Learning Task Knowledge and its Scope of Applicability in
  Experience-Based Planning Domains
  Experience-based planning domains (EBPDs) have been recently proposed to
improve problem solving by learning from experience. EBPDs provide important
concepts for long-term learning and planning in robotics. They rely on
acquiring and using task knowledge, i.e., activity schemata, for generating
concrete solutions to problem instances in a class of tasks. Using Three-Valued
Logic Analysis (TVLA), we extend previous work to generate a set of conditions
as the scope of applicability for an activity schema. The inferred scope is a
bounded representation of a set of problems of potentially unbounded size, in
the form of a 3-valued logical structure, which allows an EBPD system to
automatically find an applicable activity schema for solving task problems. We
demonstrate the utility of our approach in a set of classes of problems in a
simulated domain and a class of real world tasks in a fully physically
simulated PR2 robot in Gazebo.
