Editing Conceptual blending (section)

==Computational models==
Conceptual blending is closely related to [[frame (artificial intelligence)|frame]]-based theories, but goes beyond these primarily in that it is a theory of how to ''combine'' frames (or frame-like objects). An early computational model of a process called "[[view application]]", which is closely related to conceptual blending (which did not exist at the time), was implemented in the 1980s by Shrager at [[Carnegie Mellon University]] and [[PARC (company)|PARC]], and applied in the domains of causal reasoning about complex devices<ref>{{Cite journal |last=Shrager |first=Jeff |date=1987 |title=Theory Change via View Application in Instructionless Learning |journal=[[Machine Learning (journal)|Machine Learning]] |volume=2 |issue=3 |pages=247–276 |doi=10.1007/bf00058681 |doi-access=free}}</ref> and scientific reasoning.<ref>{{Cite book |last=Shrager |first=Jeff |title=Computational models of scientific discovery and theory formation |date=1990 |publisher=Morgan Kaufmann |isbn=9781558601314 |editor-last=Shrager |editor-first=Jeff |series=Morgan Kaufmann series in machine learning |location=San Mateo, Calif. |chapter=Commonsense perception and the psychology of theory formation |editor-last2=Langley |editor-first2=Pat}}</ref> More recent computational accounts of blending have been developed in areas such as mathematics.<ref>{{Cite journal |last1=Guhe |first1=Markus |last2=Pease |first2=Alison |last3=Smaill |first3=Alan |last4=Martinez |first4=Maricarmen |last5=Schmidt |first5=Martin |last6=Gust |first6=Helmar |last7=Kühnberger |first7=Kai-Uwe |last8=Krumnack |first8=Ulf |date=September 2011 |title=A computational account of conceptual blending in basic mathematics |url=https://linkinghub.elsevier.com/retrieve/pii/S1389041711000155 |journal=[[Cognitive Systems Research]] |volume=12 |issue=3–4 |pages=249–265 |doi=10.1016/j.cogsys.2011.01.004|url-access=subscription }}</ref>  Some later models are based upon [[Structure mapping engine|structure mapping]], which did not exist at the time of the earlier implementations. Recently, within the context of non-monotonic extensions of AI reasoning systems (and in line with the frame-based theories), a general framework able to account for both complex human-like concept combinations (like the PET-FISH problem) and conceptual blending<ref>{{cite journal |last1=Lieto |first1=Antonio |last2=Pozzato |first2=Gian Luca |year=2020 |title=A description logic framework for commonsense conceptual combination integrating typicality, probabilities and cognitive heuristics |journal=[[Journal of Experimental and Theoretical Artificial Intelligence]] |volume=32 |issue=5 |pages=769–804 |arxiv=1811.02366 |bibcode=2020JETAI..32..769L |doi=10.1080/0952813X.2019.1672799 |s2cid=53224988}}</ref> has been tested and developed in both cognitive modelling<ref>{{cite journal |last1=Lieto |first1=Antonio |last2=Perrone |first2=Federico |last3=Pozzato |first3=Gian Luca |last4=Chiodino |first4=Eleonora |year=2019 |title=Beyond subgoaling: A dynamic knowledge generation framework for creative problem solving in cognitive architectures |journal=[[Cognitive Systems Research]] |volume=58 |pages=305–316 |doi=10.1016/j.cogsys.2019.08.005 |s2cid=201127492 |hdl-access=free |hdl=2318/1726157}}</ref> and [[computational creativity]] applications.<ref>{{cite journal |last1=Lieto |first1=Antonio |last2=Pozzato |first2=Gian Luca |year=2019 |title=Applying a description logic of typicality as a generative tool for concept combination in computational creativity |journal=Intelligenza Artificiale |volume=13 |pages=93–106 |doi=10.3233/IA-180016 |s2cid=201827292 |hdl-access=free |hdl=2318/1726158}}</ref><ref>{{cite conference |last1=Chiodino |first1=Eleonora |last2=Di Luccio |first2=Davide |last3=Lieto |first3=Antonio |last4=Messina |first4=Alberto |last5=Pozzato |first5=Gian Luca |last6=Rubinetti |first6=Davide |year=2020 |title=A Knowledge-based System for the Dynamic Generation and Classification of Novel Contents in Multimedia Broadcasting |url=https://www.antoniolieto.net/ECAI_RAI_TCL.pdf |conference=ECAI 2020, 24th European Conference on Artificial Intelligence |doi=10.3233/FAIA200154 |doi-access=free}}</ref>