TArtificialIntelligenceHandler — Native AI Class in Ascoos OS

Author: Drogkidis Christos Date: 08-10-2025 14:45 🇬🇷

Introduction

TArtificialIntelligenceHandler is the central AI class in Ascoos OS, designed to provide native intelligence without relying on external APIs or cloud services. It includes over 120 methods covering machine learning, neural networks, transformers, reinforcement learning, genetic algorithms, explainability, and more.

It is fully integrated into the Web5 Kernel and compatible with the Macro Engine, DSL scripting, and semantic modules. This documentation provides a full overview of its capabilities, method categories, and usage patterns.

TArtificialIntelligenceHandler Documentation — TArtificialIntelligenceHandler — Core AI Class in Ascoos OS

1️⃣ Key Capabilities

Train and evaluate models locally (no external libraries)
Support for supervised, unsupervised, and reinforcement learning
Explainable AI with LIME, SHAP, and saliency maps
Transformer blocks and attention mechanisms
Meta-learning and task similarity scoring
Integration with DSL macros and semantic profilers

2️⃣ Method Categories

The methods of TArtificialIntelligenceHandler are grouped into functional categories. Each category represents a distinct AI paradigm or utility:

Machine Learning: Linear regression, logistic regression, decision trees, SVM, K-means clustering
Neural Networks: Layer creation, training, prediction, dropout, batch normalization
Transformers: Encoder/decoder layers, multi-head attention, positional encoding
Reinforcement Learning: Q-learning, policy gradients, value iteration, exploration strategies
Meta-Learning: MAML updates, Reptile adaptation, gradient steps across tasks
Genetic Algorithms: Population initialization, fitness evaluation, crossover, mutation
Explainability: LIME, SHAP, saliency maps, counterfactuals
Loss Functions: MSE, cross-entropy, hinge, Poisson, quantile loss
Regularization: L1, L2, ElasticNet, spectral norm
Utilities: Dataset splitting, normalization, one-hot encoding, feature importance

Each method is designed to be composable and interpretable, allowing developers to build complex AI workflows with minimal overhead.

3️⃣ Usage Examples

Below are simplified examples of how to use TArtificialIntelligenceHandler in real scenarios:

3.1 Training a Neural Network

$ai = new TArtificialIntelligenceHandler();
$ai->addLayer('dense', ['units' => 64, 'activation' => 'relu']);
$ai->addLayer('dense', ['units' => 10, 'activation' => 'softmax']);
$ai->compile(['optimizer' => 'adam', 'loss' => 'categoricalCrossEntropy']);
$ai->fit($trainingData, $trainingLabels, ['epochs' => 10, 'batchSize' => 32]);

3.2 Sentiment Analysis with NLP

$nlp = new TLanguageProcessingAIHandler();
$result = $nlp->analyzeSentiment("I love the new Ascoos OS AI features!");
echo $result['score']; // e.g., 0.92

3.3 Semantic Macro Prediction

$macroAI = new TArtificialIntelligenceHandler();
$macroAI->loadMacroHistory($macroData);
$prediction = $macroAI->predictNextMacro(['context' => 'userIntent']);
executeMacro($prediction);

4️⃣ Integration with Web5

TArtificialIntelligenceHandler is designed to operate seamlessly within the Web5 Kernel, offering full compatibility with Ascoos OS modules and execution layers.

Macro Engine: AI methods can be invoked directly from macro scripts, enabling autonomous decision-making and adaptive workflows.
Semantic Profiler: AI outputs can be semantically scored and interpreted, allowing context-aware execution and feedback loops.
DSL Scripting: Developers can embed AI logic into DSL commands for dynamic behavior and intelligent branching.
Data Modules: AI models can be trained on internal datasets, including user behavior, system logs, and macro history.

This integration ensures that AI is not just a feature — it’s a native capability. Applications built on Ascoos OS can reason, adapt, and evolve without relying on external services.

📚 References

✍️ Written by Christos Drogkidis — Developer, thinker, and advocate of logic-driven software design.