Friday, 16 May 2014

Cooperating... or not.

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.


And what about competing for a goal? Well, almost as easy, but not really the same thing: when competing, you take the other's scoring from your own one, so you can end up having some futures with a negative scoring, and negative scorings use to make players a little too prone to suicide.

In the next video, just to make the "competition" a little more "visible", I added a nasty new goal: suck energy to others by firing your main truster into them!



I am not quite happy with neither of the two videos, they do contain the idea of cooperating or competing, but somehow the efect is not what I spected. I assume adding a second layer of AI will make them realize how bad it is to push another player to dead while cooperating (cooperating with the sucking rockects goal "on" didn't avoid some occasinal deaths, not so different from the "war" video as it should be).

By the way, all this is available on the last V1.1 of the application I uploaded some days ago.

Wednesday, 14 May 2014

A seminary on optimizing using entropic intelligence.

This past monday I hold a small seminary on the University Miguel Hernandez (UMH) of Elche about optimizing using this entropy based AI, in short there will be a nice video of it, but in spanish my friends (I will try to subtitle it to english if I have the right to do it on the video and the patience).

The note about the conference can be found here (again, the little abstract is in spanish, and google translate didn't work for this url, at least for me):

http://cio.umh.es/2014/05/07/conferencia-de-d-sergio-hernandez-cerezo.html

A google translation of the abstract, not so bad... once I have fixed some odd wordings:

Abstract:

Entropy is a key concept in physics, with an amazing potential and a relatively simple definition, but it is so difficult to calculate in practice that, apart from being a great help in theoretical discussions, not much real usage is possible.

Intelligence, on the other hand, is extremely difficult to define, at least in such a general way and specific enough to allow a direct and general conversion into any artificial intelligence algorithm.


A new approach to what defines "intelligence" or "intelligent behavior" points directly to the concept of entropy as ultimately responsible of it, and although the design and development of this idea on a theoretical level is somewhat complex, its application into algorithms for artificial intelligence turns out to be incredibly simple, making it a very promising approach even at this early stage of the idea.


The resulting intelligence is able to handle any kind of "system" that you can simulate in a "smart" way without the need of defining any specific goals.


Also it is  shown a way to manipulate the  formulation of entropy itslef in order to "implant" in the resulting intelligence a tendency to maximize any objective function of our choice, obtaining truly useful algorithms in the field of proccess optimization.


As an example, we will apply the above idea to create a simple algorithm capable of driving a simulated kart around unknow circuits on all types showing a very " close to optimal" behavior.


I will edit this post (and add a new one) once the video is available.

Finally the video is out, but I can't find a way to subtitle it, surely only the owner can.