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== Reference ==
 
== Reference ==
Diarmuid O’Donoghue and Mark T. Keane: [[A Creative Analogy Machine: Results and Challenges]]. In: [[Computational Creativity 2012 ICCC 2012]].  
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Diarmuid O’Donoghue and Mark T. Keane: [[A Creative Analogy Machine: Results and Challenges]]. In: [[Computational Creativity 2012 ICCC 2012]], 17-24.  
  
 
== DOI ==
 
== DOI ==
Zeile 9: Zeile 9:
 
Are we any closer to creating an autonomous model of
 
Are we any closer to creating an autonomous model of
 
analogical reasoning that can generate new and creative
 
analogical reasoning that can generate new and creative
analogical comparisons? A three-phase model of ana-
+
analogical comparisons? A three-phase model of analogical reasoning is presented that encompasses the
logical reasoning is presented that encompasses the
 
 
phases of retrieval, mapping and inference validation.
 
phases of retrieval, mapping and inference validation.
The model of the retrieval phase maximizes its creativ-
+
The model of the retrieval phase maximizes its creativity by focusing on domain topology, combating the semantic locality suffered by other models. The mapping
ity by focusing on domain topology, combating the se-
 
mantic locality suffered by other models. The mapping
 
 
model builds on a standard model of the mapping
 
model builds on a standard model of the mapping
 
phase, again making use of domain topology. A novel
 
phase, again making use of domain topology. A novel
validation model helps ensure the quality of the infer-
+
validation model helps ensure the quality of the inferences that are accepted by the model. We evaluated the
ences that are accepted by the model. We evaluated the
 
 
ability of our tri-phase model to re-discover several h-
 
ability of our tri-phase model to re-discover several h-
 
creative analogies (Boden, 1992) from a background
 
creative analogies (Boden, 1992) from a background
 
memory containing many potential source domains.
 
memory containing many potential source domains.
The model successfully re-discovered all creative com-
+
The model successfully re-discovered all creative comparisons, even when given problem descriptions that
parisons, even when given problem descriptions that
 
 
more accurately reflect the original problem – rather
 
more accurately reflect the original problem – rather
than the standard (post hoc) representation of the anal-
+
than the standard (post hoc) representation of the analogy. Finally, some remaining challenges for a truly
ogy. Finally, some remaining challenges for a truly
 
 
autonomous creative analogy machine are assessed.
 
autonomous creative analogy machine are assessed.
  
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== Bibtex ==  
 
== Bibtex ==  
 +
@inproceedings{
 +
author = {Diarmuid O’Donoghue and Mark T. Keane},
 +
title = {A Creative Analogy Machine: Results and Challenges},
 +
editor = {Mary Lou Maher, Kristian Hammond, Alison Pease, Rafael Pérez y Pérez, Dan Ventura and Geraint Wiggins},
 +
booktitle = {Proceedings of the Third International Conference on Computational Creativity},
 +
series = {ICCC2012},
 +
year = {2012},
 +
month = {May},
 +
location = {Dublin, Ireland},
 +
pages = {17-24},
 +
url = {http://computationalcreativity.net/iccc2012/wp-content/uploads/2012/05/017-ODonoghue.pdf, http://de.evo-art.org/index.php?title=A_Creative_Analogy_Machine:_Results_and_Challenges },
 +
publisher = {International Association for Computational Creativity},
 +
keywords = {computational, creativity},
 +
}
  
 
== Used References ==
 
== Used References ==

Aktuelle Version vom 13. November 2015, 13:54 Uhr


Reference

Diarmuid O’Donoghue and Mark T. Keane: A Creative Analogy Machine: Results and Challenges. In: Computational Creativity 2012 ICCC 2012, 17-24.

