Marc Pickett

Learn More
We investigate the task of modeling opendomain, multi-turn, unstructured, multiparticipant, conversational dialogue. We specifically study the effect of incorporating different elements of the conversation. Unlike previous efforts, which focused on modeling messages and responses, we extend the modeling to long context and participant’s history. Our system(More)
The Seldon software toolkit combines concepts from agent-based modeling and social science to create a computationally social dynamic model for group recruitment. The underlying recruitment model is based on a unique three-level hybrid agent-based architecture that contains simple agents (level one), abstract agents (level two), and cognitive agents (level(More)
Since Emile Borel’s study in 1938, the game of poker has resurfaced every decade as a test bed for research in mathematics, economics, game theory, and now a variety of computer science subfields. Poker is an excellent domain for AI research because it is a game of imperfect information and a game where opponent modeling can yield virtually unlimited(More)
A central problem in decision support tasks is operator overload, in which a human operator’s performance suffers because he or she is overwhelmed by the cognitive requirements of a task. To alleviate this problem, it would be useful to provide the human operator with an automated assistant to share some of the task’s cognitive load. However, the(More)
Many future decision support systems will be human-centric, i.e., require substantial human oversight and control. Because these systems often provide critical services, high assurance is needed that they satisfy their requirements. This paper, the product of an interdisciplinary research team of experts in formal methods, adaptive agents, and cognitive(More)