See how I like this new theme called responsive.  I think I will like it better because it is wider.  However, I switched back to the grunge wall because it was taking too long to set up…

In my earlier post I was considering combining game theory and measure theory methodologies of MAS and swarm systems.  From my research into this topic I found this paper https://jacobstechnology.com/acs/pdf/ESOA06Hybrid.pdf.  Although it seems like the people who published it are respectable, the paper has never been cited.  Which is strange since it is an interesting topic.  They came to the same questions I have about combining agent/swarm methodologies.

To what extent can we construct a unified development methodology that supports
both extremes (and thus intermediate agent types as well)? It is clumsy to have to use
one methodology [38] for BDI agents, and a completely separate one [26] for
swarming agents. Hybrid systems will only become commonplace when both kinds of
agents can be developed within an integrated framework.

 

They believe that integrating different agent models is an important problem.

 

Also, before that paper was published, GMU had published a paper Cooperative Multiagent Learning: The State of the Art.  In their conclusion they stated the need for Team Heterogeneity.  Meaning that the team is composed of agents with differing abilities.  They also make the case for when the team is too large that the agent’s abilities can’t all be different.  This is the conclusion I came to.  For example, when there are swarms interacting with more complex multiagent systems.  How do they cooperate and learn from each other?  What is the framework for such interaction?  How can the agent’s mathematical underpinnings act as a catalyst for communication for teaching/learning?  Can a universal framework be developed?

I would like to be able to measure the complexity of agents to determine whether a system is really a swarm or if it is a multiagent learning system etc.  A discussion of complexity measures: http://tinyurl.com/measurecomplex.  By knowing the type of system an outside agent can better adapt to, learn from, or manipulate the system.

I would like to see if game theory and measure theory (in terms of swarms) can be merged to gain a better theoretical understanding of large complex systems of learning agents.  I have been reading http://tinyurl.com/mathswarm which describes some of the mathematics behind swarms.  Based on this article they use measure theory to model swarm so I have been learning about measure theory.

Innovation and learning seemed to develop more quickly with a larger population. Like comparing europe to the native americans. So I think that it would be wise to try to see how large groups of agents who were grouped doing different tasks whether innovation would emerge.

Like have a huge “soup” of agents and have different goals for different populations of agents. Will those with similar goals learn to cooperate.

If we limit resources will innovation emerge by having different populations cooperating to create a hirarchy to accomplish their respective goals?

Essentially, when a large population with differing goals but limited by the same resources will they learn to innovate in order to accomplish their goal or will they fail.

In the macro the micro complex actions apear to be simple effects. So, in essence we have a swarm.
Start listing of large multi agent systems that might also be swarms:

traffic networks including internet, cars, planes, and any other type of transportation routing.

learning in multi agent systems when others don’t learn?

At what point does the macrophenoma of a MAS appear as a swarm like behavior?  Then in a large multiagent learning problem could the agents be viewed as swarms instead of agents?  Can an algorithm be created to discover the swarm behavior of a large system composed of MARL agents?