Design of multi-agent systems

Distributed sensing, swarm sensing, adaptive social learning, multi-agent adaptation, iterated game theory with learning etc

2014-10-13 — 2025-05-05

Wherein the Design of Multi-Agent Systems Is Considered, With Emphasis on the Crafting of Private Utility Functions to Induce Cooperative Coalitions, and Constraints From Local Information Are Examined.

agents
AI safety
bounded compute
collective knowledge
computers are awful together
distributed
economics
edge computing
extended self
game theory
incentive mechanisms
machine learning
networks
Figure 1

This is a hub page for work on designing agents to get things done collectively.

1 Cooperation and opponent shaping in RL

How do learning agents figure each other out? Can they learn to cooperate? The broad area is multi-agent reinforcement learning. See opponent shaping for the RL formalism of agents that model and influence each other’s learning, and learning with theory of mind for the broader framing of that kind of recursive reasoning about other agents.

2 Collective utility and mechanism design

If we have autonomous agents that need to cooperate, how do we design their private utility? This looks a bit like collective decisions, but I am thinking of incentive design where we get to choose the utility function, not just the mechanism — an inverse collective action problem, if you’d like.

AFAIK the canonical answer might be the agent collective intelligence of Wolpert and Tumer? (Bieniawski and Wolpert 2004; Tumer and Wolpert 2012; David H. Wolpert, Wheeler, and Tumer 1999, 2000; David H. Wolpert and Tumer 1999; David H. Wolpert and Lawson 2002; David H. Wolpert 2006a; David H. Wolpert, Bieniawski, and Rajnarayan 2011) This cashes out in a slightly abstruse interpretation of multi agent RL AFAICT.

See also the suggestive but indirect mapping between utility and fitness.

3 Value learning and assistance games

If you’re watching an agent and want to infer its reward function, see value/reward learning. The cooperative variant — where one agent actively helps another whose goals are unknown — is the assistance games formalism.

4 Multi-agent causality and games

Games always have at least two agents; they can have many more. Extending causal DAGs to include agents and decisions: see multi-agent causality. For the commitment and contracting angle, see commitment, contracts, cooperation. For the question of many agents we might consider coalition games.

5 Local versus global design

What global organising can be done using only local information? There are formal approaches to this, e.g. H. Wang and Rubenstein (2020), and more playful ones like Mordvintsev et al. (2020), who use cellular automata as a case study. See also differentiable collective automata for the gradient-based version of the latter.

6 Nature-inspired collectives

Biomimetic approaches: ant colonies, particle swarms, probability collectives, and other nature-inspired algorithms. Agents which can form coalitions might need distributed consistency.

7 Human systems as multi-agent systems

An important specialisation: groups of humans, where the agents are a particular type of great plains ape. See wisdom of crowds versus groupthink, or weaponised social media.

8 Incoming

9 References

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