Blackboard architectures are used in agent-based AI systems to share information between agents. the blackboard is a gared information structure avaiable to all the agents. Each agent mnitors the blackboard and when it spots soemthing ot knows how towrk with takes the elemnt, processs it and then posts is results back onto the blackbiard in the hope that anoter agent will pick it up.
As an example, consider a maths solving AI system. There is one agent (call it A) that knows about basic adding up, another (call it B) that knows how group things into brackets, one (call it S) that can swop sides, M that ca multiple/divide sides by the sam thing, and others still for other kinds of manipulation. Initially the blackbaord has the equation "3x = x+6". When agent A looks at it, it can only do number arithmetic, similarly B can do nothing, but agent S can process it and posst back "3x – x = 6". At this point B kicks in and posts back "(3–1)x = 6". A now has some basic aritjmatic so posts back "2x = 6", M sees it and posts "x = 6/2", finally agent A posts "x = 3", the solution.
In practice agent archotectures will have various machnaicms to make this process more efficient such as ways for an agent to register patterns that are of interest to avoid constant polling of the blackboard; there may also be some sort of coordinating agent, that helps to prioritise the agents.
Used on pages 374, 376