DOI

Abstract

Are we any closer to creating an autonomous model of analogical reasoning that can generate new and creative analogical comparisons? A three-phase model of analogical reasoning is presented that encompasses the phases of retrieval, mapping and inference validation. The model of the retrieval phase maximizes its creativity by focusing on domain topology, combating the semantic locality suffered by other models. The mapping model builds on a standard model of the mapping phase, again making use of domain topology. A novel validation model helps ensure the quality of the inferences that are accepted by the model. We evaluated the ability of our tri-phase model to re-discover several h- creative analogies (Boden, 1992) from a background memory containing many potential source domains. The model successfully re-discovered all creative comparisons, even when given problem descriptions that more accurately reflect the original problem – rather than the standard (post hoc) representation of the analogy. Finally, some remaining challenges for a truly autonomous creative analogy machine are assessed.

Extended Abstract

Bibtex

@inproceedings{
author = {Diarmuid O’Donoghue and Mark T. Keane},
title = {A Creative Analogy Machine: Results and Challenges},
editor = {Mary Lou Maher, Kristian Hammond, Alison Pease, Rafael Pérez y Pérez, Dan Ventura and Geraint Wiggins},
booktitle = {Proceedings of the Third International Conference on Computational Creativity},
series = {ICCC2012},
year = {2012},
month = {May},
location = {Dublin, Ireland},
pages = {17-24},
url = {http://computationalcreativity.net/iccc2012/wp-content/uploads/2012/05/017-ODonoghue.pdf, http://de.evo-art.org/index.php?title=A_Creative_Analogy_Machine:_Results_and_Challenges },
publisher = {International Association for Computational Creativity},
keywords = {computational, creativity},
}

Used References

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Brown, T.L. 2003. Making Truth: Metaphor in Science. University of Illinois Press, New York, USA.

Clement, J. 2008. Creative Model Construction in Scien- tists and Students: The Role of Imagery, Analogy, and Mental Simulation. Dordrecht: Springer.

Davies, J. Goel, A.K. Yaner P.W. 2008. Pro- teus:Visuospatial analogy in problem-solving, Knowledge- Based Systems 21(7): 636-654.

Dunbar, K. and Blanchette, I. 2001. The in vivo/in vitro approach to cognition: The case of analogy, Trends in Cognitive Sciences, 5(8): 334–339.

Falkenhainer, B. 1990 A Unified Approach to Explanation and Theory Formation, in Shrager, J. & Langley, P. (eds.) Computational Models of Scientific Discovery and Theory Formation, Morgan Kaufman: CA. pp 157-196.

Forbus, K. Gentner, D. Law K. 1995. MAC/FAC: A Model of Similarity-based Retrieval, Cognitive Science, 19(2): 141-205.

Forbus, K. Usher, J. Lovett, A Lockwood, K. Wetzel J. 2011. CogSketch: Sketch Understanding for Cognitive Science Research and for Education, Topics in Cognitive Science, 3(4): 648–666.

Gentner, D. Kurtz, J. 2006. Relations, Objects, and the Composition of Analogies, Cognitive Science, 30(4): 609– 642.

Gentner, D. 1983. Structure-mapping: A theoretical framework for analogy, Cognitive Science, 7(2): 155–170.

Gomes P, Seco N, Pereira FC, Paiva P, Carreiro P, Ferreira JL, Bento C. 2006. The importance of retrieval in creative design analogies, Knowledge-Based Systems, 19(7): 480– 488.

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O'Donoghue, D.P. 2007. Statistical Evaluation of Process- Centric Computational Creativity: 4th International Joint Workshop on Computational Creativity (IJWCC), Gold- smiths, University of London, 17-19 June.

O'Donoghue, D.P, Bohan, A. Keane M.T, 2006. Seeing Things: Inventive Reasoning with Geometric Analogies and Topographic Maps, New Generation Computing, Ohmsha Ltd. and Springer 24(3): 267-288.

O'Donoghue, D.P. Crean, B. 2002. Searching for Seren- dipitous Analogies, ECAI - Workshop on Creative Systems, Lyon, France, 21-26 July.

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