Abductive Reasoning Task
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A Abductive Reasoning Task is a non-monotonic reasoning task that generates plausible hypothesis to explain observed phenomenon while allowing conclusions to be revised as new evidence emerges.
- AKA: Abduction, Inference to the Best Explanation, Retroduction, Educated Guessing.
- Context:
- It can typically infer Plausible Hypothesis from observed phenomenon through backward explanation process.
- It can typically generate Best Explanation for unexpected observation through simplicity criterion application.
- It can typically move from Observed Effect to probable cause through explanatory inference.
- It can typically identify Most Likely Explanation for surprising fact through hypothesis evaluation.
- It can typically support Scientific Discovery through hypothesis generation.
- ...
- It can often follow the Formal Structure: given observed C and rule "if A then C", infer A as plausible explanation.
- It can often involve Uncertainty Management through plausibility qualification such as "best available" or "most likely".
- It can often produce Defeasible Conclusion that can be refuted in light of new data.
- It can often contrast with Deductive Reasoning, which moves from premises to guaranteed conclusions rather than from observations to plausible explanations.
- It can often be described as "deduction in reverse" where given a rule "A follows from B" and observed result A, we infer condition B.
- ...
- It can range from being a Logic-based Abductive Reasoning Task to being a Probabilistic Abductive Reasoning Task, depending on its abductive reasoning formalism.
- It can range from being a Simple Abductive Reasoning Task to being a Complex Abductive Reasoning Task, depending on its abductive reasoning hypothesis space size.
- It can range from being a Domain-Specific Abductive Reasoning Task to being a Domain-General Abductive Reasoning Task, depending on its abductive reasoning application scope.
- It can range from being a Fully-Automated Abductive Reasoning Task to being a Human-Guided Abductive Reasoning Task, depending on its abductive reasoning human involvement level.
- It can range from being a Real-Time Abductive Reasoning Task to being a Extended-Duration Abductive Reasoning Task, depending on its abductive reasoning temporal constraint.
- ...
- It can apply Evaluation Criterion such as simplicity, explanatory scope, coherence, and testability for abductive reasoning hypothesis selection.
- It can integrate with Bayesian Framework for abductive reasoning probability calculation.
- It can support Expert System for abductive reasoning diagnostic application.
- It can connect to Logical Programming System for abductive reasoning computational implementation.
- It can combine with Inductive Reasoning for abductive reasoning learning process enhancement.
- ...
- Examples:
- Abductive Reasoning Task Domain Applications, such as:
- Scientific Abductive Reasoning Tasks, such as:
- Everyday Abductive Reasoning Tasks, such as:
- Weather Inference Abductive Reasoning Task for abductive reasoning natural phenomenon explanation (e.g., inferring it rained because the lawn is wet).
- Behavioral Explanation Abductive Reasoning Task for abductive reasoning human action interpretation.
- Artificial Intelligence Abductive Reasoning Tasks, such as:
- Abductive Reasoning Task Computational Approaches, such as:
- Logic-based Abductive Reasoning Tasks, such as:
- Set-Cover Abductive Reasoning Tasks, such as:
- Probabilistic Abductive Reasoning Tasks, such as:
- Historical Abductive Reasoning Task Developments, such as:
- Peirce's Original Abductive Reasoning Task (1901) for abductive reasoning hypothesis generation formalization.
- Modern Inference to the Best Explanation Task (1965) for abductive reasoning explanatory reasoning justification.
- Computational Abductive Reasoning Task (1980s-1990s) for abductive reasoning automated abduction implementation.
- Interdisciplinary Abductive Reasoning Task Applications, such as:
- Legal Abductive Reasoning Tasks for abductive reasoning evidence evaluation.
- Criminal Investigation Abductive Reasoning Tasks for abductive reasoning crime scenario reconstruction.
- Business Intelligence Abductive Reasoning Tasks for abductive reasoning market trend explanation.
