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Relevant bibliographies by topics / On inductive reasoning

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Academic literature on the topic 'On inductive reasoning'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "On inductive reasoning"

1

Henderson, Peter B. "Inductive reasoning." ACM SIGCSE Bulletin 35, no. 2 (2003): 27–29. http://dx.doi.org/10.1145/782941.782969.

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Hayes, Brett K., Evan Heit, and Haruka Swendsen. "Inductive reasoning." Wiley Interdisciplinary Reviews: Cognitive Science 1, no. 2 (2010): 278–92. http://dx.doi.org/10.1002/wcs.44.

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Toliver, Jean C. "Inductive Reasoning." Clinical Nurse Specialist 2, no. 4 (1988): 165–78. http://dx.doi.org/10.1097/00002800-198800240-00005.

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Hayes, Brett K., and Evan Heit. "Inductive reasoning 2.0." Wiley Interdisciplinary Reviews: Cognitive Science 9, no. 3 (2017): e1459. http://dx.doi.org/10.1002/wcs.1459.

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Nedeljković, Mitar. "The problem of justifying inductive reasoning." Zbornik radova Filozofskog fakulteta u Pristini 51, no. 2 (2021): 387–408. http://dx.doi.org/10.5937/zrffp51-30620.

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In this paper, the author considers the classical strategies of defense from Hume's argument against induction, and assesses the extent to which they were found to be successful. Synthetic, linguistic, a priori, pragmatic, and inductive strategies of defending induction are considered, as well as the question of the extent to which the justification of induction is a problem for grounding scientific knowledge. A new argument is introduced for the a priori justification of induction, as well as a critique of the synthetic and inductive defenses of induction by Black and Jacquette.

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Hanke, Miroslav. "Late Scholastic Analyses of Inductive Reasoning." Studia Neoaristotelica 17, no. 1 (2020): 35–66. http://dx.doi.org/10.5840/studneoar20201712.

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The late scholastic era was, among others, contemporary to the “emergence of probability”, the German academic philosophy from Leibniz to Kant, and the introduction of Newtonian physics. Within this era, two branches of the late-scholastic analysis of induction can be identified, one which can be thought of as a continual development of earlier scholastic approaches, while the other one absorbed influences of early modern philosophy, mathematics, and physics. Both branches of scholastic philosophy share the terminology of modalities, probability, and forms of (inductive) arguments. Furthermore, induction was commonly considered valid as a result of being a covert syllogism. Last but not least, there appears to be a difference in emphasis between the two traditions’ analyses of induction: while Tolomei discussed the theological presuppositions of induction, Amort’s “leges contingentium” exemplify the principles of induction by aleatory phenomena and Boscovich’s rules for inductive arguments are predominately concerned with the generalisation of macro-level observations to the micro-level.

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Heit, Evan, and Caren M. Rotello. "Relations between inductive reasoning and deductive reasoning." Journal of Experimental Psychology: Learning, Memory, and Cognition 36, no. 3 (2010): 805–12. http://dx.doi.org/10.1037/a0018784.

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Newstead, Stephen E. "Inductive reasoning, deductive reasoning and mental models." International Studies in the Philosophy of Science 8, no. 1 (1994): 65–67. http://dx.doi.org/10.1080/02698599408573483.

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Sosa-Moguel, Landy Elena, and Eddie Aparicio-Landa. "SECONDARY SCHOOL MATHEMATICS TEACHERS’ PERCEPTIONS ABOUT INDUCTIVE REASONING AND THEIR INTERPRETATION IN TEACHING." Journal on Mathematics Education 12, no. 2 (2021): 239–56. http://dx.doi.org/10.22342/jme.12.2.12863.239-256.

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Inductive reasoning is an essential tool for teaching mathematics to generate knowledge, solve problems, and make generalizations. However, little research has been done on inductive reasoning as it applies to teaching mathematical concepts in secondary school. Therefore, the study explores secondary school teachers’ perceptions of inductive reasoning and interprets this mathematical reasoning type in teaching the quadratic equation. The data were collected from a questionnaire administered to 22 teachers and an interview conducted to expand their answers. Through the thematic analysis method, it was found that more than half the teachers perceived inductive reasoning as a process for moving from the particular to the general and as a way to acquire mathematical knowledge through questioning. Because teachers have little clarity about inductive phases and processes, they expressed confusion about teaching the quadratic equation inductively. Results indicate that secondary school teachers need professional learning experiences geared towards using inductive reasoning processes and tasks to form concepts and generalizations in mathematics.

