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

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

Analogy, Amalgams, and Concept Blending

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 paper, we provide an intermediate report on work towards the development of a computational-level and algorithmic-level account of concept blending. We present the theoretical background as well as an algorithmic proposal combining techniques from computational analogy-making and case-based reasoning, and exemplify the feasibility of the approach in two case studies.. © 2015 Cognitive Systems Foundation.The authors acknowledge the financial support of the Future and Emerging Technologies programme within the Seventh Framework Programme for Research of the European Commission, under FET-Open grant number: 611553 (COINVENT)Peer Reviewe

Theory blending: extended algorithmic aspects and examples

In Cognitive Science, conceptual blending has been proposed as an important cognitive mechanism that facilitates the creation of new concepts and ideas by constrained combination of available knowledge. It thereby provides a possible theoretical foundation for modeling high-level cognitive faculties such as the ability to understand, learn, and create new concepts and theories. Quite often the development of new mathematical theories and results is based on the combination of previously independent concepts, potentially even originating from distinct subareas of mathematics. Conceptual blending promises to offer a framework for modeling and re-creating this form of mathematical concept invention with computational means. This paper describes a logic-based framework which allows a formal treatment of theory blending (a subform of the general notion of conceptual blending with high relevance for applications in mathematics), discusses an interactive algorithm for blending within the framework, and provides several illustrating worked examples from mathematics

Asymmetric Hybrids: Dialogues for Computational Concept Combination

When people combine concepts these are often characterised as “hybrid”, “impossible”, or “humorous”. However, when simply considering them in terms of extensional logic, the novel concepts understood as a conjunctive concept will often lack meaning having an empty extension (consider “a tooth that is a chair”, “a pet flower”, etc.). Still, people use different strategies to produce new non-empty concepts: additive or integrative combination of features, alignment of features, instantiation, etc. All these strategies involve the ability to deal with conflicting attributes and the creation of new (combinations of) properties. We here consider in particular the case where a Head concept has superior ‘asymmetric’ control over steering the resulting concept combination (or hybridisation) with a Modifier concept. Specifically, we propose a dialogical approach to concept combination and discuss an implementation based on axiom weakening, which models the cognitive and logical mechanics of this asymmetric form of hybridisation

Asymmetric Hybrids: Dialogues for Computational Concept Combination

When people combine concepts these are often characterised as “hybrid”, “impossible”, or “humorous”. However, when simply considering them in terms of extensional logic, the novel concepts understood as a conjunctive concept will often lack meaning having an empty extension (consider “a tooth that is a chair”, “a pet flower”, etc.). Still, people use different strategies to produce new non-empty concepts: additive or integrative combination of features, alignment of features, instantiation, etc. All these strategies involve the ability to deal with conflicting attributes and the creation of new (combinations of) properties. We here consider in particular the case where a Head concept has superior ‘asymmetric’ control over steering the resulting concept combination (or hybridisation) with a Modifier concept. Specifically, we propose a dialogical approach to concept combination and discuss an implementation based on axiom weakening, which models the cognitive and logical mechanics of this asymmetric form of hybridisation

Asymmetric Hybrids: Dialogues for Computational Concept Combination

When people combine concepts these are often characterised as “hybrid”, “impossible”, or “humorous”. However, when simply considering them in terms of extensional logic, the novel concepts understood as a conjunctive concept will often lack meaning having an empty extension (consider “a tooth that is a chair”, “a pet flower”, etc.). Still, people use different strategies to produce new non-empty concepts: additive or integrative combination of features, alignment of features, instantiation, etc. All these strategies involve the ability to deal with conflicting attributes and the creation of new (combinations of) properties. We here consider in particular the case where a Head concept has superior ‘asymmetric’ control over steering the resulting concept combination (or hybridisation) with a Modifier concept. Specifically, we propose a dialogical approach to concept combination and discuss an implementation based on axiom weakening, which models the cognitive and logical mechanics of this asymmetric form of hybridisation

ASP, Amalgamation and the Conceptual Blending Workflow

We present a framework for conceptual blending – a concept invention method that is advocated in cognitive science as a fundamental, and uniquely human engine for creative thinking. Herein, we employ the search capabilities of ASP to find commonalities among input concepts as part of the blending process, and we show how our approach fits within a generalised conceptual blending workflow. Specifically, we orchestrate ASP with imperative Python programming, to query external tools for theorem proving and colimit computation. We exemplify our approach with an example of creativity in mathematics. © Springer International Publishing Switzerland 2015.This work is supported by the 7th Framework Programme for Research of the European Commission funded COINVENT project (FET-Open grant number: 611553). M. Eppe is supported by the German Academic Exchange ServicePeer Reviewe

Applying blended conceptual spaces to variable choice and aesthetics in data visualisation

Computational creativity is an active area of research within the artificial intelligence domain that investigates what aspects of computing can be considered as an analogue to the human creative process. Computers can be programmed to emulate the type of things that the human mind can. Artificial creativity is worthy of study for two reasons. Firstly, it can help in understanding human creativity and secondly it can help with the design of computer programs that appear to be creative. Although the implementation of creativity in computer algorithms is an active field, much of the research fails to specify which of the known theories of creativity it is aligning with. The combination of computational creativity with computer generated visualisations has the potential to produce visualisations that are context sensitive with respect to the data and could solve some of the current automation problems that computers experience. In addition theories of creativity could theoretically compute unusual data combinations, or introducing graphical elements that draw attention to the patterns in the data. More could be learned about the creativity involved as humans go about the task of generating a visualisation. The purpose of this dissertation was to develop a computer program that can automate the generation of a visualisation, for a suitably chosen visualisation type over a small domain of knowledge, using a subset of the computational creativity criteria, in order to try and explore the effects of the introduction of conceptual blending techniques. The problem is that existing computer programs that generate visualisations are lacking the creativity, intuition, background information, and visual perception that enable a human to decide what aspects of the visualisation will expose patterns that are useful to the consumer of the visualisation. The main research question that guided this dissertation was, “How can criteria derived from theories of creativity be used in the generation of visualisations?”. In order to answer this question an analysis was done to determine which creativity theories and artificial intelligence techniques could potentially be used to implement the theories in the context of those relevant to computer generated visualisations. Measurable attributes and criteria that were sufficient for an algorithm that claims to model creativity were explored. The parts of the visualisation pipeline were identified and the aspects of visualisation generation that humans are better at than computers was explored. Themes that emerged in both the computational creativity and the visualisation literature were highlighted. Finally a prototype was built that started to investigate the use of computational creativity methods in the ‘variable choice’, and ‘aesthetics’ stages of the data visualisation pipeline.School of ComputingM. Sc. (Computing