- Educational Abductive Reasoning Tasks for abductive reasoning student misconception identification.
- ...
- Abductive Reasoning Task Domain Applications, such as:
- Counter-Examples:
- Deductive Reasoning Task, which moves from premises to conclusions through logical rule application rather than from observations to explanations.
- Inductive Reasoning Task, which generalizes from specific instances to general patterns rather than seeking specific explanations for observations.
- Analogical Reasoning Task, which transfers knowledge from source domain to target domain based on structural similarity rather than inferring best explanation for observations.
- Pure Logical Inference Task, which requires valid premise-conclusion relationship rather than plausibility-based explanation.
- Random Guessing Task, which generates possibilitys without systematic evaluation criteria for explanation selection.
- See: Non-Monotonic Reasoning Task, Explanatory Reasoning Task, Scientific Discovery Task, Hypothesis Generation Task, Diagnostic Reasoning Task, Occam's Razor Application, Inference to the Best Explanation Process, Most Economical Explanation Selection, Bayesian Confirmation Theory Application.
References
2023
- (Zandie et al., 2023) ⇒ Rohola Zandie, Diwanshu Shekhar, and Mohammad Mahoor. (2023). “COGEN: Abductive Commonsense Language Generation.” In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers).
- QUOTE: ... Reasoning is one of the most important elements in achieving Artificial General Intelligence (AGI), specifically when it comes to Abductive and counterfactual reasoning.
- QUOTE: ... The abductive inference could be viewed as going backward from the conclusions of a valid deductive inference to the premises to find its plausible causes and effects. In terms of classical logic, this is a fallacy (Andersen, 1973). Abductive reasoning is defeasible (and also non-monotonic) which means the conclusions can be refuted in the light of new data. Although abductive reasoning forms one of the core abilities of human cognition, its research in the area of NLP is still widely unexplored. ...
2022
- (Wikipedia, 2022) ⇒ https://en.wikipedia.org/wiki/Abductive_reasoning Retrieved:2022-4-19.
- Abductive reasoning (also called abduction,[1] abductive inference,[1] or retroduction ) is a form of logical inference formulated and advanced by American philosopher Charles Sanders Peirce beginning in the last third of the 19th century. It starts with an observation or set of observations and then seeks the simplest and most likely conclusion from the observations. This process, unlike deductive reasoning, yields a plausible conclusion but does not positively verify it. Abductive conclusions are thus qualified as having a remnant of uncertainty or doubt, which is expressed in retreat terms such as "best available" or "most likely". One can understand abductive reasoning as inference to the best explanation, although not all usages of the terms abduction and inference to the best explanation are exactly equivalent. In the 1990s, as computing power grew, the fields of law, [2] computer science, and artificial intelligence research[3] spurred renewed interest in the subject of abduction. Diagnostic expert systems frequently employ abduction. [4]
- ↑ Jump up to: 1.0 1.1 For example:
- ↑ See, e.g. Analysis of Evidence, 2d ed. by Terence Anderson (Cambridge University Press, 2005)
- ↑ For examples, see "Abductive Inference in Reasoning and Perception", John R. Josephson, Laboratory for Artificial Intelligence Research, Ohio State University, and Abduction, Reason, and Science. Processes of Discovery and Explanation by Lorenzo Magnani (Kluwer Academic/Plenum Publishers, New York, 2001).
- ↑ Reggia, James A., et al. “Answer justification in diagnostic expert systems-Part I: Abductive inference and its justification.” IEEE transactions on biomedical engineering 4 (1985): 263-267.
2022
- (Simple Wikipedia, 2022) ⇒ https://simple.wikipedia.org/wiki/Abduction_(logic)
- Abduction is the kind of practical logic which answers questions of the type "how did this come about?". It produces answers which are not guaranteed to be correct. Consider the observation that the lawn is wet in the morning. How did that happen? In London, the answer is most often that it rained. But in Los Angeles it is much more likely that someone left the sprinkler on.
