A 2016 paper entitled “An Agent-based Design for Distributed Artificial Intelligence”, by Carbone and Crowder and their 2012 Springer publication on Artificial Cognitive Architecture explains in great detail how to design and implement such an architecture. Specifically, how stochastic diffusion is designed-in and prioritized as part of an overall system design.

I agree that balancing autonomy and interdependence in MAS can enhance performance, robustness, scalability, and adaptability of the system. Allowing agents to act autonomously can improve response time and avoid unnecessary communication and coordination overhead. Enabling agents to interdepend on each other can lead to resource-sharing, learning, cooperation, and achieving collective outcomes beyond individual capabilities.

Yes. To give an example of how autonomy and interdependence are related, and thus can also harm each other, consider the problem of evaluating noisy judges in unlabeled settings. No single judge can be perfect, so we value the independence/autonomy of the judges. As long as they are cooperating in trying do a good job.If they just parrot each other, we essentially have one source errors. Error independence then becomes the virtue we would want most in our necessarily imperfect, honest judges. If you are interested in how the concept of error independence allows you grade noisy AI algorithms without an answer key (labeled data), check out an introduction to its mathematics - https://github.com/andrescorrada/IntroductionToAlgebraicEvaluation

Interesting! For dealing with uncertainties about the environment, other agents' actions, I like probabilistic reasoning such as Bayesian networks or partially observable Markov decision processes (POMDPs). Agents can use these techniques to update their beliefs, assess risks, and make informed decisions under uncertainty.

One way to view balance is that one must fear the mob and dictators alike in MAS. The challenge then becomes how to have a way that the MAS system can evaluate itself. By construction, conflicting information will exist. We should treat this like a feature and not a bug. Accept that all agents individually will make mistakes and turn this into an asset. One example of this very complex and multi-faceted problem I can speak about is evaluation. If you give me a bunch of experts that are disagreeing nearly independent of each other, I can grade their decisions without knowing the right answers to the test they took. Readers of these notes must have more examples of how we can benefit from conflicting information from the agents of the MAS.

Right! To illustrate a rescue scenario, imagine a team of autonomous drones is deployed to search for survivors after an earthquake. Each drone in the team is equipped with its own set of sensors, such as cameras and thermal imaging systems. The drones are also equipped with communication systems that allow them to share information with one another. When the search operation begins, those human helpers autonomously explore the area. They communicate their findings with the other drones in the team. For example, if one drone detects a potential survivor, it can inform the other drones, which may then converge on that location to assist in the rescue operation. Isn’t this cool?

Threats and Security Multi Agent Systems are vulnerable to a number of threats that can compromise security of the agent and jeopardize systems security. These security issues should be taken into consideration during development of multi-agent systems.