Many (14?) years back I attended a conference (now I can’t remember what it was for, I think a complex systems department at some DC area university) and saw a lady give a talk about using agent based modeling to do computational sociology planning around federal (mostly navy/army) development in Hawaii. Essentially a sim city type of thing but purpose built to help aid in public planning decisions. Now imagine that but the agents aren’t just sets of weighted heuristics but instead weighted heuristic/prompt driven LLMs with higher level executive prompts to bring them together.
I’m really excited to see this kind of stuff experimented with. I find it’s really useful of thinking of machine learning agent training in terms of creating a topology through balancing of the weights and connections that ends up being a model of a particular domain described by the data that it’s being fed. The agent learns patterns in the data it observes and creates an internal predictive model based on that. Currently, most machine learning systems seem to focus on either individual agents or small groups such as adding a supervisor. It would be interesting to see large graphs of such agents that interact in complex ways and where high level agents are only interacting with other agents and don’t even need to see any of the external inputs directly. One example would be to have a system trained on working with visual input and another with audio, and then have a high level system that’s responsible for integrating these inputs and doing the actual decision making.
Many (14?) years back I attended a conference (now I can’t remember what it was for, I think a complex systems department at some DC area university) and saw a lady give a talk about using agent based modeling to do computational sociology planning around federal (mostly navy/army) development in Hawaii. Essentially a sim city type of thing but purpose built to help aid in public planning decisions. Now imagine that but the agents aren’t just sets of weighted heuristics but instead weighted heuristic/prompt driven LLMs with higher level executive prompts to bring them together.
I’m really excited to see this kind of stuff experimented with. I find it’s really useful of thinking of machine learning agent training in terms of creating a topology through balancing of the weights and connections that ends up being a model of a particular domain described by the data that it’s being fed. The agent learns patterns in the data it observes and creates an internal predictive model based on that. Currently, most machine learning systems seem to focus on either individual agents or small groups such as adding a supervisor. It would be interesting to see large graphs of such agents that interact in complex ways and where high level agents are only interacting with other agents and don’t even need to see any of the external inputs directly. One example would be to have a system trained on working with visual input and another with audio, and then have a high level system that’s responsible for integrating these inputs and doing the actual decision making.