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Emre Savas

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2 papers

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AAAI Conference 2019 Conference Paper

Efficiently Reasoning with Interval Constraints in Forward Search Planning

Amanda Coles
Andrew Coles
Moises Martinez
Emre Savas
Juan Manuel Delfa
Tomás de la Rosa
Yolanda E-Martín
Angel García-Olaya

In this paper we present techniques for reasoning natively with quantitative/qualitative interval constraints in statebased PDDL planners. While these are considered important in modeling and solving problems in timeline based planners; reasoning with these in PDDL planners has seen relatively little attention, yet is a crucial step towards making PDDL planners applicable in real-world scenarios, such as space missions. Our main contribution is to extend the planner OPTIC to reason natively with Allen interval constraints. We show that our approach outperforms both MTP, the only PDDL planner capable of handling similar constraints and a compilation to PDDL 2. 1, by an order of magnitude. We go on to present initial results indicating that our approach is competitive with a timeline based planner on a Mars rover domain, showing the potential of PDDL planners in this setting.

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ECAI Conference 2016 Conference Paper

Planning Using Actions with Control Parameters

Emre Savas
Maria Fox 0001
Derek Long
Daniele Magazzeni

Although PDDL is an expressive modelling language, a significant limitation is imposed on the structure of actions: the parameters of actions are restricted to values from finite (in fact, explicitly enumerated) domains. There is one exception to this, introduced in PDDL2. 1, which is that durative actions may have durations that are chosen (possibly subject to explicit constraints in the action models) by the planner. A motivation for this limitation is that it ensures that the set of grounded actions is finite and, ignoring duration, the branching factor of action choices at a state is therefore finite. Although the duration parameter can make this choice infinite, very few planners support this possibility, but restrict themselves to durative actions with fixed durations. In this paper we motivate a proposed extension to PDDL to allow actions with infinite domain parameters, which we call control parameters. We illustrate reasons for using this modelling feature and then describe a planning approach that can handle domains that exploit it, implemented in a new planner, POPCORN (Partial-Order Planning with Constrained Real Numerics). We show that this approach scales to solve interesting problems.

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