Agents often have to construct plans that obey resource limits for continuous resources whose consumption can only be characterized by probability distributions. While Markov Deci...
Modal logic represents knowledge that agents have about other agents' knowledge. Probabilistic modal logic further captures probabilistic beliefs about probabilistic beliefs....
We propose a new discriminative framework, namely Hidden Dynamic Conditional Random Fields (HDCRFs), for building probabilistic models which can capture both internal and external...
Partially Observable Markov Decision Processes have been studied widely as a model for decision making under uncertainty, and a number of methods have been developed to find the s...
Temporal logics are widely used in specifying goals of agents. We noticed that when directing agents, humans often revise their requirements for the agent, especially as they gath...