# Symbolic Logical Reasoning Argument

(Redirected from logical reasoning)

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):****Counter(s):****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.

## 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.

- Informally, two kinds of

### 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). ...