In reinforcement learning, an agent tries to learn a policy, i.e., how to select an action in a given state of the environment, so that it maximizes the total amount of reward it ...
We propose a novel approach to intelligent tutoring gaming simulations designed for both educational and inquiry purposes in complex multi-actor systems such as infrastructures or...
Abstract. In many settings, bidding agents for auctions do not know their preferences a priori. Instead, they must actively determine them through deliberation (e.g., information p...
Presently, there are user interfaces that allow multimodal interactions. Many existing research and prototype systems introduced embodied agents, assuming that they allow a more n...
Distributed Internet-based attacks on computer systems are becoming more prevalent. These attacks usually employ some form of automation and involve the compromise of many systems...