Utilizing Cross-Domain Cognitive Mechanisms for Modeling Aspects of Artificial General Intelligence

In this era of increasingly rapid availability of resources of all kinds, a widespread need to characterize, filtrate, use, and evaluate what could be necessary and useful becomes a crucially vital everyday task. Neither research in the field of artificial intelligence (AI) nor in cognitive science (CogSci) is an exception (let alone within a crossing of both paths). A promised goal of AI was to primarily focus on the study and design of intelligent artifacts that show aspects of human-like general intelligence (GI). That is, facets of intelligence similar to those exhibited by human beings in solving problems related to cognition. However, the focus in achieving AI’s original goal is scattered over time. The initial ambitions in the 1960s and 1970s had grown by the 1980s into an "industry", where not only researchers and engineers but also entire companies developed the AI technologies in building specialized hardware. But the result is that technology afforded us with many, many devices that allegedly work like humans, though they can only be considered as life facilitators (if they even do). This is mainly due to, I propose, basic changes on viewing what true essences of intelligence should have been considered within scientific research when modeling systems with GI capacities. A modern scientific approach to achieving AI by simulating cognition is mainly based on representations and implementations of higher cognition in artificial systems. Luckily, such systems are essentially designed with the intention to be acquired with a "human like" level of GI, so that their functionalities are supported by results (and solution methodologies) from many cognitive scientific disciplines. In classical AI, only a few number of attempts have tried to integrate forms of higher cognitive abilities in a uniform framework that model, in particular, cross-domain reasoning abilities, and solve baffling cognition problems —the kind of problems that a cognitive being (endowed with traits of GI) could only solve. Unlike classical AI, the intersection between the recent research disciplines: artificial general intelligence (AGI) and CogSci, is promising in this regard. The new direction is mostly concerned with studying, modeling, and computing AI capabilities that simulate facets of GI and functioning of higher cognitive mechanisms. Whence, the focus in this thesis is on examining general problem solving capabilities of cognitive beings that are both: "human-comparable" and "cognitively inspired", in order to contribute to answering two substantial research questions. The first seeks to find whether it is still necessary to model higher cognitive abilities in models of AGI, and the second asks about the possibility to utilize cognitive mechanisms to enable cognitive agents demonstrate clear signs of human-like (general) intelligence. Solutions to cross-domain reasoning problems (that characterize human-like thinking) need to be modeled in a way that reflects essences of cognition and GI of the reasoner. This could actually be achieved (among other things) through utilizing cross-domain, higher cognitive mechanisms. Examples of such cognitive mechanisms include analogy-making and concept blending (CB), which are exceptional as active areas of recent research in cognitive science, though not enough attention has been given to the rewards and benefits one gets when they interact. A basic claim of the thesis is that several aspects of human-comparable level of GI are based on forms of (cross-domain) representations and (creative) productions of conceptions. The thesis shows that computing these aspects within AGI-based systems is indispensable for their modeling. In addition, the aspects can be modeled by employing certain cognitive mechanisms. The specific examples of mechanisms most relevant to the current text are computation of generalizations (i.e. abstractions) using analogy-making (i.e. transferring a conceptualization from one domain into another domain) and CB (i.e. merging parts of conceptualizations of two domains into a new domain). Several ideas are presented and discussed in the thesis to support this claim, by showing how the utilization of these mechanisms can be modeled within a logic-based framework. The framework to be used is Heuristic-Driven Theory Projection (HDTP), which can model solutions to a concrete set of cognition problems (including creativity, rationality, noun-noun combinations, and the analysis of counterfactual conditionals). The resulting contributions may be considered as a necessary, although not by any means a sufficient, step to achieve intelligence on a human-comparable scale in AGI-based systems. The thesis thus fills an important gap in models of AGI, because computing intelligence on a human-comparable scale (which is, indeed, an ultimate goal of AGI) needs to consider the modeling of solutions to, in particular, the aforementioned problems