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Compiling Away Uncertainty in Strong Temporal Planning with Uncontrollable Durations

AAAI Conferences

Real world temporal planning often involves dealing with uncertainty about the duration of actions. In this paper, we describe a sound-and-complete compilation technique for strong planning that reduces any planning instance with uncertainty in the duration of actions to a plain temporal planning problem without uncertainty. We evaluate our technique by comparing it with a recent technique for PDDL domains with temporal uncertainty. The experimental results demonstrate the practical applicability of our approach and show complementary behavior with respect to previous techniques. We also demonstrate the high expressiveness of the translation by applying it to a significant fragment of the ANML language.


Strong Temporal Planning with Uncontrollable Durations: A State-Space Approach

AAAI Conferences

In many practical domains, planning systems are required to reason about durative actions. A common assumption in the literature is that the executor is allowed to decide the duration of each action. However, this assumption may be too restrictive for applications. In this paper, we tackle the problem of temporal planning with uncontrollable action durations. We show how to generate robust plans,that guarantee goal achievement despite the uncontrollability of the actual duration of the actions. We extend the state-space temporalplanning framework, integrating recent techniques for solving temporalproblems under uncertainty. We discuss different ways of lifting the total order plans generated by the heuristic search to partial orderplans, showing (in)completeness results for each of them. We implemented our approach on top of COLIN, a state-of-the-art planner. An experimental evaluation over several benchmark problems shows the practical feasibility of the proposed approach.


Probabilistic Temporal Planning with Uncertain Durations

AAAI Conferences

Few temporal planners handle both concurrency and uncertain durations, but these features commonly cooccur in realworld domains. In this paper, we discuss the challenges caused by concurrent, durative actions whose durations are uncertain.


Hofmann

AAAI Conferences

Planning in an on-line robotics context has the specific requirement of a short planning duration. A property of typical contemporary scenarios is that (mobile) robots perform similar or even repeating tasks during operation. With these robot domains in mind, we propose database-driven macroplanning for STRIPS (DBMP/S) that learns macros – action sequences that frequently appear in plans – from experience for PDDL-based planners. Planning duration is improved over time by off-line processing of seed plans using a scalable database. The approach is indifferent about the specific planner by representing the resulting macros again as actions with preconditions and effects determined based on the actions contained in the macro. For some domains we have used separate planners for learning and execution exploiting their respective strengths. Initial results based on some IPC domains and a logistic robot scenario show significantly improved (over non-macro planners) or slightly better and comparable (to existing macro planners) performance.


Statement of Interest for Workshop on Integrating Planning Into Scheduling: Temporal Contingency Planning

AAAI Conferences

Department of Computer Science Michigan Technological University 1400 Townsend Drive Houghton, MI 49931 {jnfoss,nilufer}@mtu.edu In classical planning assumptions are made that ignore important aspects of the real world. As a result, many planners have been written that extend classical planning in various ways. Often times these extensions to classical planning incorporate aspects of scheduling such as optimization and reasoning about resources and time. For example, there has been considerable research with planners that allow durative actions (Smith & Weld 1999). Uncertainty is an aspect of the real world that is being studied in both planning and scheduling communities.