Work published in 2024 IEEE International Conference on Development and Learning (ICDL). This paper addresses our initial experiments exploring the acquired behavior by a simulated humanoid agent using two different motivational systems - drives and impulses - guiding the decision making process as part of the cognitive architecture.
Drives and Impulses: Shaping Motivation and Procedural Learning for Humanoid Robots / 2024 / IEEE International Conference on Development and Learning (ICDL)
In the emerging field of robotics, the initial development of robots handling basic tasks represented a revolutionary breakthrough. With ongoing advancements in science and technology, we anticipate the integration of artificial entities into diverse environments, performing a range of tasks autonomously. This necessitates the cultivation of intelligence and adaptability in these agents. To achieve this, agents must make informed decisions, considering environmental consequences. Motivation becomes pivotal, guiding decision-making and behavior toward fulfilling the agent’s needs, drawing inspiration from human adeptness driven by motivation. This research endeavors to adapt two distinct motivation theories from human literature for the learning process of artificial agents. Employing a cognitive architecture, our investigation involves the learning process of a simulated humanoid robot, incorporating two distinct motivation systems: (i) behaviors driven by enduring needs and (ii) behaviors arising from impulses linked to temporary needs. The primary aim is to enhance comprehension of behaviors under diverse motivational frameworks within artificial cognitive agents and give insights into the direction of autonomous robots.
L. L. Rossi, L. Berto, P. D. P. Costa, R. Gudwin, E. Colombini and A. Simões, “Drives and Impulses: Shaping Motivation and Procedural Learning for Humanoid Robots,” 2024 IEEE International Conference on Development and Learning (ICDL), Austin, TX, USA, 2024, pp. 1-8, doi: 10.1109/ICDL61372.2024.10645017.