Editing Symbolic artificial intelligence (section)

===== Architecture of knowledge-based and expert systems =====

A key component of the system architecture for all expert systems is the knowledge base, which stores facts and rules for problem-solving.{{sfn|Hayes-Roth|Murray|Adelman|2015}}
The simplest approach for an expert system knowledge base is simply a collection or network of [[Production system (computer science)|production rules]]. Production rules connect symbols in a relationship similar to an If-Then statement. The expert system processes the rules to make deductions and to determine what additional information it needs, i.e. what questions to ask, using human-readable symbols. For example, [[OPS5]], [[CLIPS]] and their successors [[Jess (programming language)|Jess]] and [[Drools]] operate in this fashion.

Expert systems can operate in either a [[forward chaining]] – from evidence to conclusions – or [[backward chaining]] – from goals to needed data and prerequisites – manner. More advanced knowledge-based systems, such as [[Soar (cognitive architecture)|Soar]] can also perform meta-level reasoning, that is reasoning about their own reasoning in terms of deciding how to solve problems and monitoring the success of problem-solving strategies.

[[Blackboard system]]s are a second kind of [[knowledge-based system|knowledge-based]] or [[expert system]] architecture. They model a community of experts incrementally contributing, where they can, to solve a problem. The problem is represented in multiple levels of abstraction or alternate views. The experts (knowledge sources) volunteer their services whenever they recognize they can contribute. Potential problem-solving actions are represented on an agenda that is updated as the problem situation changes. A controller decides how useful each contribution is, and who should make the next problem-solving action. One example, the BB1 blackboard architecture<ref name="BB1">{{Cite journal| doi = 10.1016/0004-3702(85)90063-3| volume = 26| issue = 3| pages = 251–321| last = Hayes-Roth| first = Barbara| title = A blackboard architecture for control| journal = Artificial Intelligence| date = 1985}}</ref> was originally inspired by studies of how humans plan to perform multiple tasks in a trip.<ref name="OPM">{{Cite conference| publisher = RAND| last = Hayes-Roth| first = Barbara| title = Human Planning Processes| date = 1980}}</ref> An innovation of BB1 was to apply the same blackboard model to solving its control problem, i.e., its controller performed meta-level reasoning with knowledge sources that monitored how well a plan or the problem-solving was proceeding and could switch from one strategy to another as conditions – such as goals or times – changed. BB1 has been applied in multiple domains: construction site planning, intelligent tutoring systems, and real-time patient monitoring.