The blackboard model was originally designed as a way to handle complex, ill-defined problems, where the solution is the sum of its parts.
To realize a blackboard in a computer program, a machine readable notation is needed in which facts can be stored.
The syntax of the LTML planning language is similar to PDDL, but adds extra features like control structures and OWL-S models.
[5][6] LTML was developed in 2007[7] as part of a much larger project called POIROT (Plan Order Induction by Reasoning from One Trial),[8] which is a Learning from demonstrations framework for process mining.
In POIROT, Plan traces and hypotheses are stored in the LTML syntax for creating semantic web services.
Meta-level reasoning with control knowledge sources could then monitor whether planning and problem-solving were proceeding as expected or stalled.
BB1 was applied in multiple domains: construction site planning,[13] inferring 3-D protein structures from X-ray crystallography,[14] intelligent tutoring systems,[15] and real-time patient monitoring.
In these 'Bayesian Blackboard' systems, the heuristics can acquire more rigorous probabilistic meanings as proposal and acceptances in Metropolis Hastings sampling though the space of possible structures.
[24][25][26] Conversely, using these mappings, existing Metropolis-Hastings samplers over structural spaces may now thus be viewed as forms of blackboard systems even when not named as such by the authors.
In this domain, the problem of integrating various AI algorithms into a single intelligent system arises spontaneously, with blackboards providing a way for a collection of distributed, modular natural language processing algorithms to each annotate the data in a central space, without needing to coordinate their behavior.