In software engineering, the blackboard pattern is a behavioral design pattern^[1] that provides a computational framework for the design and implementation of systems that integrate large and diverse specialized modules, and implement complex, non-deterministic control strategies.^[2][1]

This pattern was identified by the members of the Hearsay-II project and first applied to speech recognition.^[2]

The blackboard model defines three main components:

• blackboard—a structured global memory containing objects from the solution space
• knowledge sources—specialized modules with their own representation
• control component—selects, configures and executes modules.^[2]

The first step is to design the solution space (i.e. potential solutions) that leads to the blackboard structure. Then, knowledge sources are identified. These two activities are closely related.^[2]

The next step is to specify the control component; it generally takes the form of a complex scheduler that makes use of a set of domain-specific heuristics to rate the relevance of executable knowledge sources.^[2]

Usage-domains include:

• speech recognition
• vehicle identification and tracking
• protein structure identification
• sonar signals interpretation.^[2]

The blackboard pattern provides effective solutions for designing and implementing complex systems where heterogeneous modules have to be dynamically combined to solve a problem. This provides non-functional properties such as:

• reusability
• changeability
• robustness.^[2]

The blackboard pattern allows multiple processes to work closer together on separate threads, polling and reacting when necessary.^[1]

• Blackboard system
• Software design pattern