EpistemeBase: A semantic memory system for task planning under uncertainties

Abstract

Tasks planning under uncertainties is one of fundamental skills for enabling autonomous robots to make proper manipulations in the complex environment. But owing to inexpressive representations, autonomous robots hardly conduct efficient tasks planning, especially in unknown conditions. The application of semantic knowledge in task planning is critically required in artificial intelligence research. In this paper, we focus on two topics: semantic knowledge representations and parallel planning for uncertainties. Firstly, a semantic memory system which is called EpistemeBase is proposed for indoor tasks planning, it includes five parallel agents: Assertion, Plan, Anticipation, Behaviour and Effect. Its framework is an evolving process, which consists of Datum, Information, Knowledge and Intelligence. Secondly, the same task planning is synchronously represented by five paralleled agents. This paralleled structure can well accelerate the process of tasks planning as well as better handle it under uncertainties. Finally, the experiment of tasks planning is conducted for measuring the reaction time of planning and uncertainties by using the EpistemeBase and the Open Mind Common Sense (OMCS) respectively.