Global Temporal Movement Control

Complex movements require the fine-tuned temporal interplay of several effectors. If the temporal properties of one of these effectors were distorted, all other movement plans would need to be updated in order to produce successful behavior. This requirement of a global motor time stands in direct contrast to the multiple duration-channels in visual time. We explored whether time-critical and goal-oriented movements are indeed globally affected by temporal recalibration. In a ready-set-go paradigm, participants reproduced the interval between ready- and set-signals by performing different movements in Virtual Reality (VR). Halfway through the experiments, movements in VR were artificially slowed down, so that participants had to adapt their behavior. In three experiments, we found that these adaptation effects were not affected by movement type, interval range, location, or environmental context. We conclude that the temporal planning of motor actions is recalibrated globally, suggesting the presence of a global temporal movement controller.

Trading Plan Cost for Timeliness in Situated Temporal Planning

If a planning agent is considering taking a bus, for example, the time that passes during its planning can affect the feasibility of its plans, as the bus may depart before the agent has found a complete plan. Previous work on this situated temporal planning setting proposed an abstract deliberation scheduling scheme for maximizing the probability of finding a plan that is still feasible at the time it is found. In this paper, we extend the deliberation scheduling approach to address problems in which plans can differ in their cost. Like the planning deadlines, these costs can be uncertain until a complete plan has been found. We show that finding a deliberation policy that minimizes expected cost is PSPACE-hard and that even for known costs and deadlines the optimal solution is a contingent, rather than sequential, schedule. We then analyze special cases of the problem and use these results to propose a greedy scheme that considers both the uncertain deadlines and costs. Our empirical evaluation shows that the greedy scheme performs well in practice on a variety of problems, including some generated from planner search trees.

Temporal Planning with Intermediate Conditions and Effects

Automated temporal planning is the technology of choice when controlling systems that can execute more actions in parallel and when temporal constraints, such as deadlines, are needed in the model. One limitation of several action-based planning systems is that actions are modeled as intervals having conditions and effects only at the extremes and as invariants, but no conditions nor effects can be specified at arbitrary points or sub-intervals.In this paper, we address this limitation by providing an effective heuristic-search technique for temporal planning, allowing the definition of actions with conditions and effects at any arbitrary time within the action duration. We experimentally demonstrate that our approach is far better than standard encodings in PDDL 2.1 and is competitive with other approaches that can (directly or indirectly) represent intermediate action conditions or effects.

Decidability and Complexity of Action-Based Temporal Planning over Dense Time

This paper studies the computational complexity of temporal planning, as represented by PDDL 2.1, interpreted over dense time. When time is considered discrete, the problem is known to be EXPSPACE-complete. However, the official PDDL 2.1 semantics, and many implementations, interpret time as a dense domain. This work provides several results about the complexity of the problem, studying a few interesting cases: whether a minimum amount ϵ of separation between mutually exclusive events is given, in contrast to the separation being simply required to be non-zero, and whether or not actions are allowed to overlap already running instances of themselves. We prove the problem to be PSPACE-complete when self-overlap is forbidden, whereas, when allowed, it becomes EXPSPACE-complete with ϵ-separation and undecidable with non-zero separation. These results clarify the computational consequences of different choices in the definition of the PDDL 2.1 semantics, which were vague until now.