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Heit, Evan. "Properties of inductive reasoning." Psychonomic Bulletin & Review 7, no. 4 (2000): 569–92. http://dx.doi.org/10.3758/bf03212996.

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Dissertations / Theses on the topic "On inductive reasoning"

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Hill, Alexandra. "Reasoning by analogy in inductive logic." Thesis, University of Manchester, 2013. https://www.research.manchester.ac.uk/portal/en/theses/reasoning-by-analogy-in-inductive-logic(039622d8-ab3f-418f-b46c-4d4e7a9eb6c1).html.

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This thesis investigates ways of incorporating reasoning by analogy into Pure (Unary) Inductive Logic. We start with an analysis of similarity as distance, noting that this is the conception that has received most attention in the literature so far. Chapter 4 looks in some detail at the consequences of adopting Hamming Distance as our measure of similarity, which proves to be a strong requirement. Chapter 5 then examines various adaptations of Hamming Distance and proposes a subtle modification, further-away-ness, that generates a much larger class of solutions.

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Baker, Ian Walter Shelley. "Inductive reasoning in persecutory delusional thought." Thesis, University of Plymouth, 1997. http://hdl.handle.net/10026.1/2404.

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Delusions are considered to be one of the primary symptoms of psychosis but until recently have received little empirical investigation. One approach has been to examine the extent to which deluded individuals demonstrate cognitive biases which are different from those of normal controls in inductive reasoning tasks. In this study two hypothesis testing tasks were used to investigate cognitive biases in a group of people with persecutory delusions compared to a group whose delusions had remitted and a normal control group. Participants completed two tasks consisting of a series of visual discrimination problems in which they had to choose between pairs of stimuli presented on cards. Condition 1 examined previously reported biases of deluded participants requiring less information before making judgements and being overconfident in their judgements. Positive or negative feedback was given after every card and participants were unconstrained in giving solutions. Condition 2 partially replicated Young and Bentall's (1995) hypothesis testing study and examined participants' ability to process information sequentially and progressively focus down the set of possible correct solutions. Feedback was restricted and participant responding was constrained. No differences were found between groups in condition 1. In condition 2 deluded participants produced fewer hypothesis and sampled from a smaller range of hypotheses than remitted and control participants. Deluded participants also produced fewer correct hypotheses than the other groups. A trend was found for deluded participants to use fewest sensible responses to feedback, followed by remitted and control groups. The reverse trend was found for use of nonsensical responses to feedback. Limitations of the study, implications for clinical practice and suggestions for future research are considered.

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Crisp-Bright, Aimee Kay. "Knowledge selection in category-based inductive reasoning." Thesis, Durham University, 2010. http://etheses.dur.ac.uk/545/.

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Current theories of category-based inductive reasoning can be distinguished by the emphasis they place on structured and unstructured knowledge. Theories which draw on unstructured knowledge focus on associative strength, or temporal and spatial contiguity between categories. In contrast, accounts which draw on structured knowledge make reference to the underlying theoretical frameworks which relate categories to one another, such as causal or taxonomic relationships. In this thesis, it is argued that this apparent dichotomy can be resolved if one ascribes different processing characteristics to these two types of knowledge. That is, unstructured knowledge influences inductive reasoning effortlessly and relatively automatically, whereas the use of structured knowledge requires effort and the availability of cognitive resources. Understanding these diverging processes illuminates how background knowledge is selected during the inference process. The thesis demonstrates that structured and unstructured knowledge are dissociable and influence reasoning in line with their unique processing characteristics. Using secondary task and speeded response paradigms, it shows that unstructured knowledge is most influential when people are cognitively burdened or forced to respond fast, whereas they can draw on more elaborate structured knowledge if they are not cognitively compromised. This is especially evident for the causal asymmetry effect, in which people make stronger inferences from cause to effect categories, than vice versa. This Bayesian normative effect disappears when people have to contend with a secondary task or respond under time pressure. The next experiments demonstrate that this dissociation between structured and unstructured knowledge is also evident for a more naturalistic inductive reasoning paradigm in which people generate their own inferences. In the final experiments, it is shown how the selection of appropriate knowledge ties in with more domain-general processes, and especially inhibitory control. When responses based on structured and unstructured knowledge conflict, people’s ability to reason based on appropriate structured knowledge depends upon having relevant background knowledge and on their ability to inhibit the lure from inappropriate unstructured knowledge. The thesis concludes with a discussion of how the concepts of structured and unstructured knowledge illuminate the processes underlying knowledge selection for category-based inductive reasoning. It also looks at the implications the findings have for different theories of category-based induction, and for our understanding of human reasoning processes more generally.

