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Macro Engine with AI Predictions

This case study shows how Ascoos OS can execute macros based on AI predictions, combining logistic regression with a DSL (Domain-Specific Language). It uses the classes TArtificialIntelligenceHandler, AbstractDslAstBuilder and AstMacroTranslator to train a model, parse rules into an AST, translate them into macros and execute them dynamically.

Purpose

  • Train a model using trainLogisticRegression()
  • Build an AST from DSL with AbstractDslAstBuilder
  • Translate the AST into executable macros with AstMacroTranslator
  • Execute macros when predictions meet a condition

Core Ascoos OS Classes

  • TArtificialIntelligenceHandler
    Train and predict logistic regression models

  • AbstractDslAstBuilder
    Parse DSL statements into an Abstract Syntax Tree

  • AstMacroTranslator
    Convert AST nodes into a container of callbacks for each macro

File Structure

This case study is implemented in a single PHP script:

Prerequisites

  1. PHP ≥ 8.2
  2. Ascoos OS installed. If you’re using ASCOOS Web Extended Studio (AWES) 26, it’s pre-installed.

Getting Started

  1. Adjust your training data ($X, $y) as needed.
  2. Run the script via your web server: 
    https://localhost/aos/examples/case-studies/ai/macro_decision_engine/macro_decision_engine.php

DSL Example

WHEN predict(user.features) > 0.5 THEN
    LOG "User is eligible"
    ENABLE MODULE "AdvancedAnalytics"

Execution Flow

  1. TArtificialIntelligenceHandler trains a logistic regression model with $X and $y.
  2. AbstractDslAstBuilder transforms the DSL script into AST nodes.
  3. AstMacroTranslator maps each node to a callback:
    • LOG → print a message
    • ENABLE MODULE → enable a specific module
    • predict → call predictLogisticRegression()
  4. TMacroHandler executes commands only if the prediction exceeds the threshold (> 0.5).

Code Example

// Train the model
$ai    = new TArtificialIntelligenceHandler();
$model = $ai->trainLogisticRegression($X, $y);

// Define the DSL
$dsl = <<<DSL
WHEN predict(user.features) > 0.5 THEN
    LOG "User is eligible"
    ENABLE MODULE "AdvancedAnalytics"
DSL;

// Build AST & translate
$astBuilder     = new class extends AbstractDslAstBuilder {};
$ast            = $astBuilder->buildAst($dsl);
$translator     = new class([...]) extends AstMacroTranslator {};
$macroContainer = $translator->translateAst($ast);

// Execute based on user features
$user = ['features' => [1, 1, 0]];
$macroContainer->executeIfTrue($user);

Expected Output

If the prediction predict([1,1,0]) > 0.5:

User is eligible
Module enabled: AdvancedAnalytics

Resources

Contribution

Want to contribute to this case study? Fork the repo, add new macros or DSL enhancements in macro_decision_engine.php and submit a pull request. See CONTRIBUTING.md for guidelines.

License

This case study is covered by the Ascoos General License (AGL). See LICENSE.