Blackboard

The Blackboard pattern provides a central repository (the Blackboard) of information that multiple, independent Knowledge Sources can access, modify, and react to. A Controller selects which Knowledge Sources are relevant at any given time based on the current state of the Blackboard, and applies their expertise to solve a complex problem. This allows for a flexible and extensible system when the problem-solving strategy isn’t known in advance or changes frequently.

It’s particularly useful in domains like artificial intelligence, speech recognition, and expert systems where a variety of specialized components need to collaborate to achieve a single goal. The Blackboard decouples the problem-solving logic from the individual knowledge sources, making it easier to add, remove, or modify components without disrupting the entire system.

Usage

The Blackboard pattern is commonly used in:

• AI and Expert Systems: For tasks like image recognition, natural language processing, and automated reasoning, where different sources of knowledge (e.g., edge detection, grammar rules, inference engines) contribute to a final solution.
• Speech Recognition Systems: Different modules for acoustic modeling, phoneme recognition, and language processing contribute to recognizing spoken words.
• Complex Data Processing Pipelines: Where multiple stages of data transformation and analysis need to be applied reactively depending on the data’s contents.
• Robotics: Coordinating actions of different robot components based on sensor input and environmental conditions.
• Game AI: Managing the behavior of multiple game entities, allowing them to react and interact with each other in complex ways.

Examples

• GraalVM’s Truffle Framework: Truffle uses a Blackboard pattern to represent the abstract syntax tree (AST) of code being executed. Different language implementations (knowledge sources) contribute to analyzing and optimizing this AST, and a central interpreter (controller) manages the execution. The AST effectively is the Blackboard.
• OpenCV: OpenCV’s image processing pipeline utilizes a Blackboard approach, although not explicitly named as such. An image (the Blackboard) is passed through a series of filters and algorithms (Knowledge Sources). The output of one filter becomes the input for the next, with a central process orchestrating the pipeline and determining which algorithms to apply based on the image data. For example, object detection might involve edge detection, then shape analysis, then feature matching – each a knowledge source contributing to the overall “understanding” of the image.