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Gregory, D. J. "Age-related changes in inductive reasoning processes." Thesis, University of Aberdeen, 1986. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.372614.

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Griffiths, Anthony D. "Inductive generalisation in case-based reasoning systems." Thesis, University of York, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.336844.

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Marenco, Marc. "Inductive reasoning realism and the religious use of language." Thesis, University of Oxford, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.334114.

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Hesketh, Jane Thurmann. "Using middle-out reasoning to guide inductive theorem proving." Thesis, University of Edinburgh, 1992. http://hdl.handle.net/1842/19842.

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Techniques derived from proof theory for logic alone have been insufficient as a basis for efficient, elegant automatic theorem proving. They concentrate on syntax, neglecting both strategy for particular domains and classes of problem, and guidance from modelling human mathematicians. A novel technique suggested by Bundy, developing ideas from Ernst & Newell, is to reason 'middle-out'. Often, the overall structure of a proof may be known, but its details must be fleshed out according to the individual theorem. Conventional research might use heuristic guidance to backtrack over all possibilities. Middle-out reasoning uses variables as place-holders for parameters still to be chosen. These place-holders become instantiated by the requirements of the subsequent proof. Decisions which would multiply the search space are postponed until more information is available. This is an exciting development in search control, making extensive use of strategic guidance and harnessing tools from human reasoning. This thesis reports research on its use for the synthesis of tail recursive functions from corresponding naive functions and for proofs requiring generalisation. It enables the development of a unified framework for generalisation. An existing proof planning and development system based on Martin-Lö Type Theory, Oyster/CLAM, is used as vehicle, augmented by higher order unification.

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Miramontes, Hercog Luis. "Evolutionary and conventional reinforcement learning in multi agent systems for social simulation." Thesis, London South Bank University, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.288112.

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Badger, Julia. "An investigation into children's inductive reasoning strategies : what drives the development of category induction?" Thesis, Aston University, 2011. http://publications.aston.ac.uk/16300/.

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In a series of studies, I investigated the developmental changes in children’s inductive reasoning strategy, methodological manipulations affecting the trajectory, and driving mechanisms behind the development of category induction. I systematically controlled the nature of the stimuli used, and employed a triad paradigm in which perceptual cues were directly pitted against category membership, to explore under which circumstances children used perceptual or category induction. My induction tasks were designed for children aged 3-9 years old using biologically plausible novel items. In Study 1, I tested 264 children. Using a wide age range allowed me to systematically investigate the developmental trajectory of induction. I also created two degrees of perceptual distractor – high and low – and explored whether the degree of perceptual similarity between target and test items altered children’s strategy preference. A further 52 children were tested in Study 2, to examine whether children showing a perceptual-bias were in fact basing their choice on maturation categories. A gradual transition was observed from perceptual to category induction. However, this transition could not be due to the inability to inhibit high perceptual distractors as children of all ages were equally distracted. Children were also not basing their strategy choices on maturation categories. In Study 3, I investigated category structure (featural vs. relational category rules) and domain (natural vs. artefact) on inductive preference. I tested 403 children. Each child was assigned to either the featural or relational condition, and completed both a natural kind and an artefact task. A further 98 children were tested in Study 4, on the effect of using stimuli labels during the tasks. I observed the same gradual transition from perceptual to category induction preference in Studies 3 and 4. This pattern was stable across domains, but children developed a category-bias one year later for relational categories, arguably due to the greater demands on executive function (EF) posed by these stimuli. Children who received labels during the task made significantly more category choices than those who did not receive labels, possibly due to priming effects. Having investigated influences affecting the developmental trajectory, I continued by exploring the driving mechanism behind the development of category induction. In Study 5, I tested 60 children on a battery of EF tasks as well as my induction task. None of the EF tasks were able to predict inductive variance, therefore EF development is unlikely to be the driving factor behind the transition. Finally in Study 6, I divided 252 children into either a comparison group or an intervention group. The intervention group took part in an interactive educational session at Twycross Zoo about animal adaptations. Both groups took part in four induction tasks, two before and two a week after the zoo visits. There was a significant increase in the number of category choices made in the intervention condition after the zoo visit, a result not observed in the comparison condition. This highlights the role of knowledge in supporting the transition from perceptual to category induction. I suggest that EF development may support induction development, but the driving mechanism behind the transition is an accumulation of knowledge, and an appreciation for the importance of category membership.

