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.
Highlights
With the rapid worldwide development of science and technology over the past decades, an increasing number of intelligent robots have been designed by scientists to help people work in hazardous environments
Due to the real-time knowledge-based exchange in multi-cloud robotic sys‐ tems[7], each robot in multi-cloud robotic systems working in a hazardous environment needs to exchange localization knowledge with the cloud system and other cloud robots to satisfy the computational and storage tasks
In order to simplify the representation of our localization method in large-scale and complex robotic knowledge, we describe robotic knowledge by propositional logic in this paper
Summary
With the rapid worldwide development of science and technology over the past decades, an increasing number of intelligent robots have been designed by scientists to help people work in hazardous environments. This paper shows the definability technique based on the splitting method, first proposed by Parikh [14] in 1999 for the localization in belief revision [15, 16], which can quickly decide which are the inessential propositional atoms. Tractability is not granted even if the language is restricted to Horn formulas, given that the inference problem with Horn clauses is decidable in deterministic polynomial time It was shown by Eiter and Gottlob that for those formula-based opera‐ tors, the complexity of knowledge base change can be coNP-hard even in the case that the knowledge bases are represented in Horn formulas and the size of input infor‐ mation is bound by a constant. We present some interesting results on local definability of robotic knowl‐ edge using propositional logic in order to increase the efficiency of the computational and store task.
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