Monday, December 24, 2007

Recap to go on

Ok, to go on now it is really necessary to recap and put order in all that stuff I wrote about up to now.

Composable information theory is just my idea to represent evolutionary and relational information derived from the interaction with the abstracted virtual world. It is something like applying collaborative info collations (information unit chains) with an intelligence working with it.

Has been now understood that somewhat logic able to work with these information unit chains is needed. It is necessary to define this "logic" before proceeding.

Data and business logic, just for fun

If I remember correctly, two of the 5 tiers in a J2EE architecture are the resource tier and the business logic tier. So, in the resource tier normally we have informations stored in different types of repository. The other tier contains only the logic to manipulate data, and infact it is called business logic tier. Then we have a SOA architecture where all business logic functionalities are kept connected and available as services, but this is another story. Ok now, as here we have no client tier by now, no presentation tier, no integration, by now, let's remap the structure organized by the composable information units into the resource tier while we need to map the logic to manipulate this structured tree of data, into the business logic tier. This second remapping action is the second ingredient of two in the mix I told about before.

Sunday, December 23, 2007

Composition is one ingredient in a mix of two.

Let's google for some docs, let's see what comes out: many documents say that the most interesting approach is the "collaborative programming", whose meaning I'd like to adapt to my intent. Let's redefine collaborative programming something that let me create a software bee, or ant, or whatever swarm unit, to put in a software world where evolutionary behaviour can reward the unit whose behaviour is the best one in terms of target tasks accomplishment, Ok, now let's put a wide high level scenario on this and let's see what are the common trends:

1 Neural network
2 Virtual, collaborative, swarm programming
3 My simjple game, composable information paradigm programming, that's what I've been playing with here sinnce now.

The intent of this absurd and shy comparison, is just to find an element to add to my composable information paradigm game, in order to have a new approach that is not just a clone of the first two ways. Infact it has the idea of "information changing and assembling" but it misses in my opinion an active side that is somethijng more than linking similar pieces of informations. Let's see the list of features I added some post ago:

  1. Persistence of knowledge
  2. Events correlation
  3. Rules for "good" and "bad"
  4. Abstraction, combination... in other terms: information composition
I can say I have an idea of points 1, 4, but misses a component for point 2 (3 doesn't matter by now)

Thursday, December 20, 2007

I have a question: what's the difference between swarm programming (where the swarm population is not made up of devices, of elettromechanical units) and neural networks ? Is there a virtual swarm programmin model, where behavioural composite programming can benefits of a logical model ? In other terms a swarm of software units whose every unit contribution is the same as that one of an "ant" or a "bee" for a real swarm. In these cases the trend of the swarm get advantage of the successfully trend of a unit. Units strive to "accunulate" around the correctly behaving one. Correctly means succesfully in terms of unit life intents. A neural networks is still a cooperative system ? Maybe not, as at the end of the day I see a neural network as an information unit weight router and switcher, every unit, every neuron has NO behavioural intelligence (a threshold is not intelligence). On the other side, a neural network is a good computation pattern more abstracted and recyclable. A swarm programmin seems to me another way to obtain neural networks results, with less generalized engine algorithm. Is this last one more "modern" and less computation power consuming ? I need advice.