Dynamic plan execution strategies allow an autonomous agent to respond to uncertainties while improving robustness and reducing the need for an overly conservative plan. Executives have improved this robustness by expanding the types of choices made dynamically, such as selecting alternate methods. However, in methods to date, these additional choices introduce substantial run-time latency. This paper presents a novel system called Drake that makes steps towards executing an expanded set of choices dynamically without significant latency. Drake frames a plan as a Disjunctive Temporal Problem and executes it with a fast dynamic scheduling algorithm. Prior work demonstrated an efficient technique

This work presents Drake, a dynamic executive for temporal plans with choice. Dynamic plan execution strategies allow an autonomous agent to react quickly to unfolding events, improving the robustness of the agent. Prior work developed methods for dynamically dispatching Simple Temporal Networks, and further research enriched the expressiveness of the plans executives could handle, including discrete choices, which are the focus of this work. However, in some approaches to date, these additional choices induce significant storage or latency requirements to make flexible execution possible. Drake is designed to leverage the low latency made possible by a preprocessing step called compilation, while avoiding high memory costs through a compact representation. We leverage the concepts of labels and environments, taken from prior work in Assumptionbased Truth Maintenance Systems (ATMS), to concisely record the implications of the discrete choices, exploiting the structure of the plan to avoid redundant reasoning or storage. Our labeling and maintenance scheme, called the Labeled Value Set Maintenance System, is distinguished by its focus on properties fundamental to temporal problems, and, more generally, weighted graph algorithms. In particular, the maintenance system focuses on maintaining a minimal representation of non-dominated constraints. We benchmark Drake’s performance on random structured problems, and find that Drake reduces the size of the compiled representation by a factor of over 500 for large problems, while incurring only a modest increase in run-time latency, compared to prior work in compiled executives for temporal plans with discrete choices. 1.

In this paper we examine a collection of related incremental constraint-posting algorithms for temporal planning and for planning with continuous processes. The basis for these algorithms is an incremental version of the Bellman-Ford single-source shortest-path algorithm for consistency checking Simple Temporal Networks (STNs). We extend an existing incremental algorithm for STNs and then proceed to show how this algorithm plays a key role in temporal planning by a forward-chaining strategy, interleaving action choice with action scheduling. We go on to consider the more complex problem of temporal planning with continuous linear processes and show how the incremental STN algorithm can be integrated with a linear program (LP) solver, to achieve an efficient incremental constraint-posting algorithm for use in a forward-search planner. We demonstrate empirically that the incremental algorithms improve performance in both temporal and temporal and numeric settings. 1

We present two new and efficient algorithms for computing all-pairs shortest paths. The algorithms operate on directed graphs with real (possibly negative) weights. They make use of directed path consistency along a vertex ordering d. Both algorithms run in O(n²) wd time, where wd is the graph width induced by this vertex ordering. For graphs of constant treewidth, this yields O(n²) time, which is optimal. On chordal graphs, the algorithms run in O (nm) time. In addition, we present a variant that exploits graph separators to arrive at a run time of O(nw² d + n²) sd on general graphs, where sd ≤ wd is the size of the largest minimal separator induced by the vertex ordering d. We show empirically that on both constructed and realistic benchmarks, in many cases the algorithms outperform Floyd–Warshall’s as well as Johnson’s algorithm, which represent the current state of the art with a run time of O(n³) and O(nm + n² log n) , respectively. Our algorithms can be used for spatial and temporal reasoning, such as for the Simple Temporal Problem, which underlines their relevance to the planning and scheduling community.