The Local Definability of Robotic Large-Scale Knowledge Based on Splitting

Abstract

In order to reduce the computational tasks in robots with large-scale and complex knowledge, several methods of robotic knowledge localization have been proposed over the past decades. Logic is an important and useful tool for complex robotic reasoning, action planning, learning and verification. This paper uses propositional atoms in logic to describe the affecting factors of robotic large-scale knowledge. Definability in logic reasoning shows that truths of some propositional atoms are decided by other propositional atoms. Definability technology is an important method to eliminate inessential propositional atoms in robotic large-scale and complex knowledge, so the computational tasks in robotic knowledge can be completed faster. On the other hand, by applying the splitting technique, the knowledge base can be equivalently divided into a number of sub-knowledge bases, without sharing any propositional atoms with others. In this paper, we show that the inessential propositional atoms can be decided faster by the local definability technology based on the splitting method, first formed in local belief revision by Parikh in 1999. Hence, the decision-making in robotic large-scale and complex knowledge is more effective.