Entropic AI using Excel

Some days ago, Raul Perez (@raulperezrpm) kindly contacted me on twitter to comment about my first talk on entropic intelligence, and just two days later, I received a video from him of the entropic algorithm working inside Excel macros!

Maze: Fractal vs Entropic

Just after reading my own last post I wondered why I didn't let both algorithms to solve the maze in similar conditions so I could have a clear idea of how better one is compared to the older.

Wow, I didn't remebered the lineal "Entropic" AI to be so limited! I always tested it in the open field as I knew it didn't perform well in maze-like environments, but it looks like the google car racing against a F1.

So just have a look to the fractal "One Way" version of the last post solving the maze:

First talk translated into english

While reviewing the video of my first talk (I do it frequently, but this time I happily discovered it mentioned on a data analysis blog I was just reading) I noticed automatic subtitles were available, and quite nicely converted to text for my surprise, wow!

I tried the english automatic translation and hey, you wouldn't say, but it was near perfect, even the little jokes were almost intact!

So if you were willing to listen to a simple explanation of this "Entropic Intelligence" algorithm, one you can easily understand and directly apply to your home-made kart simulation (or whatsoever it is), here you are, just remember to activate subtitles and then, clicking on the gear, select translate and choose you own languaje, it will do it quite right.

Entropic vs Fractal AI

This blog has been devoted to "Entropic Emotional AI" until some weeks ago. Now I call this "old" implementation of the AI the "linear" or the "entropic" version, while now my attention has shifted to "Fractal Emotional AI".

Why? What makes this change in the wording means? Is "Fractal" any better that "Entropic"?

Short answer is Fractal is much better, powerful and simplier. It is the big brother of the now weak Entropic AI. It performs much better that the previous one in terms of the emerging intelligent behaviours and, more important (or not) it is far more flexible and extendable than it was never.

Video of my talk at Murcia University

Last november 19th 2014 I had a talk at the Murcia University titled "Entropic Emotional Intelligence for video games".

The talk is in spanish and surely it is difficult to follow for any foreign, my spanish is a little "local", but I will try to subtitle it into english sooner than later, so come back if you missed it.

The intelligence I present here is exactly the one being discussed on the already ongoing paper, except for a nice addition for negative feelings introduced later on the paper, what I call "relativized feelings" that is still not covered in this blog... I am becoming lazy!

Remember you have the slides, both in spanish and english, at the download page.

News from the front

Before saying actually nothing, and just to show something in the post, I will show some of the drawings I am preparing for the paper:

In this case, the drawing is a little obsolete -code is still changing its shape- and I need to re-draw it.

A Talk on Emotional Intelligence

Yesterday I presented the "Entropic Emotional Intelligence" model in a long (2h!) talk at the Computer Science faculty, Murcia University, as a way to add intelligence to video game players:
 

In about one week there will be an official video (in spanish) in the university tv site, that I will upload to youtube so I can subtitle it to english.

The slides in spanish and english are available at the blog's download page.

In the meanwhile, the paper I am working on is getting slowly to the finish line. I can't really publish it on any journal because they only accept 30 pages or less articles, not +100 pages, so I will publish it directly in ArXiv.

Uncertainty

If the algorithm wants to be generally usable, it must be able to gracefully deal with uncertainty.

Gain feelings

This is the 6th -and last- post about "Emotional Intelligence", please visit the Introducing Emotional Intelligence, goal-less intelligence, enjoy feelings, fear feelings and mood feelings before you go on reading for a proper introduction on the subject.

Introduction

To end the enumeration of the three basic feelings associated with a goal, we need to deal with gains and looses, and their proper feeling representation.

Gain or Loose feelings

When something you had at the initial state, like your health level, is higer or lower in the final state representing the end point of the future, it means you have gained or lost something.

Emotions full power

In this post I just show you a couple of new videos using the full "emotional" model for the goals and also a new system to auto adjust the joystick sensitivity (I will comment on this on a future post, it is far more important that it seems).

Asteroid field

First video shows 6 rockets being streessed by a asteroid field (with 50 of them) randomly falling down and how the actual intelligence can deal with this without getting nervous at all (this is thanks to the new joystick model).

I created this simulation because I needed some visual way to judge how competent the agents are in hard/delicate/streessing situations. It was the third or fourth video of a serie, as it was almost imposible to make a rocket be hited by a rock using 10, 20 of 30 asteroids at the same time, so finally I tried with 50, and even then only one rocket get hited!

We need the algorithm to be solid rock and stable, so this kind of tests are of great interest to me.

It is really the most remarkable video I have produced this far.

Mood feelings

This is the 5th post about "Emotional Intelligence", please visit the Introducing Emotional Intelligence, goal-less intelligence, enjoy feelings and fear feelings before you go on reading for a proper introduction on the subject.

