When almost is not even close: Remarks on the approximability of HDTP

A growing number of researchers in Cognitive Science advocate the thesis that human cognitive capacities are constrained by computational tractability. If right, this thesis also can be expected to have far-reaching consequences for work in Artificial General Intelligence: Models and systems considered as basis for the development of general cognitive architectures with human-like performance would also have to comply with tractability constraints, making in-depth complexity theoretic analysis a necessary and important part of the standard research and development cycle already from a rather early stage. In this paper we present an application case study for such an analysis based on results from a parametrized complexity and approximation theoretic analysis of the Heuristic Driven Theory Projection (HDTP) analogy-making framework

COINVENT: Towards a Computational Concept Invention Theory

We aim to develop a computationally feasible, cognitively-inspired, formal model of concept invention, drawing on Fauconnier and Turner’s theory of conceptual blending, and grounding it on a sound mathematical theory of concepts. Conceptual blending, although successfully applied to describing combinational creativity in a varied number of fields, has barely been used at all for implementing creative computational systems, mainly due to the lack of sufficiently precise mathematical characterisations thereof. The model we will define will be based on Goguen’s proposal of a Unified Concept Theory, and will draw from interdisciplinary research results from cognitive science, artificial intelligence, formal methods and computational creativity. To validate our model, we will implement a proof of concept of an autonomous computational creative system that will be evaluated in two testbed scenarios: mathematical reasoning and melodic harmonisation. We envisage that the results of this project will be significant for gaining a deeper scientific understanding of creativity, for fostering the synergy between understanding and enhancing human creativity, and for developing new technologies for autonomous creative systems.The project COINVENT acknowledges the nancial support of the Future and Emerging Tech- nologies (FET) programme within the Seventh Framework Programme for Research of the Eu- ropean Commission, under FET-Open Grant number: 611553Peer Reviewe

Rationality in context: An analogical perspective

At times, human behavior seems erratic and irrational. Therefore, when modeling human decision-making, it seems reasonable to take the remarkable abilities of humans into account with respect to rational behavior, but also their apparent deviations from the normative standards of rationality shining up in certain rationality tasks. Based on well-known challenges for human rationality, together with results from psychological studies on decision-making and from previous work in the field of computational modeling of analogy-making, I argue that the analysis and modeling of rational belief and behavior should also consider context-related cognitive mechanisms like analogy-making and coherence maximization of the background theory. Subsequently, I conceptually outline a high-level algorithmic approach for a Heuristic Driven Theory Projection-based system for simulating context-dependent human-style rational behavior. Finally, I show and elaborate on the close connections, but also on the significant differences, of this approach to notions of "ecological rationality"

Application of Analogical Reasoning for Use in Visual Knowledge Extraction

There is a continual push to make Artificial Intelligence (AI) as human-like as possible; however, this is a difficult task because of its inability to learn beyond its current comprehension. Analogical reasoning (AR) has been proposed as one method to achieve this goal. Current literature lacks a technical comparison on psychologically-inspired and natural-language-processing-produced AR algorithms with consistent metrics on multiple-choice word-based analogy problems. Assessment is based on “correctness” and “goodness” metrics. There is not a one-size-fits-all algorithm for all textual problems. As contribution in visual AR, a convolutional neural network (CNN) is integrated with the AR vector space model, Global Vectors (GloVe), in the proposed, Image Recognition Through Analogical Reasoning Algorithm (IRTARA). Given images outside of the CNN’s training data, IRTARA produces contextual information by leveraging semantic information from GloVe. IRTARA’s quality of results is measured by definition, AR, and human factors evaluation methods, which saw consistency at the extreme ends. The research shows the potential for AR to facilitate more a human-like AI through its ability to understand concepts beyond its foundational knowledge in both a textual and visual problem space