Reasoning processes, methods and systems for use in knowledge-based situation analysis support systems

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Authors
  1. Roy, J.
  2. Auger, A.
Corporate Authors
Defence R&D Canada - Valcartier, Valcartier QUE (CAN)
Abstract
Adopting a knowledge-centric view of situation analysis and information fusion ultimately requires that one cares about knowledge representation and reasoning, i.e., the area of artificial intelligence concerned with how knowledge can be represented symbolically and manipulated in an automated way by programs simulating reasoning. This memorandum reviews reasoning and inference processes, methods and systems. Aspects being discussed include the notion of an inference engine and other related concepts such as logical arguments, inference chains and theorems. Forward chaining and backward chaining are briefly described. Three basic categories of reasoning, i.e., deduction, induction, and abduction are presented, along with other reasoning methods including analogical, generate-and-test, model-based, qualitative, default, and autoepistemic reasoning. Many of the reasoning/inference systems developed and used over the years to achieve automated reasoning in computer systems are discussed. Emphasis is given to logic, rule-based, frame-based, and case-based reasoning systems, as these paradigms and systems are the most relevant to the development of knowledge-based situation analysis support systems. Finally, non-monotonic systems that allow the retraction of facts and truth maintenance systems are presented. The memorandum provides a comprehensive description of reasoning, thereby contributing to the development of a foundational R&D framework for two projects at Defence R&D Canad

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Keywords
Knowledge-Based Systems;Knowledge Representation;Reasoning;Inference;Logic;Rule-Based Systems;Frame-Based Systems;Case-Based Reasoning Systems
Report Number
DRDC-VALCARTIER-TM-2006-756 — Technical Memorandum
Date of publication
01 Oct 2008
Number of Pages
66
DSTKIM No
CA031523
CANDIS No
530426
Format(s):
CD ROM

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