Francesco Percassi

AAAI Conference 2026 Conference Paper

A Domain-specific Heuristic for PDDL+-based Traffic Signal Optimisation

• Francesco Doria
• Francesco Percassi
• Marco Maratea
• Mauro Vallati

Optimising traffic signals is crucial for mitigating urban congestion, and automated planning, particularly with PDDL+, has shown promise for real-world deployment due to its flexibility and centralised perspective. While existing PDDL+ models guarantee deployability on current infrastructure, they face significant limitations: reliance on domain-independent heuristics restricts their applicability and scalability, leading to slow solution generation and unclear plan quality. To overcome these challenges and unlock the widespread adoption of planning-based traffic control, we introduce hCAFE, a domain-specific heuristic for PDDL+-based traffic signal optimisation. Unlike prior approaches, hCAFE is designed to work effectively across multiple problem encodings, addressing a key limitation of traditional domain-specific heuristics. We demonstrate its capabilities on real-world data from a region of the UK, showing significant improvements in solution generation time and search space exploration. Our evaluation also compares the strategies generated by hCAFE against historical data from existing traffic control systems and a non-deployable benchmark, confirming the high quality of the resulting plans.

AAAI Conference 2026 Conference Paper

Planning with Uncertain Action Models

• Francesco Percassi
• Alessandro Saetti
• Enrico Scala

Uncertainty over model knowledge is a core challenge in planning and has been addressed through various approaches tailored to different scenarios. In this paper, we focus on scenarios where the agent does not initially know the exact outcome of its actions but gains knowledge upon execution, i.e., each action reveals its actual effect, removing uncertainty about future occurrences. We refer to this formulation as Planning with Uncertain Models of Actions (PUMA). We show that PUMA can be compiled in polynomial time in both Fully Observable Non-Deterministic planning and, perhaps more unexpectedly, classical planning, providing a constructive proof that PUMA remains PSPACE-complete despite its apparent exponential uncertainty. Finally, we experimentally evaluate both compilations with benchmark domains that capture the key aspects of the problem. The results show the practical feasibility of our approach and reveal a complementary behavior between the two compilations.

AAAI Conference 2026 System Paper

PPS: An Efficient Java-based Simulator for Time-Discrete PDDL+

• Enrico Scala
• Francesco Percassi
• Mauro Vallati

The expressive power of PDDL+ is crucial in a wide range of real-world applications, where it is necessary to represent hybrid discrete-continuous changes and environmental dynamics. Given the complexity of the dynamics that can be modelled in PDDL+ and the scale of the problems involved, the ability to validate plans and simulate their trajectories is essential for assessing the accuracy of the models. In this paper, we present PPS (PDDL Plus Simulator), a Java-based tool that enables seamless validation and simulation of PDDL+ plans under time-discrete semantics.

AAAI Conference 2026 System Paper

Traffic Signal Plans Explorer: A General Framework for Visualising Traffic Evolution

• Francesco Doria
• Francesco Percassi
• Marco Maratea
• Mauro Vallati

We present the Traffic Signal Plans Explorer, a framework for visualising and exploring traffic signal plans generated via PDDL+ planning. Designed to support both traffic experts and non-specialists, the tool offers a web-based interface for high-level network analysis and a SUMO-based adapter for detailed simulation. Users can inspect junction settings and link dynamics, and simulate plan execution step by step. The system bridges planning technology with practical traffic control, enhancing the transparency and usability of automatically generated solutions.

IJCAI Conference 2025 Conference Paper

An Approach to Quantify Plans Robustness in Real-world Applications

• Francesco Percassi
• Sandra Castellanos-Paez
• Romain Rombourg
• Mauro Vallati

Automated planning systems are increasingly deployed in real-world applications, often characterised by uncertainty and noise stemming from sensors, actuators, and environmental conditions. Under such circumstances, improving the deployability of generated plans requires assessing their robustness to varying conditions, thereby reducing the need for costly replanning. Replanning can be computationally intensive and may hinder the practical applicability of planning systems. In many domains, such as urban traffic control or underwater exploration, it is often sufficient for plans to reach an acceptable region rather than the exact goal. A key distinction in this context lies between valid plans (which achieve the intended goal under ideal conditions) and executable plans (which remain feasible under uncertainty or perturbation). This paper formalises the notion of execution-invariant planning tasks, in which plans are robust to noise and uncertainty. To foster the adoption of automated planning in real-world settings, we propose a statistical framework for evaluating plan robustness, offering a quantifiable measure of a plan’s ability to reach a goal within a specified tolerance under diverse perturbations or uncertainty. We validate our approach in two real-world domains, demonstrating its effectiveness.

