Starting 3rd Zero X Season

On July 7 2012 we start the 3rd Zero-X season. This will be the first season with
the new level "Clone War". The best agents of the 2nd season will be among the first participants.
For now this are Virgina, Say Cheese, Scorpion King and Unexist.

But don't worry as soon as you accomplish the first level "Greenfields", you get
immediately access to the next level. The conditions are still the same:
You need to participate in a least 5 tournaments and win 40 points.

Looking forward to see the first winners.

Winner of 2nd Season Greenfields

Wood Elf strikes again! The winner of the 1st Zero X season is also the winner of 2nd season.

In the 2nd season disengage was introduced and the combat damage, was changed in the favor of the attacker and therefore rewarded a more aggressive behaviour. But little has changed here as a look at the ranking list shows.

As announced at the beginning of this season: you will need an agent with at least an average of 4.0 EPS and 5 tournaments to get to next level "Clone War". Here are the top five agents, who achieved that goal:

6.62 Wood Elf
6.08 Mild Gruyere
6.08 Bulldog
5.66 Rincewind
4.24 Gruyere

The usual suspects at the top, let's have a look at the agents that have won more than one tournament:

1st places
3 Rincewind
2 Wood Elf
2 Bulldog
2 Oreiller

At first glance it looks surprising that Oreiller didn't made it for the next level with two 1st places. But taking a closer look we can see, that it didn't perform very well between the 2nd and 6th tournament. I'm sure he will join soon as well as Tentacle Cat, both are only a few points away.

In the next few blog posts I want to take a closer look at all the new features of the new level Clone War and introduce them step by step.

www.zero-x.net

Combat Damage in Season 2

The combat damage in ZeroX has been quite high. Until now the combat damage has been divided inversely proportional to the population size, both agents suffered the same combat damage.
With season 2 this will change in favor of the bigger adversary. The combat damage for the smaller will remain the same. But the damage for the bigger will also be inversely proportional to the population sizes. Let's compare the two algorithms with a seek and destroy sequence with:

		Season 1		Season 2
step		Agent 1	Agent 2	Agent 1	Agent 2
1	population	1000	1500	1000	1500
	combat damage	600	600	600	400
2	population	400	900	400	1100
	combat damage	276	276	293	107
3	population	124	624	106	993
	combat damage	103	103	96	11
4	population	21	521	10	982
	combat damage	20	20	10	1
5	population	1	501	0	981

As in season 1 both adversary were classified excat the same, therefore they suffered the same damage. The bigger one could never take advantage of his population superiority. This will now be considered.

Free Actions

Release 0.9.5 introduces two new actions. Both are free, means, they don't take any actions costs.

First with info you can now store messages during your agents think method. Later you can analyse them in the reports of the game reviews. All messages saved this way are private. Only the owner of the agent can see them.

info 'my secret debug info'

Second is time. Time simply returns the time units elapsed since the game has been started.

time # => time_units in floats

This is the last release before the first tournament will take place.

The documentation and the SDK have been updated.

Don't Move

In certain situations it's the best action for an agent to take no action. Don't move just stay where you are.
And sometimes it comes handy when an agent can even move to nil, which has exactly that effect no movement.

move_to nil
move_to 0,0

But until now move_to costed in any case action costs and saved move reports. Now we changed that behavior if nil is passed. It will now take no action costs and will not save a move report anymore.

Update
move_to 0,0 also takes no action costs or saves reports.

Tungmar submitted a patch to check, if your agent has moved or not. In your tests you can now use:

@agent.think
@agent.should_not have_moved

The Documentation has been updated and a new SDK (0.9.3) is available.

Releasing Zero X

I'm pleased to announce, that I released the beta version of Zero X yesterday.
Zero X is a programming game, that let's you create an agent program, that will then compete with others agents in different tournaments.
The first test tournament will take place this month, the exact date will be announced in this blog.

If you are interested in artificial intelligence, artificial life, robotics or cybernetic systems then check it out!

www.zero-x.net

Codename Zero X

Zero X is a new programming game, written in ruby and made to write in ruby. The first level is based on the greenfield scenario described in an early (german) post.
The players can code agents, which then will be uploaded to participate to the tournaments.
I hope to release a first beta version still this month, until then I leave you with a first screenshot showing the ranking page:

Szenario Greenfields

Die Lotka-Volterra-Gleichung beschreibt die Wechselwirkung von Räuber- und Beutepopulationen, wobei sich die Räuber- von der Beutepopulation ernährt. Interessant dabei ist, dass die gegenseitige Wechselwirkung der beiden Populationen das System stabil hält und sich dadurch selbst reguliert.

In meinem neusten Projekt, einer Simulation, habe ich dieses Szenario nachgebildet. Die Beutepopulation, bei mir einfach Resource genannt, hat im Gegensatz zur Obigen ein begrenztes Wachstum. Dadurch schwanken die beiden Populationen nicht mehr, in einer sinusförmigen Kurve, sondern stabilisieren sich auf einem Niveau.

Zudem wollte ich, die Resource auch unabhängig von der Population, meinen Räubern, simulieren können. Um dies zu erreichen musste ich die Resource von der Populationen entkoppeln. In meiner Simulation ist nur noch die Population an die Resource gekoppelt, hat aber auf diese Einfluss, indem sie sich von dieser ernährt und direkt deren Grösse ändert. Die Grösse der Resource auf der anderen Seite ist ausschlaggebend, wie viel Nahrung für die Population verfügbar ist und steuert damit deren Sterberate. Dadurch ist die Wechselwirkung wieder gegeben.

Dieses erste Szenario, das ich vorläufig Greenfields getauft habe, soll nun die Basis für verschiedene KI Agenten werden. Sie sollen die Populationen steuern und sich in diesem System zurechtfinden.