Executive functions for Learning and decision-making in a bio-inspired cognitive architecture
Klaus Raizer · 2015
This work's goal is the development of executive functions for a codelet-based bioinspired cognitive architecture.One of the major challenges every creature faces, being biological or artificial, is to define the next action to be taken, at each time step, as a function of how it perceives its surrounding environment.This decision can be made by a reactive algorithm, which always repeats the same decisions for a given situation, or by an adaptive process, which is able to make use of learning mechanisms in order to make distinct decisions based on past experience.In this work, deliberative decision-making and reinforcement learning mechanisms have been integrated into a single framework.In cognitive science literature, these functions are known as executive functions.The solution proposed here is part of our group's central line of research, which is the investigation and development of a codeletbased cognitive architecture.In this context, a central contribution made by this work is the development and implementation of algorithms capable of providing this cognitive architecture with a group of executive functions, which in turn can be used to implement complex solutions with arbitrary granularity.Functions for deliberative decision-making have been implemented in the form of a modified behavior network, while the learning component was developed in the form of a new algorithm called GLAS (Gated-Learning Action Selection), based on stimulus gating and known computational neuroscience models.This framework has been validated with problems in mobile robotics and in action selection by reinforcement learning.The cognitive architecture under development, when incremented by the contributions presented in this work, has the potential to serve as a base for future work and research in the fields of artificial intelligence, robotics and artificial cognition.