Introduction

After discussing the simpliest feeling associated with a goal, the enjoy feeling and its counterpart, the fear feeling, and the way the are added to calculate the global "step enjoy" feeling after a agent change of state -or step- we are now going to start dealing with the enjoy modulators.

We will start with the "mood feelings", the simpliest and more evident form of enjoy changers, and then turn into the most strange ones, the gain and loose enjoy modulators.

Fear feelings

This is the 4th post about "Emotional Intelligence", please visit the Introducing Emotional Intelligence, goal-less intelligence and enjoy feelings before you go on reading for a proper introduction on the subject.

Introduction

As commented in Introducing Emotional Itelligence post, the goals, when are defined usign "feelings" toy models, have only three scoring parameters, three kind of emotional "outputs".

The first of them correspond to things you "enjoy" experiencing, like speeding where you enjoyed velocity. Enjoy feelings are basically added together into a general "enjoy feeling" score after each movement the agent does.

But all the three components of a goal, each kind of basic feeling (enjoy, moods and gains) has a reverse, a negative counterpart you need to know and properly manage in your algorithm.

Fear feelings

Enjoy feelings

This is the 3rd post about "Emotional Intelligence", please visit the Introducing Emotional Intelligence and goal-less intelligence before you go on reading for a proper introduction on the subject.

Enjoy feelings

Once I had this simulation with the goal-less algortihm working I wanted to go further. The kart was really driving it quite nicely, but it clearly was not optimal.

Why? The idea was so simple and powerful it was not clear the problem at a first glipse.

Goal less intelligence

This is the 2nd post about "Emotional Intelligence", please visit the Introducing Emotional Intelligence before you go on reading for a proper introduction on the subject.

Goal less intelligences

In my first post I already commented on the internals of the simpliest entropic intelligence possible, one that scores all the futures as 1. If you haven't read it and want to know in more detail this case, you can visit the link first. Anyhow, I will try to summarize the basic working of tis model again.

First "emotional" video

My code is not still "fully emotional" this far, some cases are not still used and others lack more testing, but I am ready to produce my first video where goals are considered in this new "emotional" way.

The video just shows the old test case of a set of agents -karts in this case- moving around, where the must collect drops and then deploy it on squared containers to get a big reward, but this time they are rockets inside a cavern and follow the goals in a fully "emotional way".

The changes are not totally evident in this case, the task is too simple to make a great difference, surely I need to find more challenging scenarios for the next videos. But you will still notice the big step in the small details: how actively the pursue theirs goals and how efficiently they do it.

Introducing Emotional Intelligence

In the actual entropic intelligence algorithm, the scoring you assign to each different future you imagine is the golden key, as they determine how the options you are considering will compare to each other, ultimately defining the resulting "psicology" of the agent that makes it behaves one way or the other.

These future scorings are made up by adding the effects of a set of different "motivations" or goals the agent has, like in "I love speeding", "I care about energy" or "I care about health", measured over the future's path, step by step, like in a path integral aproximation.

Being able to define the right motivations set for an agent, along with a proper way to calculate the different effects those motivation could have on every step the agent takes, and mix them together to get the correct future's score, is ultimately what I am looking for and the life motiv of this blog.

Adding evolution to the mix

My second experiment was about using a simple evolutive algorithm to fine adjust the goal strengths in order to get the best possible set of strengths given the environment you place the players in.

I added a "magic" check box on the app so you can switch on this evolution, then add new players if desired, goals, and let the population grow and the best adapted selected over time.

Follow my orders!

After some months without working in this algorithm, I am back with some new ideas to code, but before this, I want to show you a couple of experiments I made before the summer break.

First one, showed at the talk in Miguel Hernandez University in an early stage, is just a concept test: could this intelligence be used to drive a vehicle without effort and safely, but following you directions in real time?

Imagine a real car using this algorithm to drive you anywhere, you can let it drive for you as in a google car, but with an added hability of a "semi-autonomous mode".

Why do reductive goals exits?

Ten minutes ago I discovered what exactly "reductive" goals represent, or I think so.

As you know (other way, read the old post first) this entropic intelligence needs a simulation of a system to be able to work, but also, if you pretend it to make some hard work for you, you also need a set of "goals" that represent how much you earn when system travels from point A to point B.

Those goals I already talked about, could be categorised in "positive" goals, like earning points for the meters you run, or the energy you pick. Then we also needed "reductive" goals to make it work properly.

On the media for the first time!

I want to share an recent article about the video of the seminary I held by Francis Villatoro (@emulenews), one of the more important science blogger in spanish.

The original article is in plain spanish, but google does a nice work this time, so you can read it translated to english if you wish.

I can't be happier today!