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Integrating Reinforcement Learning and Declarative Programming to Learn Causal Laws in Dynamic Domains

  • Mohan Sridharan
  • Sarah Rainge
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8755)

Abstract

Robots deployed to assist and collaborate with humans in complex domains need the ability to represent and reason with incomplete domain knowledge, and to learn from minimal feedback obtained from non-expert human participants. This paper presents an architecture that combines the complementary strengths of Reinforcement Learning (RL) and declarative programming to support such commonsense reasoning and incremental learning of the rules governing the domain dynamics. Answer Set Prolog (ASP), a declarative language, is used to represent domain knowledge. The robot’s current beliefs, obtained by inference in the ASP program, are used to formulate the task of learning previously unknown domain rules as an RL problem. The learned rules are, in turn, encoded in the ASP program and used to plan action sequences for subsequent tasks. The architecture is illustrated and evaluated in the context of a simulated robot that plans action sequences to arrange tabletop objects in desired configurations.

Keywords

Reinforcement Learn Knowledge Representation Incremental Learning Domain Dynamic Simulated Robot 
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

  • Mohan Sridharan
    • 1
  • Sarah Rainge
    • 2
  1. 1.Department of Electrical and Computer EngineeringThe University of AucklandNZ
  2. 2.Department of Computer ScienceTexas Tech UniversityUSA

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