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Controller Synthesis for Timeline-based Games

Renato Acampora, Luca Geatti, Nicola Gigante, Angelo Montanari, Valentino Picotti

TL;DR

This paper provides an effective and computationally optimal approach to controller synthesis for timeline-based games by providing an effective and computationally optimal approach to controller synthesis for timeline-based games.

Abstract

In the timeline-based approach to planning, the evolution over time of a set of state variables (the timelines) is governed by a set of temporal constraints. Traditional timeline-based planning systems excel at the integration of planning with execution by handling temporal uncertainty. In order to handle general nondeterminism as well, the concept of timeline-based games has been recently introduced. It has been proved that finding whether a winning strategy exists for such games is 2EXPTIME-complete. However, a concrete approach to synthesize controllers implementing such strategies is missing. This paper fills this gap, by providing an effective and computationally optimal approach to controller synthesis for timeline-based games.

Controller Synthesis for Timeline-based Games

TL;DR

This paper provides an effective and computationally optimal approach to controller synthesis for timeline-based games by providing an effective and computationally optimal approach to controller synthesis for timeline-based games.

Abstract

In the timeline-based approach to planning, the evolution over time of a set of state variables (the timelines) is governed by a set of temporal constraints. Traditional timeline-based planning systems excel at the integration of planning with execution by handling temporal uncertainty. In order to handle general nondeterminism as well, the concept of timeline-based games has been recently introduced. It has been proved that finding whether a winning strategy exists for such games is 2EXPTIME-complete. However, a concrete approach to synthesize controllers implementing such strategies is missing. This paper fills this gap, by providing an effective and computationally optimal approach to controller synthesis for timeline-based games.
Paper Structure (16 sections, 11 theorems, 13 equations, 4 figures)

This paper contains 16 sections, 11 theorems, 13 equations, 4 figures.

Key Result

Theorem 4.6

Let $P=(\mathsf{SV}\xspace, S)$ be a timeline-based planning problem and let $\mathsf{A}\xspace_P$ be the associated automaton. Then, the size of $A_P$ is at most doubly-exponential in the size of $P$.

Figures (4)

  • Figure 1: The constraint system of \ref{['eq:example-3var-synch']}.
  • Figure 2: DBM of \ref{['eq:example-3var-synch']}. Missing entries are intended to be $+\infty$.
  • Figure 3: Example of timelines for variables $x_0, \, x_1, \, x_2, \, x_3$.
  • Figure 4: On the left, the removal of transitions ${\mu}\xspace=(A,\delta)$ with $\delta>1$ and ending actions of controllable tokens in $A$. On the right, the transformation of a transition of $A_G$ into a sequence of transitions in $A^a_G$, with $x\in\mathsf{SV}\xspace_C$, $y\in\mathsf{SV}\xspace_E$, and $\gamma_x(v_1)=\gamma_y(w_1)=\mathsf{u}$.

Theorems & Definitions (44)

  • Definition 3.1: State variable
  • Definition 3.2
  • Definition 3.3: Event sequence GiganteMOCR20
  • Definition 3.4: Atom
  • Definition 3.5: Synchronization rule
  • Definition 3.6: Matching functions Gigante19
  • Definition 3.7: Semantics of synchronization rules
  • Definition 3.8: Timeline-based planning problem
  • Definition 3.9: Timeline-based game
  • Definition 3.10: Partition of player actions
  • ...and 34 more