Symbolic Logical Reasoning Argument

A Symbolic Logical Reasoning Argument is a reasoning argument that is based a premises and logical consequences

• Context:
  • It can range from being a Simple Logical Argument to being a Complex Logical Argument.
• Example(s):
  • Deductive Reasoning,
  • Inductive Reasoning,
  • Abductive reasoning.
  • …
• Counter(s):
  • Analogical Reasoning;
  • Defeasible Reasoning;
  • Dialectical Reasoning;
  • Logical Fallacy;
  • Moral Argument.
• See: Automated Reasoning, Scientist, Premise, Logical Consequence, Material Conditional, Deductive Reasoning, Mathematical Logic, Philosophical Logic, Inductive Reasoning, Problem of Induction, Science, Abductive Reasoning, Wikt:Cogent.

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References

2018

• (Wikipedia, 2018) ⇒ https://en.wikipedia.org/wiki/Logical_reasoning Retrieved:2018-11-3.
  • Informally, two kinds of logical reasoning can be distinguished in addition to formal deduction: induction and abduction. Given a precondition or premise, a conclusion or logical consequence and a rule or material conditional that implies the conclusion given the precondition, one can explain that:
    • Deductive reasoning determines whether the truth of a conclusion can be determined for that rule, based solely on the truth of the premises. Example: "When it rains, things outside get wet. The grass is outside, therefore: when it rains, the grass gets wet." Mathematical logic and philosophical logic are commonly associated with this type of reasoning.
    • Inductive reasoning attempts to support a determination of the rule. It hypothesizes a rule after numerous examples are taken to be a conclusion that follows from a precondition in terms of such a rule. Example: "The grass got wet numerous times when it rained, therefore: the grass always gets wet when it rains." While they may be persuasive, these arguments are not deductively valid, see the problem of induction. Science is associated with this type of reasoning.
    • Abductive reasoning, a.k.a. inference to the best explanation, selects a cogent set of preconditions. Given a true conclusion and a rule, it attempts to select some possible premises that, if true also, can support the conclusion, though not uniquely. Example: "When it rains, the grass gets wet. The grass is wet. Therefore, it might have rained." This kind of reasoning can be used to develop a hypothesis, which in turn can be tested by additional reasoning or data. Diagnosticians, detectives, and scientists often use this type of reasoning.

2017

• (Das et al., 2017) ⇒ Rajarshi Das, Arvind Neelakantan, David Belanger, and Andrew McCallum. (2017). “Chains of Reasoning over Entities, Relations, and Text Using Recurrent Neural Networks.” In: Proceedings of EACL-2017.
  • QUOTE: Our goal is to combine the rich multistep inference of symbolic logical reasoning with the generalization capabilities of neural networks. We are particularly interested in complex reasoning about entities and relations in text and large-scale knowledge bases (KBs). ...

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