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Ronel, Tahel. "Symmetry principles in polyadic inductive logic." Thesis, University of Manchester, 2016. https://www.research.manchester.ac.uk/portal/en/theses/symmetry-principles-in-polyadic-inductive-logic(6bd9665b-b236-435c-9aad-7edb3cfc399e).html.

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We investigate principles of rationality based on symmetry in Polyadic Pure Inductive Logic. The aim of Pure Inductive Logic (PIL) is to determine how to assign probabilities to sentences of a language being true in some structure on the basis of rational considerations. This thesis centres on principles arising from instances of symmetry for sentences of first-order polyadic languages. We begin with the recently introduced Permutation Invariance Principle (PIP), and find that it is determined by a finite number of permutations on a finite set of formulae. We test the consistency of PIP with established principles of the subject and show, in particular, that it is consistent with Super Regularity. We then investigate the relationship between PIP and the two main polyadic principles thus far, Spectrum Exchangeability and Language Invariance, and discover there are close connections. In addition, we define the key notion of polyadic atoms as the building blocks of polyadic languages. We explore polyadic generalisations of the unary principle of Atom Exchangeability and prove that PIP is a natural extension of Atom Exchangeability to polyadic languages. In the second half of the thesis we investigate polyadic approaches to the unary version of Constant Exchangeability as invariance under signatures. We first provide a theory built on polyadic atoms (for binary and then general languages). We introduce the notion of a signature for non-unary languages, and principles of invariance under signatures, independence, and instantial relevance for this context, as well as a binary representation theorem. We then develop a second approach to these concepts using elements as alternative building blocks for polyadic languages. Finally, we introduce the concepts of homomorphisms and degenerate probability functions in Pure Inductive Logic. We examine which of the established principles of PIL are preserved by these notions, and present a method for reducing probability functions on general polyadic languages to functions on binary languages.

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Books on the topic "On inductive reasoning"

1

Gustason, William. Reasoning from evidence: Inductive logic. Macmillan, 1994.

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B, Binko James, ed. Inductive reasoning in the secondary classroom. NEA Professional Library, National Education Association, 1992.

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Chiappe, Danilo Luigi. The role of constraints in inductive reasoning. National Library of Canada = Bibliothèque nationale du Canada, 1992.

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International, Workshop AII '86 (1986 Wendisch-Rietz Germany). Analogical and inductive inference: Proceedings. Springer-Verlag, 1987.

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Moore, Kathleen Dean. Patterns of inductive reasoning: Developing critical thinking skills. 4th ed. Kendall/Hunt, 1998.

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Pakistan), Iqbal Academy (Lahore, ред. Istiqrāʼī istadlāl aur fikr-i Iqbāll: Inductive reasoning and Iqbal's thought. Iqbāl Akādamī Pākistān, 2010.

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Levi, Isaac. For the sake of the argument: Ramsey Test conditionals, inductive inference, and nonmonotonic reasoning. Cambridge University Press, 1996.

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Sugden, Robert. Rationality and experience: The role of inductive reasoning in the evolution of conventions. Law and Economics Programme, Faculty of Law, University of Toronto, 1997.

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Klauer, Karl Josef. Cognitive training for children: A developmental program of inductive reasoning and problem solving. Hogrefe & Huber Publishers, 1993.

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P, Jantke K., ed. Analogical and inductive inference: International Workshop AII '92, Dagstuhl Castle, Germany, October 1992 : proceedings. Springer-Verlag, 1992.

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Book chapters on the topic "On inductive reasoning"

1

Li, Ming, and Paul Vitányi. "Inductive Reasoning." In An Introduction to Kolmogorov Complexity and Its Applications. Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4757-2606-0_5.

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Sauce, Bruno, and Louis D. Matzel. "Inductive Reasoning." In Encyclopedia of Animal Cognition and Behavior. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-47829-6_1045-1.

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Carabelli, Anna M. "Inductive Reasoning." In On Keynes’s Method. Palgrave Macmillan UK, 1988. http://dx.doi.org/10.1007/978-1-349-19414-8_4.

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Li, Ming, and Paul Vitanyi. "Inductive reasoning." In Language Computations. American Mathematical Society, 1994. http://dx.doi.org/10.1090/dimacs/017/06.

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Li, Ming, and Paul Vitányi. "Inductive Reasoning." In Texts in Computer Science. Springer New York, 2008. http://dx.doi.org/10.1007/978-0-387-49820-1_5.

