• Improving the efficiency of robot task planning by automatically integrating its planner and common-sense knowledge base

      Al-Moadhen, Ahmed; Packianather, Michael; Qiu, Renxi; Setchi, Rossi; Ji, Ze; Cardiff School of Engineering (Springer Science and Business Media Deutschland GmbH, 2015-12-31)
      This chapter presents a newly developed approach for intelligently generating symbolic plans for mobile robots acting in domestic environments, such as offices and houses. The significance of this approach lies in its novel framework which consists of new modelling of high-level robot actions and their integration with common-sense knowledge in order to support robotic task planner. This framework will enable direct interactions between the task planner and the semantic knowledge base. By using common-sense domain knowledge, the task planner will take into consideration the properties and relations of objects and places in its environment, before creating semantically related actions that will represent a plan. A new module has been appended to the framework which is called Semantic Realization and Refreshment Module (SRRM). This module has the ability to discover and select entities in the robot’s world (entities related to robot plan) which are semantically equivalent or have a degree of similarity (where they don’t exceed a predefined threshold) by using techniques and standards (metrics) for similarities. SRRM supports robotic task planning to generate approximate plans to solve its tasks when there is no exact plan can be generated according to initial and goal state by extending initial state and action details with similar or equivalent objects. The extended framework enables direct interactions between task planner, Semantic Action Models (SAMs) and knowledge-base through creating planning domain (or extended planning domain) with predicates (or semantically equivalent or similar predicates) which specify domain features. The proposed framework and approach are tested on some scenarios that cover most aspects of robot planning system.
    • Robot task planning in deterministic and probabilistic conditions using semantic knowledge base

      Al-Moadhen, Ahmed Abdulhadi; Packianather, Michael; Setchi, Rossitza; Qiu, Renxi; Cardiff University; University of Bedfordshire (IGI Global, 2016-01-01)
      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.