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Title:
Contextual Abductive Reasoning with Side-Effects
Publication date:
July 2014
Citation:
carse
Abstract:
The belief bias effect is a phenomenon which occurs when we think that we judge an argument based on our reasoning, but are actually influenced by our beliefs and prior knowledge. We discuss one specific case which is commonly assumed to be believable but which is actually not logically valid. By introducing abnormalities, abduction and background knowledge, we adequately model this case under the weak completion semantics. Our formalization reveals new questions about possible extensions in abductive reasoning. For instance, observations and their explanations might include some relevant prior abductive contextual information concerning some side-effect or leading to a contestable or refutable side-effect. Though motivated with and exemplified by the running psychology application, the various new general abductive context definitions are introduced here and given a declarative semantics for the first time, and have a much wider scope of application.
Journal
Authors:
Luís Moniz Pereira
, Emmanuelle-Anna Dietz, Steffen Hölldobler
Journal:
Theory and Practice of Logic Programming
Publisher:
Cambridge University Press
Address:
Cambridge, UK
Volume:
14
Number:
4-5
Pages:
633-648
ISBN:
-
ISSN:
1471-0684
Note:
doi: 10.1017/S1471068414000258 http://arxiv.org/abs/1405.3713
Url address:
http://centria.di.fct.unl.pt/~lmp/publications/online-papers/Contextual_Abductive.pdf
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Plain text:
Luís Moniz Pereira and Emmanuelle-Anna Dietz and Steffen Hölldobler, Contextual Abductive Reasoning with Side-Effects, Theory and Practice of Logic Programming, Vol. 14, No. 4-5, Pag. 633-648, Cambridge University Press, Cambridge, UK, ISSN 1471-0684, <i>doi: 10.1017/S1471068414000258 http://arxiv.org/abs/1405.3713</i>, (http://centria.di.fct.unl.pt/~lmp/publications/online-papers/Contextual_Abductive.pdf), July 2014.
HTML:
<b><a href="/people/members/view.php?code=6175f826202ff877fba2ad77784cb9cb" class="author">Luís Moniz Pereira</a>, Emmanuelle-Anna Dietz and Steffen Hölldobler</b>, <u>Contextual Abductive Reasoning with Side-Effects</u>, Theory and Practice of Logic Programming, Vol. 14, No. 4-5, Pag. 633-648, Cambridge University Press, Cambridge, UK, ISSN 1471-0684, <i>doi: 10.1017/S1471068414000258 http://arxiv.org/abs/1405.3713</i>, (<a href="http://centria.di.fct.unl.pt/~lmp/publications/online-papers/Contextual_Abductive.pdf" target="_blank">url</a>), July 2014.
BibTeX:
@article {carse, author = {Lu\'{\i}s Moniz Pereira and Emmanuelle-Anna Dietz and Steffen H\"olldobler}, title = {Contextual Abductive Reasoning with Side-Effects}, journal = {Theory and Practice of Logic Programming}, publisher = {Cambridge University Press}, address = {Cambridge, UK}, volume = {14}, number = {4-5}, pages = {633-648}, issn = {1471-0684}, note = {doi: 10.1017/S1471068414000258 http://arxiv.org/abs/1405.3713}, url = {http://centria.di.fct.unl.pt/~lmp/publications/online-papers/Contextual_Abductive.pdf}, abstract = {The belief bias effect is a phenomenon which occurs when we think that we judge an argument based on our reasoning, but are actually influenced by our beliefs and prior knowledge. We discuss one specific case which is commonly assumed to be believable but which is actually not logically valid. By introducing abnormalities, abduction and background knowledge, we adequately model this case under the weak completion semantics. Our formalization reveals new questions about possible extensions in abductive reasoning. For instance, observations and their explanations might include some relevant prior abductive contextual information concerning some side-effect or leading to a contestable or refutable side-effect. Though motivated with and exemplified by the running psychology application, the various new general abductive context definitions are introduced here and given a declarative semantics for the first time, and have a much wider scope of application.}, keywords = {Abductive Reasoning; Contextual Reasoning; Side-effects; Human Reasoning; Belief-Bias; Inspection Points; Three-valued}, month = {July}, year = {2014}, }
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