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Ripple-down rules consist of a data structure and knowledge acquisition scenarios. Human experts' knowledge is stored in the data structure. The knowledge is coded as a set of rules. The process of transferring human experts' knowledge to Knowledge-based systems in RDR is explained in knowledge acquisition scenario.
An expert system is an example of a knowledge-based system. Expert systems were the first commercial systems to use a knowledge-based architecture. In general view, an expert system includes the following components: a knowledge base, an inference engine, an explanation facility, a knowledge acquisition facility, and a user interface. [48] [49]
Expert systems gave us the terminology still in use today where AI systems are divided into a knowledge base, which includes facts and rules about a problem domain, and an inference engine, which applies the knowledge in the knowledge base to answer questions and solve problems in the domain. In these early systems the facts in the knowledge ...
Knowledge acquisition is the process used to define the rules and ontologies required for a knowledge-based system. The phrase was first used in conjunction with expert systems to describe the initial tasks associated with developing an expert system, namely finding and interviewing domain experts and capturing their knowledge via rules ...
Here is a small example: A human user is executing a workflow in a computer game. The user presses some buttons and interacts with the game engine. While the user interacts with the game, a plan trace is created. That means the user's actions are stored in a logfile.
The most common decision problems are basic database-query-like questions like instance checking (is a particular instance (member of an ABox) a member of a given concept) and relation checking (does a relation/role hold between two instances, in other words does a have property b), and the more global-database-questions like subsumption (is a ...
Inductive logic programming has adopted several different learning settings, the most common of which are learning from entailment and learning from interpretations. [16] In both cases, the input is provided in the form of background knowledge B, a logical theory (commonly in the form of clauses used in logic programming), as well as positive and negative examples, denoted + and respectively.
Rule-based machine learning approaches include learning classifier systems, [4] association rule learning, [5] artificial immune systems, [6] and any other method that relies on a set of rules, each covering contextual knowledge.