ECAI Conference 2025 Conference Paper

Conditional Effects in Numeric Planning Reloaded

• Luigi Bonassi
• Joan Espasa
• Francesco Percassi
• Enrico Scala

Automated planning, a core area of artificial intelligence, aims to generate action sequences that achieve specified goals based on a formal model. In classical planning, where only Boolean state variables are allowed, conditional effects are the standard approach for modelling actions with state-dependent outcomes. However, unlike in the classical setting, relatively little research has focused on developing planning methods for numeric problems with conditional effects. To address this gap in the literature, this work studies numeric planning with conditional effects. We formalise its semantics and revise existing classical planning compilations for conditional effects to account for the specific features of numeric planning. This results in three encodings: two are designed for the full class of numeric planning problems, while the third is specific to tasks with conditional effects that increase or decrease variables by a constant, transforming such problems into instances of Simple Numeric Planning, a well-known and practically significant subclass of numeric tasks. The experimental evaluation compares these compilations across both newly designed and compelling benchmarks as well as existing domains featuring conditional effects. Our empirical findings reveal complementary behaviour among the approaches, highlighting the practical impact of selecting the appropriate compilation for different problem structures.

AAAI Conference 2025 Conference Paper

Towards Practical Classical Planning Compilations of Numeric Planning

• Luigi Bonassi
• Francesco Percassi
• Enrico Scala

It is well known that numeric planning can be made decidable if the domain of all numeric state variables is finite. This bounded formulation can be polynomially compiled into classical planning with Boolean conditions and conditional effects preserving the plan size exactly. However, it remains unclear whether this compilation has any practical utility. To explore this aspect, this work revisits the theoretical compilation framework from a practical perspective, focusing on the fragment of simple numeric planning. Specifically, we introduce three different compilations. The first, called one-hot, aims to systematise the current practice among planning practitioners of modelling numeric planning through classical planning. The other two, termed binary compilations, extend and specialise the logarithmic encoding introduced in previous literature. Our experimental analysis reveals that the overly complex logarithmic encoding can, surprisingly, be made practical with some representational expedients. Among these, the use of axioms is particularly crucial. Furthermore, we identify a class of mildly numeric planning problems where a classical planner, i.e., LAMA, when run on the compiled problem, is highly competitive with state-of-the-art numeric planners.

AAAI Conference 2024 Conference Paper

An Effective Polynomial Technique for Compiling Conditional Effects Away

• Alfonso Emilio Gerevini
• Francesco Percassi
• Enrico Scala

The paper introduces a novel polynomial compilation technique for the sound and complete removal of conditional effects in classical planning problems. Similar to Nebel's polynomial compilation of conditional effects, our solution also decomposes each action with conditional effects into several simpler actions. However, it does so more effectively by exploiting the actual structure of the given conditional effects. We characterise such a structure using a directed graph and leverage it to significantly reduce the number of additional atoms required, thereby shortening the size of valid plans. Our experimental analysis indicates that this approach enables the effective use of polynomial compilations, offering benefits in terms of modularity and reusability of existing planners. It also demonstrates that a compilation-based approach can be more efficient, either independently or in synergy with state-of-the-art optimal planners that directly support conditional effects.

