# Hierarchical Temporal Memory Light Cycle Algorithm

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The algorithm to make a light cycle play more like a human has been eluding us for a long time.

Most or all action games only become more challenging by speeding up game play or number of enemies.

With the Tron light cycle game the challenge increases with speed.
We want the challenge to increase along with the computers strategic progression.

Possible help in this area:
A technology which stores huge numbers of games and their outcomes along with quick search of the games data streams.
A technology which is able to quickly identify a current game and generate a list of similar games.
Ability to improvise another game into this one by handling the interpolation, transposition and scaling of different games.

This is an implementation of my variation of the cortical algorithm PDF psuedocode:
[media]
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More vids:

The problems with seeing games at various scales, knowing which games the current game is like with low processing power are very well handled by this.
There are additional hurdles and I believe we are at a point where they can be overcome.

If you are not excited about math you will like this new biological based emergent algorithm very much.
It offers ways to have improvised motor output and lots more.

How is this received so far? With an open mind, objections etc??

Thanks!

Huh?

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If not even IADaveMark has any idea what you are talking about, maybe you need to explain yourself better

I don't even begin to understand what problem you are trying to solve...
And can't really comment anything on the algoritm without any description of how it works or what it solves...

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I read your description and watched the video. Is this what you are saying?

- You find a list of past games that seem similar, in some respect, to the current game.

- Using the past games as a model you attempt to find a new action that is optimal.

- Optimal, in this case, is a decision that appears human like in its pursuit of a win.

- The difficulty of the CPU opponent is increased by giving it more past games to compare to.

I don't know anything about HTMs, but from what you're saying they sound like a way of storing and retrieving events from a collection of records based on some similarity match.

Am I in the right ball park?

It would be helpful if you could post a video of a light-cycle game without and with your HTM implementation. Could you compare this method to some existing ones? I've seen some really good light-cycle AIs built using genetic algorithms.

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Hidden
See numenta.com to get familiar with this general algorithm of our neocortex.

- You find a list of past games that seem similar, in some respect, to the current game.
The algorithm may recall from millions of games within 100 memory steps the best and worst matching games for example.
The important part here is we have an algorithm that is alreadying 'seeing' with 100 neuron steps like our mind.
This example is just one possible use.

>>a way of storing and retrieving events from a collection of records based on some similarity match.

***Yes but it does it over planes of generalization and scaling concepts within a game***
It is able to predict what you will do on large and small scales and even is able to predict next game with minimum CPU.

Out of the box the algorithm seems too high but as optimizers it is there waiting for you to implement.
I have never seen a game or A.I. programmer check out the vision videos and algorithms at Numenta.com and have anything intelligent to say about it.
The Tron idea was to being it about in a palletable manner but the game and algorithm may be outdated.

I don't care to see computer outwit me or companies with 2000 IQ programs running my world so since it is very tedious I don't expect anyone of the self indulgent generations to touch it but big companies- which is why they rule the world.

See numenta.com to get familiar with this general algorithm of our neocortex.

- You find a list of past games that seem similar, in some respect, to the current game.
The algorithm may recall from millions of games within 100 memory steps the best and worst matching games for example.
The important part here is we have an algorithm that is alreadying 'seeing' with 100 neuron steps like our mind.
This example is just one possible use.

>>a way of storing and retrieving events from a collection of records based on some similarity match.

***Yes but it does it over planes of generalization and scaling concepts within a game***
It is able to predict what you will do on large and small scales and even is able to predict next game with minimum CPU.

Check out the vision videos and algorithms at Numenta.com - what they do was impossible before it came out.

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Oh good, you're back.

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The important part here is we have an algorithm that is alreadying 'seeing' with 100 neuron steps like our mind.
This example is just one possible use.

>>a way of storing and retrieving events from a collection of records based on some similarity match.

***Yes but it does it over planes of generalization and scaling concepts within a game***
It is able to predict what you will do on large and small scales and even is able to predict next game with minimum CPU.

That could mean anything. Seriously, anything. Genome clustering? Database optimization? Compression algorithm?

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That could mean anything. Seriously, anything. Genome clustering? Database optimization? Compression algorithm?

The pdf on numenta seem to suggest some sort of neural network.
Have only read the intro so far, a bit interesting.

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I don't think lightcycles are a suitable problem domain for techniques that employ very little abstraction and directly reproduce the moves that gave good results in cases that match the current situation:

• How do you maintain high level plans? Even if you figure them out from your examples, how do you ensure that you stick to the same plan when your system extracts other examples?
Trapping an opponent can be a very long process: in a traditional racing game stringing together good short term reactions keeps you close to the ideal trajectory, but in lightcycles the only meaningful short term decision is avoiding crashes: clearly not enough to win, since the benefit of building walls is usually reaped much later.
• Minute geometrical differences (e.g. whether one more lightcycle path can fit in a certain gap) can have a great importance (e.g.whether the lightcycle can come back through the gap or gets trapped), but not always (e.g. you might not want to go in and out of that gap in the first place). How can you be confident that an example is relevant or irrelevant?
The state space given by lightcycle walls is huge, probably condemning your example-based approach by sheer curse of dimensionality, and decent ways to simplify it are hard to find and time-varying.

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