Knowledge representation techniques from "summary" of Introduction to Logic Programming by Christopher John Hogger

Knowledge representation is an important topic in artificial intelligence that studies how knowledge can be represented and expressed within machines.

Logic programming is one of the well-known methods for representing knowledge in computers and involves encoding facts into logical formulae.

Semantic nets are a graph-based representation where the nodes stand for concepts and links signify the relationships between those concepts.

Symbols associated with a language are the basic elements used to encode knowledge, which provides the machine with ability to reason and interact on its own.

Frames are another principal type of knowledge structure found in Artificial Intelligence applications, where each frame contains attributes associated with the entity being described.

Ontology is an abstraction of reality where all possible terms related to the problem domain are specified so as to provide consistent understanding of the defined aspects.

Rule-based systems use premise-conclusion inference schemes to define how the system should derive new sentences or conclusions based on existing set of facts.

Hierarchical networks are a type of knowledge structure used to store and retrieve information from ontologies, allowing access to the information stored in the nodes.

Predicate logic is typically used to express rules about the world, where the predicates represent case relations between objects.

Scripts are special kind of rules which represent plan of action for typical circumstance. Knowledge encoded using rule-based systems and scripts can adjust behaviour when necessary.