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Types of inference engines in Artificial Intelligence

As Expert Systems evolved many new techniques were incorporated into various types of inference engines. Some of the most important of these were:
  • Truth Maintenance.
  • Hypothetical Reasoning.
  • Fuzzy Logic.
  • Ontology Classification.
Truth Maintenance
  • Truth maintenance systems record the dependencies in a knowledge-base so that when facts are altered dependent knowledge can be altered accordingly.
  • For example, if the system learns that Jon is no longer known to be a man it will revoke the assertion that Jon is mortal.
Hypothetical Reasoning
  • In hypothetical reasoning, the knowledge base can be divided up into many possible views, aka worlds.
  • This allows the inference engine to explore multiple possibilities in parallel.
  • In this simple example, the system may want to explore the consequences of both assertions, what will be true if Jon is a Man and what will be true if he is not?
Fuzzy Logic
  • Once of the first extensions of simply using rules to represent knowledge was also to associate a probability with each rule.
  • So, not to assert that Jon is mortal but to assert Jon may be mortal with some probability value.
  • Simple probabilities were extended in some systems with sophisticated mechanisms for uncertain reasoning and combination of probabilities.
Ontology Classification
  • With the addition of object classes to the knowledge base a new type of reasoning was possible. Rather than reason simply about the values of the objects the system could also reason about the structure of the objects as well.
  • In this simple example Man can represent an object class and R1 can be defined as a rule that defines the class of all men.
  • These types of special purpose inference engines are known as classifiers.Although they were not highly used in expert systems classifiers are very powerful for unstructured volatile domains and are a key technology for the Internet and the emerging.

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