| Summary: | 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.
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