Cognitively inspired design : Rethink the wheel for self-driving cars

Sammanfattning: This thesis examines Cognitively Inspired Design (CID), which is the process of transferring cognitive science frameworks and theories to intelligent systems in an application context. The thesis studies the relation between cognitive science and the traditional approach to developing systems. There are numerous differences and challenges between those two fields, making the transformation from cognitive science to designing a novel cognitive system a challenging process. To examine this process, the Guest and Martin (2021) multi-layer model has been utilized. The model proposes a sequence of six layers in which a researcher follows from a defined cognitive concept or framework to an empirical experiment of a computational model. This multi-layered model is a path function in which each layer is a function that takes the input from the previous layer and passes the output to the following layer.The thesis takes the application of self-driving cars as the context of study. Self-driving cars are considered one of the most important applications requiring a high level of intelligence and cognitive ability because they encounter real world scenarios and the risk of failure may cost lives. This thesis analyzes the transformation of CID in three main studies.The first study theoretically analyzes the applicability and compares the different cognitive paradigms and current AI techniques for self-driving cars. The thesis argues for exploring the emergent paradigm as a less explored paradigm in cognitive systems compared to its main opponent paradigm; the cognitivist. The emergent paradigm is claimed to describe the interactive nature of the human cognition. The analysis highlights the opportunities that the field of self-driving cars benefits from when considering the characteristics of the emergent paradigm.The second study considers the path function for a selected emergent paradigm theory. The study focuses on the aspect of how humans learn from hypothetical scenarios before encountering them in the real world, in particular, learning how to handle rare scenarios that are difficult to learn in the real world. The study addresses the mechanism for automatically generating these scenarios without being designed and created manually by a developer. The study considers curriculum learning as the candidate theory subject of study. The process of transferring this theory is studied using the path function multilayer model. The study conducts an experiment to address the relation between the importance of the theory in human learning and its equivalence in artificial cognitive systems.The third study focuses on more debatable theories in the emergent paradigm, in particular enactive and embodiment theories. These theories have gained much attention in research because of the high promise they may deliver for advancing the field of artificial cognitive systems. The applicability of the transition of these theories into artificial cognitive systems is examined in relation to the application of self-driving cars, using the path function multi-layer model. The study considers the aspects that support and hinder such transformation.The thesis concludes by discussing the current state of CID and the aspects the researchers and developers need to consider in this process before, during, and after the transformation. Overall, the thesis attempts to study cognitive theories mainly from an engineering perspective. In short, the thesis focuses on the transformation of CID, not the promise of delivering a novel cognitive system solution.