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Cellier, François E. "Inductive Reasoning." In Continuous System Modeling. Springer New York, 1991. http://dx.doi.org/10.1007/978-1-4757-3922-0_13.

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Li, Ming, and Paul Vitányi. "Inductive Reasoning." In An Introduction to Kolmogorov Complexity and Its Applications. Springer New York, 1993. http://dx.doi.org/10.1007/978-1-4757-3860-5_5.

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Li, Ming, and Paul Vitányi. "Inductive Reasoning." In Texts in Computer Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11298-1_5.

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Kimmig, Angelika. "Inductive Reasoning." In Encyclopedia of Systems Biology. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_592.

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Day, John Patrick. "Inductive Reasoning and Inductive Logic." In Inductive Probability. Routledge, 2021. http://dx.doi.org/10.4324/9781003244356-7.

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Conference papers on the topic "On inductive reasoning"

1

Hickman, Craig. "Inductive reasoning, 1989." In ACM SIGGRAPH 98 Electronic art and animation catalog. ACM Press, 1998. http://dx.doi.org/10.1145/281388.281638.

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Eusebi, Edward V. "Inductive reasoning from relations." In the international conference. ACM Press, 1987. http://dx.doi.org/10.1145/28315.28363.

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Bagherpour, Solmaz, Àngela Nebot, and Francisco Mugica. "Hierarchical Fuzzy Inductive Reasoning Classifier." In 4th International Conference on Simulation and Modeling Methodologies, Technologies and Applications. SCITEPRESS - Science and Technology Publications, 2014. http://dx.doi.org/10.5220/0005041604340442.

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Filinski, Andrzej, and Kristian Støvring. "Inductive reasoning about effectful data types." In the 2007 ACM SIGPLAN international conference. ACM Press, 2007. http://dx.doi.org/10.1145/1291151.1291168.

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d'Amato, Claudia, Floriana Esposito, Nicola Fanizzi, Bettina Fazzinga, Georg Gottlob, and Thomas Lukasiewicz. "Inductive reasoning and semantic web search." In the 2010 ACM Symposium. ACM Press, 2010. http://dx.doi.org/10.1145/1774088.1774397.

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Bradley, Aaron R. "Incremental, Inductive Model Checking." In 2013 20th International Symposium on Temporal Representation and Reasoning (TIME). IEEE, 2013. http://dx.doi.org/10.1109/time.2013.9.

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Bagherpour, Solmaz, Fransisco Mugica, and Angela Nebot. "A Hierarchical perspective to Fuzzy Inductive Reasoning." In 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2015. http://dx.doi.org/10.1109/fuzz-ieee.2015.7338067.

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Adawiyah, Robiah, Abdul Muin, and Khairunnisa Khairunnisa. "Mathematical Inductive-Creative Reasoning, A Theoretical Study." In International Conference on Mathematics and Science Education. Atlantis Press, 2017. http://dx.doi.org/10.2991/icmsed-16.2017.53.

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Ibáñez-García, Yazmín, Víctor Gutiérrez-Basulto, and Steven Schockaert. "Plausible Reasoning about EL-Ontologies using Concept Interpolation." In 17th International Conference on Principles of Knowledge Representation and Reasoning {KR-2020}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/kr.2020/51.

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Description logics (DLs) are standard knowledge representation languages for modelling ontologies, i.e. knowledge about concepts and the relations between them. Unfortunately, DL ontologies are difficult to learn from data and time-consuming to encode manually. As a result, ontologies for broad domains are almost inevitably incomplete. In recent years, several data-driven approaches have been proposed for automatically extending such ontologies. One family of methods rely on characterizations of concepts that are derived from text descriptions. While such characterizations do not capture ontological knowledge directly, they encode information about the similarity between different concepts that can be exploited for filling in the gaps in existing ontologies. To this end, several inductive inference mechanisms have already been proposed, but these have been defined and used in a heuristic fashion. In this paper, we instead propose an inductive inference mechanism which is based on a clear model-theoretic semantics, and can thus be tightly integrated with standard deductive reasoning. We particularly focus on interpolation, a powerful commonsense reasoning mechanism which is closely related to cognitive models of category-based induction. Apart from the formalization of the underlying semantics, as our main technical contribution we provide computational complexity bounds for reasoning in EL with this interpolation mechanism.

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Garland, S. J., and J. V. Guttag. "Inductive methods for reasoning about abstract data types." In the 15th ACM SIGPLAN-SIGACT symposium. ACM Press, 1988. http://dx.doi.org/10.1145/73560.73579.

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