Special Session on
Computational Models of Motivation for Trusted Autonomy
Within the IEEE Symposium on Computational Intelligence for Human-Like Intelligence
School of Engineering and Information Technology
University of New South Wales, Canberra
School of Electrical Engineering and Computer Science
Description of the special session:
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.
Topics of interests include but are not limited to:
· Cognitive architectures incorporating motivation or value systems
· Computational models of motivation, trust, creativity
· Goal representation and experience-based goal generation
· Human factors studies of motivation, trust, creativity
· Leadership and teamwork
· Machine consciousness
· Motivated learning or optimisation
· Problem solving based on intuition, creativity, insight, curiosity and imagination
· Role of emotions in value systems
· Trusted autonomous systems
· Value systems or computational motivation for artificial agents and robots
Kathryn Merrick, University of New South Wales, Canberra, Australia
Janusz Starzyk, Ohio University, USA.
Hussein Abbass, University of New South Wales, Canberra,
Sreenatha Anavatti, University of New South Wales, Canberra, Australia
Angelo Cangelosi, University of Plymouth, UK
Michael Barlow, University of New South Wales, Canberra, Australia
Matt Garratt, University of New South Wales, Canberra, Australia
Haibo He, University of Rhode Island, USA
Adrian Horzyk, AGH, Krakow, Poland
Jacek Mandziuk, Warsaw University of Technology, Poland
Inaki Rano, University of Ulster, UK.
Ruhul Sarker, University of New South Wales, Canberra, Australia
Nazmul Siddique, University of Ulster, UK.
This Special Session will be part of the 2016 IEEE Symposium Series on Computational Intelligence to be held between 6-9 December 2016 in Athens, Greece.
July 18, 2016: Deadline for submission of full-length papers.
September 12, 2016: Acceptance/Rejection Notification.
October 10, 2016: Final papers submission deadline.
Guideline for paper submission is available on the IEEE SSCI web site.