Skip to Main Content
To interact naturally with humans, robots need to be aware of their own surroundings. This awareness is usually encoded in some implicit or explicit representation of the situated context. In this paper, we present a new framework for constructing rich belief models of the robot's environment. Key to our approach is the use of Markov Logic as a unified framework for inference over these beliefs. Markov Logic is a combination of first-order logic and probabilistic graphical models. Its expressive power allows us to capture both the rich relational structure of the environment and the uncertainty arising from the noise and incompleteness of low-level sensory data. The constructed belief models evolve dynamically over time and incorporate various contextual information such as spatio-temporal framing, multi-agent epistemic status, and saliency measures. Beliefs can also be referenced and extended “top-down” via linguistic communication. The approach is being integrated into a cognitive architecture for mobile robots interacting with humans using spoken dialogue.