Reasoning Task

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A reasoning task is an information processing task that accepts a question and supporting evidence is is required to provide reasoned argument (that includes a conclusion/inference).



References

2013


  • http://en.wikipedia.org/wiki/Inference#Definition_of_inference
    • The process by which a conclusion is inferred from multiple observations is called inductive reasoning. The conclusion may be correct or incorrect, or correct to within a certain degree of accuracy, or correct in certain situations. Conclusions inferred from multiple observations may be tested by additional observations.

      This definition is disputable (due to its lack of clarity. Ref: Oxford English dictionary: "induction ... 3. Logic the inference of a general law from particular instances.") The definition given thus applies only when the "conclusion" is general.

      1. A conclusion reached on the basis of evidence and reasoning.
      2. The process of reaching such a conclusion: "order, health, and by inference cleanliness".

2012

  1. http://www.thefreedictionary.com/inference
  2. "So We Need Something Else for Reason to Mean", International Journal of Philosophical Studies 8: 3, 271 — 295.
  3. Alasdair MacIntyre, Dependent Rational Animals: Why Human Beings Need the Virtues, Peru, Illinois: 2002.
  4. Merriam-Webster Dictionary definition of intuitive reason

2009

  • http://en.wiktionary.org/wiki/Reasoning
    • S: (n) reasoning, logical thinking, abstract thought (thinking that is coherent and logical)
    • S: (v) reason, reason out, conclude (decide by reasoning; draw or come to a conclusion) "We reasoned that it was cheaper to rent than to buy a house"
    • S: (v) argue, reason (present reasons and arguments)
    • S: (v) reason (think logically) "The children must learn to reason"
    • S: (adj) intelligent, reasoning, thinking (endowed with the capacity to reason)
  • http://en.wiktionary.org/wiki/reasoning
    • Noun
      • 1. Action of the verb to reason.
      • 2. The deduction of inferences or interpretations from premises; abstract thought; ratiocination.
    • Verb
      • 1. Present participle of reason.


  • (Wikipedia, 2009) ⇒ http://en.wikipedia.org/wiki/Reasoning
    • Reasoning is the cognitive process of looking for reasons for beliefs, conclusions, actions or feelings. [1]
    • Humans have the ability to engage in reasoning about their own reasoning. Different forms of such reflection on reasoning occur in different fields. In philosophy, the study of reasoning typically focuses on what makes reasoning efficient or inefficient, appropriate or inappropriate, good or bad. Philosophers do this by either examining the form or structure of the reasoning within arguments, or by considering the broader methods used to reach particular goals of reasoning. Psychologists and cognitive scientists, in contrast, tend to study how people reason, which cognitive and neural processes are engaged, how cultural factors affect the inferences people draw. The properties of logics which may be used to reason are studied in mathematical logic. The field of automated reasoning studies how reasoning may be modelled computationally. Lawyers also study reasoning.



  • http://clopinet.com/isabelle/Projects/ETH/Exam_Questions.html
    • Inference refers to the ability of a learning system, namely going from the "particular" (the examples) to the "general" (the predictive model). In the best of all worlds, we would not need to worry about model selection. Inference would be performed in a single step: we input training examples into a big black box containing all models, hyper-parameters, and parameters; outcomes the best possible trained model. In practice, we often use 2 levels of inference: we split the training data into a training set and a validation set. The training set serves the trains at the lower level (adjust the parameters of each model); the validation set serves to train at the higher level (select the model.) Nothing prevents us for using more than 2 levels. However, the price to pay will be to get smaller data sets to train with at each level.