Probabilistic Networks for Automated Reasoning

University Of California-Los Angeles, Los Angeles CA

Investigators

Abstract

The ultimate aim of this investigation is to develop computer systems capable of operating autonomously in dynamic and uncertain environments. Specifically, the investigation comprises theoretical and experimental studies in the following areas: (1) Information fusion, situation assessment, diagnosis, and planning under uncertainty using causal and counterfactual relationships; (2) Automatic generation of natural language explanations of actions, recommendations, and unexpected eventualities; and (3) Learning causal structures from data to facilitate predictions and decisions in data-intensive applications. Priority will be given to new methods of generation explanatory sentences and to structural learning tasks in molecular biology. The theoretical part of the research is fundamental to our understanding the structure of human knowledge and scientific inquiry, and could have far-reaching broader impact in the fields of epidemiology, social science, economics, medicine and biology, where causal knowledge plays a major role.