Author name cluster

Yolanda E-Martín

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5 papers

2 author rows

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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ICAPS Conference 2019 Conference Paper

Finding Centroids and Minimum Covering States in Planning

Alberto Pozanco
Yolanda E-Martín
Susana Fernández
Daniel Borrajo

In automated planning, the most common task consists of finding a plan that achieves a set of goals. In this paper, we focus on a different task; that of finding states that minimize some goal-related metric. First, we present some domains for which that task is useful. Second, we propose two of such types of states: (1) centroid states, which minimize the distance to all the goals in the problem; and (2) minimum covering states, which minimize the maximum distance to any of the goals. Third, we define optimal and suboptimal algorithms to find such states. Finally, we show some experimental results in planning instances from different domains.

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IJCAI Conference 2019 Conference Paper

Guarantees for Sound Abstractions for Generalized Planning

Blai Bonet
Raquel Fuentetaja
Yolanda E-Martín
Daniel Borrajo

Generalized planning is about finding plans that solve collections of planning instances, often infinite collections, rather than single instances. Recently it has been shown how to reduce the planning problem for generalized planning to the planning problem for a qualitative numerical problem; the latter being a reformulation that simultaneously captures all the instances in the collection. An important thread of research thus consists in finding such reformulations, or abstractions, automatically. A recent proposal learns the abstractions inductively from a finite and small sample of transitions from instances in the collection. However, as in all inductive processes, the learned abstraction is not guaranteed to be correct for the whole collection. In this work we address this limitation by performing an analysis of the abstraction with respect to the collection, and show how to obtain formal guarantees for generalization. These guarantees, in the form of first-order formulas, may be used to 1) define subcollections of instances on which the abstraction is guaranteed to be sound, 2) obtain necessary conditions for generalization under certain assumptions, and 3) do automated synthesis of complex invariants for planning problems. Our framework is general, it can be extended or combined with other approaches, and it has applications that go beyond generalized planning.

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IJCAI Conference 2018 Conference Paper

Counterplanning using Goal Recognition and Landmarks

Alberto Pozanco
Yolanda E-Martín
Susana Fernández
Daniel Borrajo

In non-cooperative multi-agent systems, agents might want to prevent the opponents from achieving their goals. One alternative to solve this task would be using counterplanning to generate a plan that allows an agent to block other's to reach their goals. In this paper, we introduce a fully automated domain-independent approach for counterplanning. It combines; goal recognition to infer an opponent's goal; landmarks' computation to identify subgoals that can be used to block opponents' goals achievement; and classical automated planning to generate plans that prevent the opponent's goals achievement. Experimental results in several domains show the benefits of our novel approach.

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AAAI Conference 2011 Conference Paper

Probabilistic Plan Graph Heuristic for Probabilistic Planning

Yolanda E-Martín
Maria R-Moreno
David Smith

This work focuses on developing domain-independent heuristics for probabilistic planning problems characterized by full observability and non-deterministic effects of actions that are expressed by probability distributions. The approach is to ﬁrst search for a high probability deterministic plan using a classical planner. A novel probabilistic plan graph heuristic is used to guide the search towards high probability plans. The resulting plans can be used in a system that handles unexpected outcomes by runtime replanning. The plans can also be incrementally augmented with contingency branches for the most critical action outcomes. This abstract will describe the steps that we have taken in completing the above work and the obtained results.

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