Abduction is logical inference which goes from an observation to a theory which accounts for the observation. It makes the simplest and most likely explanation. In abductive reasoning, unlike in deductive reasoning, the premises do not guarantee the conclusion. Abductive reasoning is "inference to the best explanation".
- Abduction is the kind of practical logic which answers questions of the type "how did this come about?". It produces answers which are not guaranteed to be correct. Consider the observation that the lawn is wet in the morning. How did that happen? In London, the answer is most often that it rained. But in Los Angeles it is much more likely that someone left the sprinkler on.
2017a
- (Stanford Encyclopedia of Philosophy, 2017) ⇒ Stanford Encyclopedia of Philosophy: Abduction http://plato.stanford.edu/entries/abduction/ First published Wed Mar 9, 2011; substantive revision Fri Apr 28, 2017
- QUOTE: In the philosophical literature, the term “abduction” is used in two related but different senses. In both senses, the term refers to some form of explanatory reasoning. However, in the historically first sense, it refers to the place of explanatory reasoning in generating hypotheses, while in the sense in which it is used most frequently in the modern literature it refers to the place of explanatory reasoning in justifying hypotheses. In the latter sense, abduction is also often called “Inference to the Best Explanation.”
This entry is exclusively concerned with abduction in the modern sense, although there is a supplement on abduction in the historical sense, which had its origin in the work of Charles Sanders Peirce — see the Supplement: Peirce on Abduction. See also the entry on scientific discovery, in particular the section on discovery as abduction.
Most philosophers agree that abduction (in the sense of Inference to the Best Explanation) is a type of inference that is frequently employed, in some form or other, both in everyday and in scientific reasoning. However, the exact form as well as the normative status of abduction are still matters of controversy. This entry contrasts abduction with other types of inference; points at prominent uses of it, both in and outside philosophy; considers various more or less precise statements of it; discusses its normative status; and highlights possible connections between abduction and Bayesian confirmation theory.
- QUOTE: In the philosophical literature, the term “abduction” is used in two related but different senses. In both senses, the term refers to some form of explanatory reasoning. However, in the historically first sense, it refers to the place of explanatory reasoning in generating hypotheses, while in the sense in which it is used most frequently in the modern literature it refers to the place of explanatory reasoning in justifying hypotheses. In the latter sense, abduction is also often called “Inference to the Best Explanation.”
2017b
- (Kakas, 2017) ⇒ Antonis C. Kakas. (2017). “Abduction”. In: (Sammut & Webb, 2017) DOI:10.1007/978-1-4899-7687-1_1
- QUOTE: Abduction is a form of reasoning, sometimes described as “deduction in reverse,” whereby given a rule that “A follows from B” and the observed result of “A” we infer the condition “B” of the rule. More generally, given a theory, T, modeling a domain of interest and an observation, “A,” we infer a hypothesis “B” such that the observation follows deductively from T augmented with “B.” We think of “B” as a possible explanation for the observation according to the given theory that contains our rule. This new information and its consequences (or ramifications) according to the given theory can be considered as the result of a (or part of a) learning process based on the given theory and driven by the observations that are explained by abduction. Abduction can be combined with induction in different ways to enhance this learning process.
2001
- (Magnani, 2001) ⇒ Lorenzo Magnani. (2001). “Abduction, Reason, and Science. Processes of Discovery and Explanation.” Springer. ISBN:0306465140
1989
- (Poole, 1989) ⇒ David Poole. (1989). “Explanation and Prediction: an architecture for default and abductive reasoning.” In: Computational Intelligence, 5(2) doi:10.1111/j.1467-8640.1989.tb00319.x
1903
- (Peirce, 1903) ⇒ Charles S. Peirce. (1903), Harvard lectures on pragmatism, Collected Papers v. 5, paragraphs 188–189.
1901
- (Peirce, 1901) ⇒ Charles S. Peirce. (1901). “On the Logic of drawing History from Ancient Documents especially from Testimonies."