Declarative Logic-Based Pareto-Optimal Agent Decision Making.

There are many applications where an autonomous agent can perform many sets of actions. It must choose one set of actions based on some behavioral constraints on the agent. Past work has used deontic logic to declaratively express such constraints in logic, and developed the concept of a feasible status set (FSS), a set of actions that satisfy these constraints.

Bayesian-knowledge driven ontologies: A framework for fusion of semantic knowledge under uncertainty and incompleteness.

The modeling of uncertain information is an open problem in ontology research and is a theoretical obstacle to creating a truly semantic web. Currently, ontologies often do not model uncertainty, so stochastic subject matter must either be normalized or rejected entirely. Because uncertainty is omnipresent in the real world, knowledge engineers are often faced with the dilemma of performing prohibitively labor-intensive research or running the risk of rejecting correct information and accepting incorrect information.

An Application of Inverse Reinforcement Learning to Estimate Interference in Drone Swarms.

Despite the increasing applications, demands, and capabilities of drones, in practice they have only limited autonomy for accomplishing complex missions, resulting in slow and vulnerable operations and difficulty adapting to dynamic environments. To lessen these weaknesses, we present a computational framework for deducing the original intent of drone swarms by monitoring their movements. We focus on interference, a phenomenon that is not initially anticipated by drones but results in complicated operations due to its significant impact on performance and its challenging nature.