Abstract
Abduction is formally defined as finding the best explanation for a set of observations or inferring cause from effect. Here, we discuss the notion of Occam Abduction, which relates to finding the simplest explanation with respect to inferring cause from effect. Occam abduction is useful in artificial intelligence in application of autonomous reasoning, knowledge assimilation, belief revision, and works well within a multi-agent AI framework. Here we present a flexible, hypothesis-driven methodology for Occam Abduction within a cognitive, artificially intelligent, system architecture.
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Notes
- 1.
If H D contains free variables, ∃ (H D) should be consistent w.r.t. B D.
- 2.
Note that the general set-covering problem is NP-complete.
- 3.
An abducible argument is a first-order argument consisting of both positive and negative instances of abducible predicates. Abducible predicates are those defined by facts only and the inference engine required to interpret the meaning. In formal logic, abducible refers to incomplete or not completely defined predicates. Problem solving is affected by deriving hypotheses on these abducible predicates as solutions to the problem to be solved (observations to be explained).
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Crowder, J.A., Carbone, J., Friess, S. (2020). Artificial Intelligent Inferences Utilizing Occam Abduction. In: Artificial Psychology. Springer, Cham. https://doi.org/10.1007/978-3-030-17081-3_7
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DOI: https://doi.org/10.1007/978-3-030-17081-3_7
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