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Tomás de la Rosa

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8 papers

2 author rows

AIJ Journal 2021 Journal Article

Selecting goals in oversubscription planning using relaxed plans

Angel García-Olaya
Tomás de la Rosa
Daniel Borrajo

Planning deals with the task of finding an ordered set of actions that achieves some goals from an initial state. In many real-world applications it is unfeasible to find a plan achieving all goals due to limitations in the available resources. A common case consists of having a bound on a given cost measure that is less than the optimal cost needed to achieve all goals. Oversubscription planning (OSP) is the field of Automated Planning dealing with such kinds of problems. Usually, OSP generates plans that achieve only a subset of the goals set. In this paper we present a new technique to a priori select goals in no-hard-goals satisficing OSP by searching in the space of subsets of goals. A key property of the proposed approach is that it is planner-independent once the goals have been selected; it creates a new non-OSP problem that can be solved using off-the-shelf planners. Extensive experimental results show that the proposed approach outperforms state-of-the-art OSP techniques in several domains of the International Planning Competition.

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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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ICAPS Conference 2017 Conference Paper

Performance Modelling of Planners from Homogeneous Problem Sets

Tomás de la Rosa
Isabel Cenamor
Fernando Fernández 0001

Empirical performance models play an important role in the development of planning portfolios that make a per-domain or per-problem configuration of its search components. Even though such portfolios have shown their power when compared to other systems in current benchmarks, there is no clear evidence that they are capable to differentiate problems (instances) having similar input properties (in terms of objects, goals, etc.) but fairly different runtime for a given planner. In this paper we present a study of empirical performance models that are trained using problems having the same configuration, with the objective of guiding the models to recognize the underlying differences existing among homogeneous problems. In addition we propose a set of new features that boost the prediction capabilities under such scenarios. The results show that the learned models clearly performed over random classifiers, which reinforces the hypothesis that the selection of planners can be done on a per-instance basis when configuring a portfolio.

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JAIR Journal 2016 Journal Article

The IBaCoP Planning System: Instance-Based Configured Portfolios

Isabel Cenamor
Tomás de la Rosa
Fernando Fernández

Sequential planning portfolios are very powerful in exploiting the complementary strength of different automated planners. The main challenge of a portfolio planner is to define which base planners to run, to assign the running time for each planner and to decide in what order they should be carried out to optimize a planning metric. Portfolio configurations are usually derived empirically from training benchmarks and remain fixed for an evaluation phase. In this work, we create a per-instance configurable portfolio, which is able to adapt itself to every planning task. The proposed system pre-selects a group of candidate planners using a Pareto-dominance filtering approach and then it decides which planners to include and the time assigned according to predictive models. These models estimate whether a base planner will be able to solve the given problem and, if so, how long it will take. We define different portfolio strategies to combine the knowledge generated by the models. The experimental evaluation shows that the resulting portfolios provide an improvement when compared with non-informed strategies. One of the proposed portfolios was the winner of the Sequential Satisficing Track of the International Planning Competition held in 2014.

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ECAI Conference 2014 Conference Paper

Planning with Ensembles of Classifiers

Alberto Garbajosa
Tomás de la Rosa
Raquel Fuentetaja 0001

Learning search control for forward state planning has been previously addressed as a relational classification task, where predictions are used to generate action policies. In this paper, we describe a new bagging approach to learn and apply ensembles of relational decision trees to generate more robust policies for planning. Preliminary experimental results demonstrate that new policies produce on average plans of better quality.

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KER Journal 2013 Journal Article

Using automated planning for improving data mining processes

Susana Fernández
Tomás de la Rosa
Fernando Fernández
Rubén Suárez
Javier Ortiz
Daniel Borrajo
David Manzano

Abstract This paper presents a distributed architecture for automating data mining (DM) processes using standard languages. DM is a difficult task that relies on an exploratory and analytic process of processing large quantities of data in order to discover meaningful patterns. The increasing heterogeneity and complexity of available data requires some expert knowledge on how to combine the multiple and alternative DM tasks to process the data. Here, we describe DM tasks in terms of Automated Planning, which allows us to automate the DM knowledge flow construction. The work is based on the use of standards that have been defined in both DM and automated-planning communities. Thus, we use PMML (Predictive Model Markup Language) to describe DM tasks. From the PMML, a problem description in PDDL (Planning Domain Definition Language) can be generated, so any current planning system can be used to generate a plan. This plan is, again, translated to a DM workflow description, Knowledge Flow for Machine Learning format (Knowledge Flow file for the WEKA (Waikato Environment for Knowledge Analysis) tool), so the plan or DM workflow can be executed in WEKA.

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KER Journal 2012 Journal Article

A review of machine learning for automated planning

Sergio Jiménez
Tomás de la Rosa
Susana Fernández
Fernando Fernández
Daniel Borrajo

Abstract Recent discoveries in automated planning are broadening the scope of planners, from toy problems to real applications. However, applying automated planners to real-world problems is far from simple. On the one hand, the definition of accurate action models for planning is still a bottleneck. On the other hand, off-the-shelf planners fail to scale-up and to provide good solutions in many domains. In these problematic domains, planners can exploit domain-specific control knowledge to improve their performance in terms of both speed and quality of the solutions. However, manual definition of control knowledge is quite difficult. This paper reviews recent techniques in machine learning for the automatic definition of planning knowledge. It has been organized according to the target of the learning process: automatic definition of planning action models and automatic definition of planning control knowledge. In addition, the paper reviews the advances in the related field of reinforcement learning.

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ICAPS Conference 2008 Conference Paper

Learning Relational Decision Trees for Guiding Heuristic Planning

Tomás de la Rosa
Sergio Jiménez Celorrio
Daniel Borrajo

The current evaluation functions for heuristic planning are expensive to compute. In numerous domains these functions give good guidance on the solution, so it worths the computation effort. On the contrary, where this is not true, heuristics planners compute loads of useless node evaluations that make them scale-up poorly. In this paper we present a novel approach for boosting the scalability of heuristic planners based on automatically learning domain-specific search control knowledge in the form of relational decision trees. Particularly, we define the learning of planning search control as a standard classification process. Then, we use an off-the-shelf relational classifier to build domain-specific relational decision trees that capture the preferred action in the different planning contexts of a planning domain. These contexts are defined by the set of helpful actions extracted from the relaxed planning graph of a given state, the goals remaining to be achieved, and the static predicates of the planning task. Additionally, we show two methods for guiding the search of a heuristic planner with relational decision trees. The first one consists of using the resulting decision trees as an action policy. The second one consists of ordering the node evaluation of the Enforced Hill Climbing algorithm with the learned decision trees. Experiments over a variety of domains from the IPC test-benchmarks reveal that in both cases the use of the learned decision trees increase the number of problems solved together with a reduction of the time spent.

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