Infants are excellent at interacting with the environment. Especially in the initial phase of cognitive development, they exhibit amazing abilities to generate novel behaviors in unfamiliar situations and explore actively to learn the best while lacking extrinsic rewards from the environment. These abilities of sense-making and knowledge construction of the environment set them apart from even the most advanced autonomous robots.
For most artificial agents (and robots), acquiring such abilities is overwhelming. In most traditional Artificial Intelligence (AI) approaches, learning is usually insufficient, with various biases, and lacks of flexibility. Seeking ways to explain the learning mechanism behind infants’ early cognitive development and try to replicate some of these abilities that babies have for an autonomous agent have become a focal point of recent efforts in robotics and AI research.
In this dissertation, I propose a computational model of Constructivist Cognitive Architecture (CCA) as a way towards simulating the early learning mechanism of infants’ cognitive development based on theories of enactive cognition, intrinsic motivation, and constructivist epistemology. Meanwhile, the CCA allows a self-motivated agent to autonomously construct the perception of the environment and acquire capabilities of self-adaption and flexibility to generate proper behaviors to tackle with diverse situations in interacting with the environment.