Sunday, 18 June 2017

OpenAI first record!

We have just submitted our first official scoring on OpenAI gym for the atari game "MsPackMan-ram-v0" based on RAM (so you do not see the screen image, instead you "see" a 128 KB RAM dump).

Our just submitted algorithm "Fractal AI" played 100 consecutive games -the minimum allowed for a official scoring- and get an average score for the best 10 games of 11543 +/- 492, well above previous record of  9106 +/- 143, so we are actually #1 on this particular atari game:


Thursday, 15 June 2017

Fractal optimising, a first paper

The fractal "family" of algorithms actually started as a very naïve optimising algorithm: after all, intelligence is just about maximising a certain "utility function", so they are quite related.

Once the fractal AI was done, the optimisation facet was again re-visited with a much more promissing results to later abandon it again.

And finally, with the help of our friend José María Amigó from the Miguel Hernandez University, we wrote an article about this fractal algorithm we named "GAS" (namely for "General Algorithmic Search" but it was actually for the capitals on our names, Guillem, Amigó and Sergio) and compared it against other similar ones out there (Basin hopping, Descent evolution and Cuckoo search).

Thursday, 8 June 2017

Fractal VS Pack-Man

Last week my friend Guillem adapted the fractal AI for the OpenAI Atari games (OpenAI is a "gym" for AIs), in particular he focused on "Ms Pack Man", an environment labeled as "unsolved" as I write this.

Yesterday the work was almost done and the first videos came out of the pipeline and, to be honest, the results have stonished me, it worked out far beyond my always-optimistic high spectations.

So here is the video that made me so happy yesterday:


Friday, 3 March 2017

Imperfect information

In the actual incarnation of the fractal AI, we need to supply it with its exact state, all the interactions with environment (the simulation) and the potential function, hand crafted, to be followed. This is know as having "perfect information".

Having perfect information of any system is just not feasible, so my models are not usable in real environments, with real drones, moving real motors, as all of them are unknow for us and we will have just some sensor's outputs as our information.

This week I have visited the Cognitive Sciencies research team at Zaragoza University as a guest for a short but intense seminary about my fractal inteligence algoritms in an effort to team with them here and there, but they really emphasized on the sensorial approach -imperfect information case- in order to make our works compatible.

Thursday, 8 September 2016

Generating consciousness

One week ago I wrote this post about an insight on what could "consciousness" be like, and I imagined it as something not-so hard to gasp as we always thought. Today I come back with a "pseudo-code" version of it on my mind.

Those new ideas have come along with an effort in our company to port the fractal algorithm into a distributed, highly scalable architecture. A work in progress that is already producing a great speed-up in our tests.

This new architecture allows me to play with big groups of fractals diseminated over a network of PCs, all doing the same decision work in paralell, to later join all their findings and take a "collegiated" decision.

More interestingly, I can now "pack" some fractals to work as one big fractal and replicate it endlessly to build a tree of cooperating fractals as a nice way to distribute work over the PCs on the network.

But how to arrange them to distribute the work even more efficiently? By building it as a "fractal of fractals", a tree of fractals whose structure evolves dynamically as you use it, to finally form a nice tree-like fractal that adapts its form to live in the environment you gave to it.

Monday, 1 August 2016

What is Consciousness?

Some days ago I wrote a little about how this fractal AI works, it was not too detailed and the really important details were intentionally left unsolved. I promise to fill the gaps in short for you to be able to try the fractals by your self, but not now.

Today I want to give an overview of how the "complete fractal AI algorithm" could look like in some months, the ideas I am actually working on, and specially some random thoughts about consciousness.

The part I am now working at is about how to add memory to the fractal AI. This far, fractal AI was totally memory-less, meaning it do not learn from experience at all. I now call this an pure instinct-drive or intuitive mind. When you ask something to this AI, it thinks on the problem from scratch, and gives you an answer that is good enough for evolving in this medium with intelligence, a "real time " decision making algorithm good enough for many task.

But while driving a rocket on a difficult environment is hard and can be done with just an intuitive fractal AI, most NP-hard problems -problems where the time needed to solve it grows exponentially with the size of the problem to be solved- are usually not so easy to solve just with pure intelligence, usually you need something more to guide the intuition.

Friday, 6 May 2016

Pathfinding problem

In my first contact with professor Talbi he proposed me to try to solve the Pathfinding general problem with my algorithms, as the examples I showed in my talk were quite similar to solving it.

The Pathfinding problem consist in finding the shortest path from point A to B and it is useful not only in programming robots to go here and there, many other problem relates to finding this path.

So I borrowed time here and there to prepare a couple of videos and yesterday I have a second meeting with him to show the videos along with the real-time example where the agent follows the mouse. Here you have the video I prepared for the meeting: