Blackboard System An AI Approach to Complex Problem Solving

A blackboard system is a specialized artificial intelligence approach based on the blackboard architectural model. It is designed to solve complex, ill-defined problems where the final solution is the cumulative result of various partial solutions. In this model, a common knowledge base—referred to as the "blackboard"—is iteratively updated by a diverse group of specialist knowledge sources, moving from an initial problem specification toward a final solution.

The Blackboard Metaphor

To understand how a blackboard system functions, imagine a group of specialists seated in a room with a large blackboard. These experts work as a team to brainstorm a solution, using the board as a shared workspace for cooperative development.

• Initiation: The process begins when the problem specifications are written on the blackboard.
• Observation: Specialists monitor the board, waiting for an opportunity to apply their specific expertise to the evolving solution.
• Contribution: When one specialist writes a piece of information that aligns with another's expertise, the second specialist adds their contribution.
• Iteration: This cycle of adding contributions continues until the problem is solved.

Core Components of a Blackboard System

A functional blackboard-system application consists of three primary components:

1. Knowledge Sources (KSs)

These are software specialist modules. Much like human experts, each knowledge source provides the specific expertise required by the application to solve a particular part of the problem.

2. The Blackboard

The blackboard serves as a shared repository for problems, partial solutions, suggestions, and contributed information. It acts as a dynamic "library" where contributions are published by various knowledge sources for others to utilize.

3. The Control Shell

The control shell manages the flow of problem-solving activity. Because multiple knowledge sources might attempt to update the blackboard simultaneously, the control shell acts as a moderator to organize their use in the most effective and coherent fashion.

Learnable Task Modeling Language (LTML)

In multi-agent systems, the blackboard serves as the central communication platform for agents to describe the world. To implement this in software, a machine-readable notation is required. While SQL databases are one option, the Learnable Task Modeling Language (LTML) provides a more specialized approach.

Developed in 2007 as part of the POIROT (Plan Order Induction by Reasoning from One Trial) project, LTML is a planning language similar to PDDL but with added control structures and OWL-S models. It is particularly useful for process mining and creating semantic web services.

Example: In a computer game, a user's interactions are logged as a plan trace. This log is transformed into LTML syntax and placed on the blackboard, where software agents can then parse the data to analyze or automate the workflow.

Implementations and Evolution

Early implementations of this architecture include BB1 and GBB. The BB1 architecture was inspired by human task planning, which researchers found to be an opportunistic process rather than a strictly top-down one.

BB1's key innovation was applying this opportunistic model to its own control mechanism. By using meta-level reasoning, BB1 could monitor its own progress; if the problem-solving process stalled, it could switch strategies based on the goals or remaining time. BB1 has been successfully applied in diverse fields, including:

• Construction site planning
• Inferring 3-D protein structures from X-ray crystallography
• Intelligent tutoring systems
• Real-time patient monitoring