knowledge representation

Development of a representation for detecting and reasoning about deception

We argue that, although belief revision mechanisms surely operate at the level of single agents, we must also consider the need to lift an agent’s understanding of the belief revision process to the knowledge level in order to intentionally guide other agents’ revision processes with whom it socially interacts.

We argue that distinguishing between types of deception is required to generate successful action. We introduce a Framework for Identifying Deceptive Entities and demonstrate that it has the representational power to discriminate categories of deception.

We explore the representational and inferential requirements for supporting a rich notion of belief revision. More to the point, we argue that although belief revision mechanisms surely operate at the level of single agents, we must also consider the need to lift an agent’s understanding of the belief revision process to the knowledge level in order to intentionally guide other agents’ revision processes with whom it socially interacts.

We introduce a practical, computational framework that enables socially aware inference. We then demonstrate the framework’s ability to model a common, complex, and under-investigated aspect of human social behavior: deception.

We present a novel approach to temporal clustering of patient treatment information based on the semantic similarity of longitudinal histories.

We report on a system for inductive process modeling that uses structural constraints to reduce its search through the space of candidate models and to produce ones that human scientists find plausible.

We introduce an approach for extracting constraints on process model construction. Results suggest that the learned constraints make sense ecologically and may provide insight into the nature of the modeled domain.