So called "Intelligente behaviour" can be defined in a pure thermodinamic languaje, using just "entropy". Formulaes look pretty intimidating, but once you get the idea, coding it into a working AI is quite simple.
Fractalizing the same idea takes away entropy calc form the AI and makes it work much better.
Adaptive foraging, or the ability to harvest and collect items, is the main test-bed for swarm intelligences, as it resemble real life problems and mimic interesting social behaviours like ants and bees swarms.
So I am adapting my fractal AI code to deal with this problem, and as a first result, here I show you a video with only one agent (quite a small swarm!) so I can test if the new gadgets works OK.
The rocket now have a hook that will trap asteroids as a magnet. The hook is connected to the rocket with a rubber band, making the rocket-hook structure quite difficult to manage.
Controlling an arbitrary complex agent so it behaves "intelligently" by using the thermodynamic concept of causal entropic forces is possible by using an special kind of fractal decision tree, the "One Way" fractal algorithm I have commented here before.
But controlling a group of agents in a intelligent way is not that
simple. I always managed to make several agents to evolve at the same
time in the same environment, but it was done just by giving each agent
its own personal intelligence, while considering the rest of the agents
as mobile parts of the environment: obstacles to be avoided to survive.
Here is a real "swarm intelligence" controlling a group of agents as one:
Cooperating is quite easy in this framework: If you get 10 points in your score for a given future, then the other players will also get those 10 extra points. That simple.
So, if we all are cooperating on a goal, then we all share a common scoring for that goal (being it the sum of the scorings for this goal of all the players) no matter who exactly got each point.
In the case of a reductive goal, it is the same, all the players reduce their scoring with the reductive goal a single kart get, so again there is asingle reductive coeficient (multiply the reductive coefs. of all the players to get it) that is shared by all the players.
This last point is not free of troubles: If a players dies, its redcutive goal for health drops to zero, so my own scorings will be all... zero! So I lost the joy of living and let the rocket fall down to ground and break... uh! not so good to cooperate in those conditions!
The following video shows a group of players cooperating on all theirs goals. The effect is not much evident just because one layer of intelligence only simulates five seconds or so, and it is not long enough to really appreciate the difference. I hope the use of a second layer of AI (not implemented yet) will make it much more visible.
