Tummi

Exploring a 5 Dimensional Space with 3.5 Dimensional Causality

If we look at the pretty basic MiniMax algorithm for playing computer chess we can classify the traversed chess game tree as 5 dimensional, 1d - squares, 2d - pieces, 3d - color, 3.5d - time runnning forward as the sequence of the game, 4d - MiniMax algorithm moving back n forth, up n down in the game tree, and 5d - the computed permuations of the game tree of the initial chess position. Hereby the causality itself navigating the negentropy is moving always forward, relatively spoken, we just change the direction in the game tree. Question remains open if such a calculus can be implemented on a memetic level -> Xi calculus.

ENXIKRON

Hmm, okay, this started as an relative simple meme machine in mind, and now I am up to level 5 on pen n paper...

- Epsilon engine level I
- Epsilon engine level II
- Xi calculus
- Ny
- Omikron

Epsilon I was the initial intend, a meme machine for processing natural language, Epsilon II was planed as extension to map math n algorithms on an memetic level, I will not get into the details here what the other levels mean, but it is simply too off and too Frankensteiny for just an hobby project...better play some didgeridoo or alike...

Epsilon Engine

To be more precise, Tummi will be only a kind of front-end to the Epsilon engine, there will be a back-end, Luther, to view and edit the knowledge graphs, and I aim for another front-end, KEN (acronym for Karl Eugen Neumann), for language translations.

I plan different pipes for the Epsilon engine, to address different kind of queries for the same memepool as knowledge graph, but I did not work out any details yet...

Epsilon and front-end code is currently Ruby, with front-ends as simple Sinatra web-applications, database with SPARQL-endpoints will probably be Fuseki from the Jena project, cos they offer RDFs reasoner and SPARUL.

The first Tummi release is aimed as an proof of concept on the SQuA-Dataset with ~500 English Wikipedia articles, we will see how far I will get with this.

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