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

editorial computational creativity concept invention and general intelligence

Abstract Over the last decade, computational creativity as a field of scientific investigation and computational systems engineering has seen growing popularity. Still, the levels of development between projects aiming at systems for artistic production or performance and endeavours addressing creative problem-solving or models of creative cognitive capacities is diverging. While the former have already seen several great successes, the latter still remain in their infancy. This volume collects reports on work trying to close the accrued gap

Abstracts of the 2014 Brains, Minds, and Machines Summer School

A compilation of abstracts from the student projects of the 2014 Brains, Minds, and Machines Summer School, held at Woods Hole Marine Biological Lab, May 29 - June 12, 2014.This work was supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF-1231216

Generalize and Blend: Concept Blending Based on Generalization, Analogy, 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 ele- ments and allowing the performance of reasoning and inference over the combination, is taken as a key ele- ment 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, present- ing the theoretical background together with the main model characteristics, as well as two case studies.The authors acknowledge the financial support of the Future and Emerging Technologies within the 7th Framework Pro- gramme for Research of the European Commission, under FET-Open grant 611553 (COINVENT).Peer Reviewe

Theory and Implementation of Multi-Context Systems Containing Logical and Sub-Symbolic Contexts of Reasoning

In the introductory part, we give a brief overview of the state of the art concerning multi-context systems (MCS), giving some recent examples from the literature, as well as lining out advantages and disadvantages of the different approaches. Then we propose an extension of the heterogeneous multi-context reasoning framework by G. Brewka and T. Eiter, which, in addition to logical contexts of reasoning, also incorporates sub-symbolic contexts of reasoning. The main findings concerning this topic are a simple extension of the concept of bridge rules to the sub-symbolic case and the concept of bridge rule models that allows for a straightforward enumeration of all equilibria of multi-context systems. Also a very basic, yet applicable algorithm for solving this task is presented, and our approach is illustrated with two examples from the fields of text and image classification. Moreover, after some theoretical considerations containing refinements and an expansion of the basic algorithm, we present a proof of concept implementation of an MCS, already integrating different techniques for reducing computational complexity. These techniques have been developed for this very purpose and are described and analyzed as well. The main ideas are a formalism to impose constraints on bridge rules, allowing to state dependencies between different bridge rules or sets of bridge rules, and the concept of conflicting bridge rules, which allows for the application of pruning techniques within the possible set of equilibria of the MCS. Again we illustrate our approach with three examples taken from different domains of application, having a closer look at a special purpose application of multi-context systems made for museum data completion and consistency checking. Finally possible future prospects and extensions of MCS are sketched, presenting inter alia the notion of generalized bridge rules and bridge rule inference. To conclude the thesis a comparison of our work with similar or related approaches is given

On Cognitive Aspects of Human-Level Artificial Intelligence

Following an introduction to the context of Human-Level Artificial Intelligence (HLAI) and (computational) analogy research, a formal analysis assessing and qualifying the suitability of the Heuristic-Driven Theory Projection (HDTP) analogy-making framework for HLAI purposes is presented. An account of the application of HDTP (and analogy-based approaches in general) to the study and computational modeling of conceptual blending is outlined, before a proposal and initial proofs of concept for the application of computational analogy engines to modeling and analysis questions in education studies, teaching research, and the learning sciences are described. Subsequently, the focus is changed from analogy-related aspects in learning and concept generation to rationality as another HLAI-relevant cognitive capacity. After outlining the relation between AI and rationality research, a new conceptual proposal for understanding and modeling rationality in a more human-adequate way is presented, together with a more specific analogy-centered account and an architectural sketch for the (re)implementation of certain aspects of rationality using HDTP. The methods and formal framework used for the initial analysis of HDTP are then applied for proposing general guiding principles for models and approaches in HLAI, together with a proposal for a formal characterization grounding the notion of heuristics as used in cognitive and HLAI systems as additional application example. Finally, work is reported trying to clarify the scientific status of HLAI and participating in the debate about (in)adequate means for assessing the progress of a computational system towards reaching (human-level) intelligence. Two main objectives are achieved: Using analogy as starting point, examples are given as inductive evidence for how a cognitively-inspired approach to questions in HLAI can be fruitful by and within itself. Secondly, several advantages of this approach also with respect to overcoming certain intrinsic problems currently characterizing HLAI research in its entirety are exposed. Concerning individual outcomes, an analogy-based proposal for theory blending as special form of conceptual blending is exemplified; the usefulness of computational analogy frameworks for understanding learning and education is shown and a corresponding research program is suggested; a subject-centered notion of rationality and a sketch for how the resulting theory could computationally be modeled using an analogy framework is discussed; computational complexity and approximability considerations are introduced as guiding principles for work in HLAI; and the scientific status of HLAI, as well as two possible tests for assessing progress in HLAI, are addressed