Dynamically extending planning models using an ontology

Michael Cashmore, Maria Fox, Derek Long, Daniele Magazzeni, Bram Ridder, Valerio De Carolis, David Lane, Francesco Maurelli

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

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In this paper we couple a deterministic planner with an ontology, in order to adapt to new discoveries during plan execution and to reason about the affordances that are available to the planner as the set of known objects is updated. This allows us to extend the planning agent’s functionality during execution. We use as an example planning for persistent autonomous behaviour in underwater vehicles. Planning in this scenario takes place in a symbolic model of the environment, simulating sequences of possible decisions. Ensuring that the simulation remains robust requires careful matching of the model to the real world, including dynamically updating the model from continuous sensing actions. We describe how our system constructs an initial state for planning, using the ontology; how the ontology is also used to determine the results of each action performed by the planner; and finally demonstrate the performance of the system in a simulation, in which two AUVs are required to cooperate in an unknown environment, demonstrating that with additional reasoning the planning system is able to make new efficient choices, taking advantage of the environment in new ways.
Original languageEnglish
Title of host publicationProceedings of the 2nd ICAPS Workshop on Planning and Robotics (PlanRob)
EditorsAlberto Finzi, Felix Ingrand, Andrea Orlandini
Place of Publication[Jerusalem]
Number of pages7
Publication statusPublished - 8 Jun 2015


  • AI planning
  • ontologies
  • autonomous underwater vehicles (AUVs)
  • artificial intelligence


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