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Knowledge Representation Techniques

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Semantic Networks

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A graphical representation of knowledge based on nodes representing concepts and edges representing relationships. Example: A node for 'Bird' with an edge labeled 'is-a' connected to another node for 'Animal'.

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Frames

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Data structures representing stereotypical situations with slots to fill in properties or attributes. Example: A frame for 'House' with slots for 'color', 'size', and 'number of rooms'.

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Production Rules

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Condition-action pairs used in rule-based systems. Example: IF 'traffic light is red' THEN 'stop the car'.

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Ontologies

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A formal representation of a set of concepts within a domain and the relationships between them. Example: Concepts like 'Person', 'Company', and 'Job' with relationships like 'works at' and 'owns'.

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Description Logics

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A family of formal knowledge representation languages used for ontologies. Example: Using concepts, roles, and individuals to define a 'Person' who 'hasChild' some 'Individual'.

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Belief Networks

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Also known as Bayesian Networks, they are graphical models representing probabilistic relationships among variables. Example: A network representing the probability of 'Rain' given the presence of 'Clouds'.

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Fuzzy Logic

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A form of logic where truth values can range between 0 and 1, representing uncertainty. Example: 'The weather is warm' evaluated to a truth value of 0.7.

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Rule-Based Systems

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Systems that apply rules to a knowledge base to infer new information or make decisions. Example: A medical diagnosis system that uses rules like, IF 'symptom A' and 'symptom B' THEN 'disease X'.

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Case-Based Reasoning

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A problem-solving technique that reuses past experiences to find solutions to new problems. Example: Diagnosing a patient based on similar previous cases.

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Default Reasoning

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A strategy to deal with uncertainty by making presumptions when no information to the contrary is known. Example: Assuming a bird flies unless it is known that it does not.

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Genetic Algorithms

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Search algorithms based on the mechanics of natural selection and genetics. Example: Optimizing a function by evolving a population of candidate solutions.

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Neural Networks

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Computational models inspired by the human brain that are used for pattern recognition. Example: A multi-layer network trained to recognize handwritten digits.

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