AI System Modeling

This document outlines the modeling of AI systems within the AISpec framework.

Motivation

Understanding how AI systems are defined and structured within the framework is essential for understanding their behavior and functionality. This document will guide developers through the process of defining and modeling AI systems within the AISpec framework.

System Modeling

AISpec utilizes a hierarchical model to represent AI systems. This model is based on the following components:

  • System: The overall AI system. This component encapsulates all other components and represents the complete system being modeled.
  • Agent: An entity within the system that performs actions and interacts with the environment. Agents can represent individual AI components or groups of AI components working together.
  • Task: A specific objective or goal that the agent aims to achieve. Tasks are the building blocks of system functionality.
  • Environment: The context in which the AI system operates. This can include physical environments, datasets, or other systems.
  • Data: The information used by the agents to perform tasks and make decisions.
  • Model: The computational representation of the agent’s knowledge and reasoning. This can include machine learning models, rule-based systems, or other types of knowledge representations.

Modeling Options

The AISpec framework provides several options for modeling AI systems:

1. Text-based Description:

  • This approach allows for a natural language description of the system, its components, and their interactions.
  • This option is suitable for initial system design and high-level descriptions.
  • Examples:
    • system: "Image Classification System"
                agent: "Image Classifier"
                task: "Classify images into different categories"
                environment: "Dataset of images"
                data: "Image data"
                model: "Convolutional Neural Network"
                
  • Reference: aispec/models/aispec.py

2. YAML-based Configuration:

  • This approach leverages YAML to define the system structure and components in a structured and machine-readable format.
  • YAML configurations provide a flexible and extensible method for representing system models.
  • Examples:
    system:
                  name: "Image Classification System"
                  agents:
                      - name: "Image Classifier"
                          tasks:
                              - name: "Classify images"
                                  environment: "Image Dataset"
                                  data: "Image data"
                                  model: 
                                      type: "Convolutional Neural Network"
                                      parameters:
                                          # Model parameters
              
  • Reference: aispec/models/aispec.py

3. Python Code:

  • AISpec supports the use of Python code to define and interact with AI system models.
  • This approach allows for greater flexibility and control over the system’s behavior.
  • Examples:
    from aispec import System, Agent, Task, Environment, Data, Model
              
              system = System(name="Image Classification System")
              classifier = Agent(name="Image Classifier")
              task = Task(name="Classify images", environment="Image Dataset")
              system.add_agent(classifier)
              classifier.add_task(task)
              
  • Reference: aispec/models/aispec.py

4. Integration with External Tools:

  • AISpec allows integration with external tools and frameworks for defining AI systems.
  • This enables the use of specialized tools for modeling and analysis.
  • Examples:
    • Integrating with MLflow for tracking and managing machine learning experiments.
    • Using Docker for containerization and deployment of AI systems.
    • Reference: aispec/models/aispec.py

Conclusion

This document provides a comprehensive overview of AI System Modeling within the AISpec framework. Developers can leverage the available modeling options to define and represent their AI systems effectively. As the project evolves, AISpec aims to support a wider range of modeling approaches and integrate with external tools for enhanced functionality.