Robot Task Planning in Deterministic and Probabilistic Conditions Using Semantic Knowledge Base

Ahmed Abdulhadi Al-Moadhen, Michael Packianather, Rossitza Setchi and Renxi Qiu
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Ahmed Abdulhadi Al-Moadhen: Cardiff University, Cardiff, UK
Michael Packianather: School of Engineering, Cardiff University, Cardiff, UK
Rossitza Setchi: School of Engineering, Cardiff University, Cardiff, UK
Renxi Qiu: Department of Computer Science and Technology, University of Bedfordshire, Bedfordshire, UK

International Journal of Knowledge and Systems Science (IJKSS), 2016, vol. 7, issue 1, 56-77

Abstract: A new method is proposed to increase the reliability of generating symbolic plans by extending the Semantic-Knowledge Based (SKB) plan generation to take into account the amount of information and uncertainty related to existing objects, their types and properties, as well as their relationships with each other. This approach constructs plans by depending on probabilistic values which are derived from learning statistical relational models such as Markov Logic Networks (MLN). An MLN module is established for probabilistic learning and inference together with semantic information to provide a basis for plausible learning and reasoning services in support of robot task-planning. The MLN module is constructed by using an algorithm to transform the knowledge stored in SKB to types, predicates and formulas which represent the main building block for this module. Following this, the semantic domain knowledge is used to derive implicit expectations of world states and the effects of the action which is nominated for insertion into the task plan. The expectations are matched with MLN output.

Date: 2016
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