Storing and Predicting Dynamic Attributes in a World Model Knowledge Store

摘要

The world is an ever-changing, dynamic environment. If robots and other intelligent systems are to find ways to cope with and reason about the world adequately, they must be capable of understanding these dynamic features. This dissertation examines the need for a centralized knowledge store capable of storing information that is both spatial and temporal in nature. The interface of a new and unique architecture to handle the exchange of dynamic information and questions about the future state of that information is presented. A novel algorithm, called the Statistics-Based Nth Order Polynomial Predictor (SNOPP), is also developed which allows state prediction of almost any time-variant data.