In typical use in the Information Technology field however, the phrase is usually reserved for systems that perform more complex kinds of reasoning.
For example, not for systems that do fairly straightforward types of reasoning such as calculating a sales tax or customer discount but making logical inferences about a medical diagnosis or mathematical theorem.
Expert systems focused on much more well defined domains than general problem solving such as medical diagnosis or analyzing faults in an aircraft.
[2] With the rise in popularity of expert systems many new types of automated reasoning were applied to diverse problems in government and industry.
Others such as constraint satisfaction algorithms were also influenced by fields such as decision technology and linear programming.
Also, a completely different approach, one not based on symbolic reasoning but on a connectionist model has also been extremely productive.
These systems typically support a variety of procedural and semi-declarative techniques in order to model different reasoning strategies.
They emphasise pragmatism over formality and may depend on custom extensions and attachments in order to solve real-world problems.
This is important when building situated reasoning agents which must deal with uncertain representations of the world.
These include the use of certainty factors, probabilistic methods such as Bayesian inference or Dempster–Shafer theory, multi-valued ('fuzzy') logic and various connectionist approaches.
[4] This section provides a non-exhaustive and informal categorisation of common types of reasoning system.
For example, they may be used to calculate optimal scheduling, design efficient integrated circuits or maximise productivity in a manufacturing process.
In addition to academic use, typical applications of theorem provers include verification of the correctness of integrated circuits, software programs, engineering designs, etc.
A common approach is to implement production systems to support forward or backward chaining.
Deductive classifiers arose slightly later than rule-based systems and were a component of a new type of artificial intelligence knowledge representation tool known as frame languages.
Unlike object-oriented models however, frame languages have a formal semantics based on first order logic.
Classifiers are an important technology in analyzing the ontologies used to describe models in the Semantic web.
For example, machine learning systems may use inductive reasoning to generate hypotheses for observed facts.
Case-based reasoning uses the top (superficial) levels of similarity; namely, the object, feature, and value criteria.
CBR systems are commonly used in customer/technical support and call centre scenarios and have applications in industrial manufacture, agriculture, medicine, law and many other areas.