As one of the few genuine departments of cognitive science in the world, we offer unique and exciting opportunities for students to focus on the scientific study of mind, brain, and intelligence. Staffed by a core of cognitive-science oriented psychologists, philosophers, and computer scientists, the department complements Rensselaer's traditional strengths in science, engineering, and technology, and is widely regarded as a leader in the area of computational cognitive modeling. We offer a highly selective PhD program in Cognitive Science, and BS programs in both Psychology and Philosophy. Faculty research interests include computational cognitive modeling, artificial intelligence, human and machine reasoning, computational linguistics, perception and action, theoretical neuroscience, cognitive robotics, cognitive engineering and advanced synthetic characters.
COGSCI2013, the 35th annual meeting of the Cognitive Science Society and the first to take place in Germany, was held from the 31st of July to the 3rd of August. Cognitive scientists with varied backgrounds gathered in Berlin to report and discuss on expanding lines of research, spanning multiple fields but striving in one direction: to understand cognition with all its properties and peculiarities. A rich program featuring keynotes, symposia, workshops and tutorials, along regular oral and poster sessions, offered the attendees a vivid and exciting overview of where the discipline is going while serving as a fertile forum of interdisciplinary discussion and exchange. This report attempts to point out why this should matter to artificial intelligence as a whole.
We outline the cognitive model CASS (Cognitive-Affective State System). As the name suggests it is a cognitive model that also takes human affect into account. CASS combines Dynamic Bayesian Networks (DBNs) and an ACT-R model. The DBN model (R-BARS, the Rensselaer Bayesian Affect Recognition System) determines the user's most likely affective states using both current and stored sensory data. The affective-cognitive model integrates R-BARS with ACT-R to play two roles: (1) the use of model tracing to determine the impact of affective state on cognitive processing, and (2) linking changes in affective state to changes in the value of ACT-R's parameters so as to directly generate (i.e., predict) the influence of affect on cognition.
I am sure we have all heard about Sophia the robot, as most of us have been fixated on her journey for quite some time now. Like Sophia, who has been constructed using Artificial Intelligence (AI) technology, which has become one of the industry's most followed technology of the season, is being studied by many scientists and researchers to connect the distinctions between machines and humans. How do these machines run on AI technology allowing them to operate independently, learning from their environment to interact how humans do. Isn't it marvelous and something to be in awe of?
Prof. Tom Griffiths is the director of the Computational Cognitive Science Lab and the Institute of Cognitive and Brain Sciences at UC Berkeley. He studies human cognition and is involved with the Center for Human Compatible Artificial Intelligence. I asked him for insight into the intersection of cognitive science and AI. He offers his thoughts on the historical interaction of the fields and what aspects of human cognition might be relevant to developing AI in the future.