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Classifying abduction

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  1. Introduction
  2. Aristotle
  3. Induction and deduction
  4. McDermott and Charniak
  5. Artificial Intelligence
  6. Conclusion

Abduction is a syllogism with a certain major premise but a probable minor premise. It involves inference using the available best explanation. The hypotheses generate explanations, conclusions and observations. Broadly speaking, the aim of abduction is to find the causes or explanations of facts or observed phenomenon. In deduction, premises cannot guarantee conclusion unlike in induction where premises guarantee conclusion. In induction, one can believe that conclusion is based on the premise but the truth of the conclusion is not guaranteed. Abduction is a logical fallacy that affirms the consequent. It serves as a hypothesis to explain observations. While there are infinite explanations to the observed phenomena, abduction enables one to form a few or one explanation . Thus, abduction helps to disregard certain possibilities. Consequently, we are able to orient ourselves to the environment better.

Current essay will provide a classification of abduction. The paper will be premised upon Artificial Intelligence (AI) and philosophy. Towards this end, it will provide interesting philosophical debates and arguments. Moreover, to understand abduction, a historical background of this concept will be provided with attribution to certain philosophers' works. The essay will provide a detailed examination coupled with personal ideas and opinions regarding Artificial Intelligence (AI). It will classify abduction as part of Artificial Intelligence (AI) and logic. Lastly, the essay will provide recent arguments regarding this issue.

[...] Abduction facilitates conception of explanatory hypothesis while induction helps in providing a verdict on a hypothesis. According to Pierce, abduction has schematic characterization associated with science. To Frankfurt, abduction involves adopting hypotheses and not necessarily inventing hypotheses. However, abduction can still be considered as integral in the discovery context. The presence of many hypotheses accounting for an assortment of facts does not suffice hypothesis adoption. Thus, abduction is inference to best explanation that evolves from data description to a hypothesis, which accounts or explains the data. [...]

[...] It emphasizes on solving realistic tasks. AI provides solutions through inductive and abductive engines applicable in the provision of domain knowledge and right parameter setting. Applications in AI include automated planning, belief revision, and fault diagnosis. Abduction is used in automatic detection of faulty systems. Abduction in medicine is used in clinical judgment and evaluation. In conclusion, computing machines starting from right premises can reach correct conclusion. Therefore, the machine will fail to infer at times in case of wrong premises. [...]

[...] Philosophy and Artificial Intelligence. Englewood Cliffs, N.J: Prentice Hall Mu?ller, Vincent C. Philosophy and Theory of Artificial Intelligence. Berlin: Springer Ricardo Caferra Logic for Computer Science and Artificial Intelligence (London: ISTE, 2011) Eugene Charniak and Drew V. McDermott, Introduction to?????????????????????????????????????????????? ????????????????????????????????????????????? ???????? Artificial Intelligence (Reading, Mass: Addison-Wesley, 1995) Todd C. Moody, Philosophy and Artificial Intelligence (Englewood Cliffs, N.J: Prentice Hall, 1993) James K. Feibleman, An Introduction to Peirce's Philosophy (New York: Harper & Brothers, 1996) Armin Laux and H. Wansing, Knowledge and Belief in Philosophy and Artificial Intelligence (Berlin: Akademie Verlag, 1995) Anthony Kenny, Philosophy in the Modern World (Oxford: Oxford University Press, 2008) Vincent C. [...]

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