Animotron
Animo language
A language based on ideas from linguistic, neuroscience, algebra and XML database. It is a declarative representation of information relationships and processing agreements.
RDF, SPARQL, OWL, XQuery, XSL and XML Database are well known technologies, but there is no single glue to put all this together. The main advantage of these formats is flexibility. However, that leads to many different representations of same structures (e.g DocBook, TEI and etc). The problem of complexity is similar to C libraries, in that we must operate at the level of the data structures. After C, came Java with its standard libraries, a language which programmers can use and which can be easily understood by other programmer. The Animo language aim to create universal language for people (not only programmers)to speak.
Each time we start new project, we have to choose which technologies we should use, and how to put them all together. We are also concerned with separating data from code, and constructing tests to assert the behaviour.
Animo solves these issues and much more. With Animo, at design time your are not forced, for example, to have to choose top-down or bottom-up processing, it can be controlled by instructions. Default evaluation strategy similar to LISP. The Animo language cover PROLOG's use cases.
The language incorporates many proven features whilst also incorporating some new ideas, with the aim of making programming more natural for humans. The eventual goal is to create a bridge between native languages and Animo structures, computer programming style should evolve to become a dialog with the computer and as such dialog constructs must be a part of the language itself. Currently the language constructions are based on English-Prime concepts, so you can write your code as through writting a book or specification.
Features:
- Integrated database space as the global context for language processing.
- Processing flow and Context mix-in
- IS-HAVE relations build into the data model
- Data and code merged into one model
- Pluggable architecture: data, models, code and languages (evaluation of different languages)
- Type safe reference resolving
- Animo language injections to XQuery, XSL and others
Merging code and data into one topological space
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There are numerous differing languages, but each share common ideas:• Processor operation is directly mapped to language operator; • Stability of processing line (order of calculations [and-structure]); • References (variables or functions); • Conditional forking of processing-flow (binary [if-then-else] or multi [case statements or pattern matching]); • Multitasking (parallel calculations [or-structure]); |
Animo language is different because:• All information resides in one reference space - "context" & dynamic language processing model. [Language evaluation model is merged with language processing model] This hides function parameters, but does not remove them completely (hide it into the function's code). Keep the possibility of static analyze to resolve "dead" references (variables). • Merging code and data into one topological space. This has some interesting effects: a) The processing line for same input can be different, because of feedback effect. b) "separating line" moving from data-code to more-less stable structures. Additionally, simultaneously, it's possible to optimize (cache) calculations to speed-up processing in general. |
Processing model
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The processing model is base on ideas taken from nervous system and has axioms: Axiom 1: Cell's life (including neuron) can be described by R = F ( C ) (1) where F - function (flow), C - environment force to cell and R - cell force to environment Axiom 2: Group of cells' life (including neuron) can be described by (1) Axiom 3: Neuron or neuron's structure choose processing flow base on input signal (pattern detection) Axiom 4: Neuron's structure can generate dynamic (depend on incoming signal pattern) or constant signal (signal will not changer over lifetime). Neural circuit always have input, output and processing path. This can be simulated by two different processing models: "from inputs to outputs" (FIO) or "from outputs to inputs" (FOI). Both of them are equal on result, but different on optimization needs and opportunities. Processing optimization for single output is better with FOI model, but best result can be achieve by balancing it with FIO model. |
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The request processing
[input] -> sense functions -> (context, processing flow) -> processing flow(context) -> [output] where sense function detect input information and transform it into processing flow and context.
Evaluation contexts
The processing flow element have access to several contexts: local, stack, global and flow.
- local is context that have highest priority;
- stack is context's train. The evaluation result pushes local context to stack;
- global context is documents stored at XML database;
- flow context is processing line until current position.
During evaluation context can be transformed to processing flow, or added to context. Result of the evaluation is sent to context.
