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Robot task planning in deterministic and probabilistic conditions using semantic knowledge base

  • Ahmed Abdulhadi Al-Moadhen
    ,
  • Michael Packianather
    ,
  • Rossitza Setchi
    ,
  • Renxi Qiu
  • Cardiff University
Research Output: Contribution to journal Article Peer-review

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.

Publication Information

Output type

Research Output: Contribution to journal Article Peer-review

Original language

English

Pages from-to (Number of pages)

Pages 56-77

Journal (Volume, Issue Number)

International Journal of Knowledge and Systems Science (Volume 7, Issue 1)

Publication milestones

  • Published - 01/01/2016

Publication status

Published - 01/01/2016

ISSN

1947-8208

External Publication IDs

  • handle.net: 10547/623003

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