Towards a computational- and algorithmic-level account of concept blending using analogies and amalgams

ABSTRACT Concept blending – a cognitive process which allows for the combination of certain elements (and their relations) from originally distinct conceptual spaces into a new unified space combining these previously separate elements, and enables reasoning and inference over the combination – is taken as a key element of creative thought and combinatorial creativity. In this article, we summarise our work towards the development of a computational-level and algorithmic-level account of concept blending, combining approaches from computational analogy-making and case-based reasoning (CBR). We present the theoretical background, as well as an algorithmic proposal integrating higher-order anti-unification matching and generalisation from analogy with amalgams from CBR. The feasibility of the approach is then exemplified in two case studies.

[1]  Peter Danielson.  Artificial Intelligence and Natural Man , 1982 .

[2]  Tarek Richard Besold,et al.  Generalize and Blend: Concept Blending Based on Generalization, Analogy, and Amalgams , 2015, ICCC.

[3]  Alison Pease,et al.  Using Argumentation to Evaluate Concept Blends in Combinatorial Creativity , 2015, ICCC.

[4]  J. Goguen.  Mathematical Models of Cognitive Space and Time , 2006 .

[5]  Marco Schorlemmer,et al.  Coherent concept invention , 2016, C3GI@ESSLLI.

[6]  H. Barlow.  Vision: A computational investigation into the human representation and processing of visual information: David Marr. San Francisco: W. H. Freeman, 1982. pp. xvi + 397 , 1983 .

[7]  J. Bateman,et al.  Ontological Blending in DOL , 2012 .

[8]  Brian Falkenhainer,et al.  The Structure-Mapping Engine: Algorithm and Examples , 1989, Artif. Intell..

[9]  Santiago Ontañón,et al.  Amalgams: A Formal Approach for Combining Multiple Case Solutions , 2010, ICCBR.

[10]  Kai-Uwe Kühnberger,et al.  Heuristic-Driven Theory Projection: An Overview , 2014, Computational Approaches to Analogical Reasoning.

[11]  Enric Plaza,et al.  Analogy, Amalgams, and Concept Blending , 2015 .

[12]  T. Veale,et al.  Computation and Blending , 2001 .

[13]  Agnar Aamodt,et al.  Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches , 1994, AI Commun..

[14]  D. Gentner,et al.  Analogical Learning and Reasoning , 2013 .

[15]  Fabio Massacci,et al.  E Pluribus Unum , 2004, WAC.

[16]  Eleanor Rosch,et al.  Principles of Categorization , 1978 .

[17]  Kai-Uwe Kühnberger,et al.  Algorithmic Aspects of Theory Blending , 2014, AISC.

[18]  A. Weiner,et al.  E Pluribus Unum: 3' end formation of polyadenylated mRNAs, histone mRNAs, and U snRNAs. , 2005, Molecular cell.

[19]  M. Boden.  The creative mind : myths & mechanisms , 1991 .

[20]  Gordon Plotkin,et al.  A Note on Inductive Generalization , 2008 .

[21]  Paul Thagard,et al.  The AHA! Experience: Creativity Through Emergent Binding in Neural Networks , 2011, Cogn. Sci..

[22]  Ulf Krumnack,et al.  Theory Blending as a Framework for Creativity in Systems for General Intelligence , 2012 .

[23]  Boyang Li,et al.  Goal-Driven Conceptual Blending: A Computational Approach for Creativity , 2012, ICCC.

[24]  Joseph A. Goguen,et al.  Style: A Computational and Conceptual Blending-Based Approach , 2010, The Structure of Style.

[25]  Gilles Fauconnier,et al.  Conceptual Integration Networks , 1998, Cogn. Sci..

[26]  Santiago Ontañón,et al.  On Knowledge Transfer in Case-Based Inference , 2012, ICCBR.

[27]  A. Smaill,et al.  The role of blending in mathematical invention , 2015 .

[28]  Ulf Krumnack,et al.  What Is a Derived Signature Morphism? , 2014, WADT.

[29]  Francisco C. Pereira.  Optimality Principles for Conceptual Blending: A First Computational Approach , 2003 .

[30]  Angela Schwering,et al.  Syntactic principles of heuristic-driven theory projection , 2009, Cognitive Systems Research.