thing about swarm so Gnerk share.
Scale Free Agency, Hierarchical Agency, Multi-Agent Systems, Cooperative AI.
To know if group is coordinated we need to know when group act the same. Are there any group size based scaling laws we can make? Gnerk share model.
By end, you see methods from physics to couple graph structure to time for information flow in a group. You see graph laplacian and information flow on multi-agent systems. You see Fiedler value and how it bound collective speed.
You also see graph structure matter. We introduce smart tool for bounding size of collective agent through physics and explain part of physics - energy relation.
Gnerk like direction, Gnerk still somewhat early in figuring out, therefore Gnerk share as Gnerk.”
(Narrator Voice, Morgan Freeman): And so began the adventure of Gnerk.
Agents are think-do loopsOne agent work with other agent, make big agent, agent swarm scary, agent swarm maybe bad?
Hmmm… Not goood.
What bound size of BIG agent? (Agent swarm)
Hmmmm… How agent work in general?
(This is example of way of seeing not all information.)
They loop, hmmm but that is for one agent, how shared agent loop?
Figure: General Policy Improvement Loop from RL and Perception Action Loop from Active Inference.
Big Agent RepresentationOkay, I smart. One agent = Blob
Many agent = many Blob
Okay, hmmmm, when Many Agent = One Agent?
Intentional stance, if Many Agent quack like a One Agent, walk like a One Agent, act like a One Agent, it’s probably a One Agent.
Ah okay, cool.
Go back to cool