Reasoning System

AKA: Reasoning Engine.
Context:
- It can (typically) implement
- It can range from being a Human Reasoner to being an Automated Reasoner (reasoning agent).
- It can range from being a Common Sense Reasoner to being a Domain-Specific Reasoner.
- It can have a Reasoning Performance Level (reasoning capability).
- ...
Example(s):
- Jena System.
- Pellet System (pellet.owldl.com e.g. v1.5.0)
- OWLIM System (www.ontotext.com e.g. v2.9.0)
- DENDRAL System ~1980
- MYCIN System ~1975
- LUNAR System ~1975
- ...
- an LLM-based Reasoning System.
- ...
Counter-Example(s):
- a Discovery System.
See: AI System, Machine Learning System, Expert System, Internally Represented Concept.

References

http://en.wikipedia.org/wiki/Reasoning_system
- In information technology a reasoning system is any software application, hardware device or combination of software and hardware whose computational function is to generate conclusions from available knowledge using logical techniques of deduction, induction or other forms of reasoning. Reasoning systems are a subset of a broader category of intelligent systems. They play an important role in the practical implementation knowledge engineering and artificial intelligence.
  A reasoning system manipulates previously acquired knowledge in order to generate new knowledge. Knowledge is typically represented symbolically as informational facts and propositional statements that capture assertions, assumptions, beliefs and other premises. Sub-symbolic (connectionist) knowledge representations may also be used (e.g., trained neural nets). Reasoning systems automate the process of inferring or otherwise deriving new knowledge via the application of logic. In a concrete implementation, reasoning systems may support procedural attachments and built-in actions to process or apply knowledge within some given domain or situation.
  Reasoning systems have a wide field of application that includes scheduling, business rule processing, problem solving, complex event processing, intrusion detection, predictive analytics, robotics, computer vision, and natural language processing.

Saul Amarel. (?). “On Representations of Problems of Reasoning About Actions."
- QUOTE: Many problems of practical importance are problems of reasoning about actions. In these problems, a course of action has to be found that satisfies a number of specified conditions. Everyday examples include planning an airplane trip, organizing a dinner party, etc. ... A problem of reasoning about actions is given in terms of an initial situation, a terminal situation, a set of feasible actions, and a set of constraints... The task of a problem solver is to find the best sequence of permissable actions that can transform the initial situation into the terminal situation.

(Barr & Feigenbaum, 1981) ⇒ Avron Barr, and Edward A. Feigenbaum. (1981). “The Handbook of Artificial Intelligence, Volume 1.” Kaufman
- QUOTE: When the system is required to do something that it has not been explicitly told how to do, it must reason - it must figure out what it needs to know from what it already knows. For instance, suppose an information retrieval program 'knows' only that Robins are birds and that All birds have wings. Keep in mind that for a system to know these facts means only that it contains data structures and procedures that would allow it to answer the questions: Are Robins birds? ⇒ Yes. Do all birds have wings? ⇒ Yes. If we then ask it, Do robins have wings? the program must reason to answer the query. In problems of any complexity, the ability to do this becomes increasingly important. The system must be able to deduce and verify a multitude of new facts beyond those it has been told explicitly.