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.