Inductive and Deductive Reasoning

Deductive reasoning can be described as reasoning of the form if A then B. Deduction is in some sense the direct application of knowledge in the production of new knowledge. However, this new knowledge does not represent any new semantic information: the rule represents the knowledge as completely as the added knowledge since any time the assertions (A) are true then the conclusion B is true as well. Purely deductive learning includes methods such as caching, building macro-operators, and explanation-based learning.

In contrast to this, inductive reasoning results in the addition of semantic information. There are a great many ways in which inductive inference has been characterized but most are similar to those specified by the philosopher John Stuart Mill (1843). Basically, in this paradigm, positive instances of some phenomena that have a common trait identify that trait as indicating some larger commonality. Similarly, negative instances that differ for some trait from the positive instances are also indicative of a crucial feature. This methodology is at the center of concept acquisition programs and plays a key role in many AI systems. In general, induction is more difficult than deduction because of both the addition of new semantic information and because the inferred concept may not be the correct one. In induction, assertions do not necessarily lead to true conclusions.

Combinations of inductive and/or deductive reasoning are present in most cognitive architectures that utilize a symbolic world model and are described in the individual architecture document with more specific capabilities such as planning and learning.


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