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@PHDTHESIS{Hofmann:972976,
      author       = {Hofmann, Till},
      othercontributors = {Lakemeyer, Gerhard and Lespérance, Yves},
      title        = {{T}owards bridging the gap between high-level reasoning and
                      execution on robots},
      school       = {RWTH Aachen University},
      type         = {Dissertation},
      address      = {Aachen},
      publisher    = {RWTH Aachen University},
      reportid     = {RWTH-2023-10508},
      pages        = {1 Online-Ressource : Illustrationen},
      year         = {2023},
      note         = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
                      University; Dissertation, RWTH Aachen University, 2023},
      abstract     = {When reasoning about actions, e.g., by means of task
                      planning or agent programming with Golog, the robot's
                      actions are typically modeled on an abstract level, where
                      complex actions such as picking up an object are treated as
                      atomic primitives with deterministic effects and
                      preconditions that only depend on the current state.
                      However, when executing such an action on a robot it can no
                      longer be seen as a primitive. Instead, action execution is
                      a complex task involving multiple steps with additional
                      temporal preconditions and timing constraints. Furthermore,
                      the action may be noisy, e.g., producing erroneous sensing
                      results and not always having the desired effects. While
                      these aspects are typically ignored in reasoning tasks, they
                      need to be dealt with during execution. In this thesis, we
                      propose several approaches towards closing this gap. Based
                      on a logic that combines the situation calculus with metric
                      time and metric temporal logic, we model the robot platform
                      with timed automata and temporal constraints to describe the
                      connection between the high-level actions and the robot
                      platform. We then describe two approaches towards
                      transforming the high-level program. First, we view the
                      transformation as a synthesis problem, where the task is to
                      synthesize a controller that executes the program while
                      satisfying the specification, independent of the
                      environment's choices. We show that the synthesis problem is
                      decidable, describe an algorithm to construct a controller,
                      and evaluate the approach in two robotics scenarios. While
                      this approach supports controlling arbitrary Golog programs
                      against any specification with timing constraints, it does
                      not scale well. For this reason, we describe a second
                      approach based on some simplifying assumptions which allow
                      us to view the transformation problem as a reachability
                      problem on timed automata, which can be solved with
                      state-of-the-art tools. We demonstrate the effectiveness and
                      scalability of the approach in a number of scenarios.
                      Finally, we turn towards noisy sensors and effectors. Based
                      on DS, a probabilistic variant of the situation calculus
                      that allows modeling the agent's degree of belief, we
                      describe an abstraction framework for Golog programs with
                      noisy actions. In this framework, a high-level and
                      non-stochastic program is mapped to a more detailed and
                      stochastic low-level program. As the high-level program is
                      non-stochastic, we may use non-probabilistic reasoning
                      methods such as task planning or classical Golog program
                      execution. At the same time, by mapping the abstract actions
                      to low-level programs, we may still deal with uncertainty
                      during execution. We define a suitable notion of
                      bisimulation that guarantees the equivalence between the
                      high-level and low-level programs and demonstrate the
                      approach with an example.},
      cin          = {121920 / 120000 / 080060},
      ddc          = {004},
      cid          = {$I:(DE-82)121920_20140620$ / $I:(DE-82)120000_20140620$ /
                      $I:(DE-82)080060_20170720$},
      pnm          = {TAILOR - Foundations of Trustworthy AI - Integrating
                      Reasoning, Learning and Optimization (952215) / DFG project
                      288705857 - Constraint-basierte Transformation abstrakter
                      Handlungspläne in ausführbare Aktionen autonomer Roboter
                      (288705857) / GRK 2236 - GRK 2236: Unsicherheit und
                      Randomisierung in Algorithmen, Verifikation und Logik.
                      (282652900)},
      pid          = {G:(EU-Grant)952215 / G:(GEPRIS)288705857 /
                      G:(GEPRIS)282652900},
      typ          = {PUB:(DE-HGF)11},
      doi          = {10.18154/RWTH-2023-10508},
      url          = {https://publications.rwth-aachen.de/record/972976},
}