Towards an Architecture for Knowledge Representation and Reasoning in Robotics

  • Shiqi Zhang
  • Mohan Sridharan
  • Michael Gelfond
  • Jeremy Wyatt
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8755)


This paper describes an architecture that combines the complementary strengths of probabilistic graphical models and declarative programming to enable robots to represent and reason with qualitative and quantitative descriptions of uncertainty and domain knowledge. An action language is used for the architecture’s low-level (LL) and high-level (HL) system descriptions, and the HL definition of recorded history is expanded to allow prioritized defaults. For any given objective, tentative plans created in the HL using commonsense reasoning are implemented in the LL using probabilistic algorithms, and the corresponding observations are added to the HL history. Tight coupling between the levels helps automate the selection of relevant variables and the generation of policies in the LL for each HL action, and supports reasoning with violation of defaults, noisy observations and unreliable actions in complex domains. The architecture is evaluated in simulation and on robots moving objects in indoor domains.


Knowledge Representation Belief State Partially Observable Markov Decision Process Initial Belief Probabilistic Graphical Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Shiqi Zhang
    • 1
  • Mohan Sridharan
    • 2
  • Michael Gelfond
    • 3
  • Jeremy Wyatt
    • 4
  1. 1.Department of Computer ScienceThe University of Texas at AustinUSA
  2. 2.Department of Electrical and Computer EngineeringThe University of AucklandNZ
  3. 3.Department of Computer ScienceTexas Tech UniversityUSA
  4. 4.School of Computer ScienceUniversity of BirminghamUK

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