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.


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.


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.


Inference in Hidden Markov Models with Explicit State Duration Distributions

arXiv.org Machine Learning

In this letter we borrow from the inference techniques developed for unbounded state-cardinality (nonparametric) variants of the HMM and use them to develop a tuning-parameter free, black-box inference procedure for Explicit-state-duration hidden Markov models (EDHMM). EDHMMs are HMMs that have latent states consisting of both discrete state-indicator and discrete state-duration random variables. In contrast to the implicit geometric state duration distribution possessed by the standard HMM, EDHMMs allow the direct parameterisation and estimation of per-state duration distributions. As most duration distributions are defined over the positive integers, truncation or other approximations are usually required to perform EDHMM inference.