Computational models of motivation play an important role in the design of artificial agents and robots with adaptive, lifelong learning behaviour, because they provide a way for agents to behave autonomously through spontaneous, self-generated activity. Broadly, motivated behaviour has two universal characteristics: control striving in the physical and social environment; and goal setting, engagement, and disengagement. These goals may be concerned with what the agent will do, why, when, with whom, how, and so on. Artificial systems research has incorporated computational models of motivation such as curiosity, novelty seeking and competence seeking as well as models of cooperation, imitation, protection, understanding, trust, emotion, creativity or other models that permit the agent to evaluate the saliency of environmental stimuli. These value systems can be embedded in different agent frameworks for learning, planning, evolution or rule-based action.

Trusted autonomy is a field of research that focuses on understanding and designing the interaction space between two entities, each of which exhibits a level of autonomy. These entities can be humans, animals, machines, or a combination of these. The concept of trust is receiving increasing attention from computer scientists and engineers, with the understanding that automation is usable only if trusted by humans. Human factor studies have examined interactions between humans and machines, computers and robots to explore the role of trust and to improve the performance of agents during interactions. Trust models are likely to play an important role in future autonomous systems, and will need to integrate seamlessly with other components of autonomy such as motivation.

This special session aims to bring together researchers in computational motivation and trust to explore the synthesis of ideas in autonomous, cognitive and developmental systems.  We call for papers on two broad facets of this topic: computational motivation for trusted autonomy and human studies that can inform the design of such motivation systems.