We present an approach for autonomous sensor control for information gathering under partially observable, dynamic and sparsely sampled environments.
Artificial intelligence publications
Our new framework provides various measures of RL agent competence stemming from interestingness analysis and is applicable to a wide range of RL algorithms.
We simulate the process of corpus review and word list creation, showing that several simple interventions greatly improve recall as a function of simulated labor.
We present a framework for learning comprehensible models of sequential decision tasks in which agent strategies are characterized using temporal logic formulas.
We describe a large-scale experiment in which non-artificial intelligence subject matter experts (SMEs)—with neither artificial intelligence background nor extensive training in the task—author knowledge bases (KBs) following a challenge problem specification with a strong question-answering component.
Outcome-Guided Counterfactuals for Reinforcement Learning Agents from a Jointly Trained Generative Latent Space
We present a novel generative method for producing unseen and plausible counterfactual examples for reinforcement learning (RL) agents based upon outcome variables that characterize agent behavior.
We present VALET, a framework for rule-based information extraction written in Python. We show how a handful of rules suffices to implement sophisticated matching, and describe a user interface that facilitates exploration for development and maintenance of rule sets.
We present a new approach to dialogue specification for Virtual Personal Assistants (VPAs) based on so-called dialogue workflow graphs. Our approach relies on Semantic Web technology (OWL), implemented in Common Lisp with the help of the Racer reasoner.
The MetaFlux software supports creating, executing, and solving quantitative metabolic flux models using flux balance analysis (FBA).