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Al-Moadhen, Ahmed Abdulhadi; Packianather, Michael; Setchi, Rossitza; Qiu, Renxi (2015)
Publisher: IGI Global
Languages: English
Types: Article
Subjects: TJ, TS
New method is proposed to increase the reliability of generating symbolic plans by extending the\ud Semantic-Knowledge Based (SKB) plan generation to take into account the amount of information and\ud uncertainty related to existing objects, their types and properties, as well as their relationships with\ud each other. This approach constructs plans by depending on probabilistic values which are derived\ud from learning statistical relational models such as Markov Logic Networks (MLN). An MLN module\ud is established for probabilistic learning and inference together with semantic information to provide a\ud basis for plausible learning and reasoning services in support of robot task-planning. The MLN module\ud is constructed by using an algorithm to transform the knowledge stored in SKB to types, predicates\ud and formulas which represent the main building block for this module. Following this, the semantic\ud domain knowledge is used to derive implicit expectations of world states and the effects of the action\ud which is nominated for insertion into the task plan. The expectations are matched with MLN output.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

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