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AAAI 2013

Timelines with Temporal Uncertainty

Conference Paper Papers Artificial Intelligence

Abstract

Timelines are a formalism to model planning domains where the temporal aspects are predominant, and have been used in many real-world applications. Despite their practical success, a major limitation is the inability to model temporal uncertainty, i. e. the fact that the plan executor cannot decide the actual duration of some activities. In this paper we make two key contributions. First, we propose a comprehensive, semantically well founded framework that (conservatively) extends with temporal uncertainty the state of the art timeline approach. Second, we focus on the problem of producing time-triggered plans that are robust with respect to temporal uncertainty, under a bounded horizon. In this setting, we present the first complete algorithm, and we show how it can be made practical by leveraging the power of Satisfiability Modulo Theories.

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Context

Venue
AAAI Conference on Artificial Intelligence
Archive span
1980-2026
Indexed papers
28718
Paper id
752407152352515924