SoCS Conference 2024 Conference Paper

Deployable Yet Effective Traffic Signal Optimisation via Automated Planning (Extended Abstract)

• Anas El Kouaiti
• Francesco Percassi
• Alessandro Saetti
• Thomas Leo McCluskey
• Mauro Vallati

The use of planning techniques in traffic signal optimisation has proven effective in managing unexpected traffic conditions as well as typical traffic patterns. However, significant challenges concerning the deployability of generated signal plans remain, as planning systems need to consider constraints and features of the actual real-world infrastructure on which they will be implemented. To address this challenge, we introduce a range of PDDL+ models embodying technological requirements as well as insights from domain experts. The proposed models have been extensively tested on historical data using a range of well-known search strategies and heuristics, as well as alternative encodings. Results demonstrate their competitiveness with the state of the art.

SoCS Conference 2024 Conference Paper

Optimised Variants of Polynomial Compilation for Conditional Effects in Classical Planning

• Francesco Percassi
• Enrico Scala
• Alfonso Emilio Gerevini

Conditional effects are a key feature in classical planning, enabling the description of actions whose outcomes are state-dependent. It is well known that the polynomial removal of conditional effects necessarily increases the size of a valid plan by a polynomial factor while preserving exactly the plan size requires an exponential encoding of the problem. The paper proposes and empirically evaluates optimisations for existing polynomial compilations. These optimisations aim to make the resulting compilations more suitable for planners while limiting the increase in plan size, which is inevitable if we want to keep the compilation polynomial. Specifically, the paper introduces a polynomial compilation technique that expands conditional effects when their number is below a certain threshold and sequentialises them otherwise. Additionally, the paper demonstrates that even straightforward optimisations can have a notable impact.

ICAPS Conference 2024 Conference Paper

PDDL+ Models for Deployable yet Effective Traffic Signal Optimisation

• Anas El Kouaiti
• Francesco Percassi
• Alessandro Saetti
• Thomas Leo McCluskey
• Mauro Vallati

The use of planning techniques in traffic signal optimisation has proven effective in managing unexpected traffic conditions as well as typical traffic patterns. However, significant challenges concerning the deployability of generated signal strategies remain, as existing approaches tend not to consider constraints and features of the actual real-world infrastructure on which they will be implemented. To address this challenge, we introduce a range of PDDL+ models embodying technological requirements as well as insights from domain experts. The proposed models have been extensively tested on historical data using a range of well-known search strategies and heuristics, as well as alternative encodings. Results demonstrate their competitiveness with the state of the art.

ICAPS Conference 2024 Conference Paper

Taming Discretised PDDL+ through Multiple Discretisations

• Matteo Cardellini
• Marco Maratea
• Francesco Percassi
• Enrico Scala
• Mauro Vallati

The PDDL+ formalism allows the use of planning techniques in applications that require the ability to perform hybrid discrete-continuous reasoning. PDDL+ problems are notoriously challenging to tackle, and to reason upon them a well-established approach is discretisation. Existing systems rely on a single discretisation delta or, at most, two: a simulation delta to model the dynamics of the environment, and a planning delta, that is used to specify when decisions can be taken. However, there exist cases where this rigid schema is not ideal, for instance when agents with very different speeds need to cooperate or interact in a shared environment, and a more flexible approach that can accommodate more deltas is necessary. To address the needs of this class of hybrid planning problems, in this paper we introduce a reformulation approach that allows the encapsulation of different levels of discretisation in PDDL+ models, hence allowing any domain-independent planning engine to reap the benefits. Further, we provide the community with a new set of benchmarks that highlights the limits of fixed discretisation.

SoCS Conference 2024 Conference Paper

Taming Discretised PDDL+ through Multiple Discretisations (Extended Abstract)

• Matteo Cardellini
• Marco Maratea
• Francesco Percassi
• Enrico Scala
• Mauro Vallati

The PDDL+ formalism allows the use of planning techniques in applications that require the ability to perform hybrid discrete-continuous reasoning. PDDL+ problems are notoriously challenging to tackle, and to reason upon them a well-established approach is discretisation. Existing systems rely on a single discretisation delta or, at most, two: a simulation delta to model the dynamics of the environment, and a planning delta, that is used to specify when decisions can be taken. However, there exist cases where this rigid schema is not ideal, for instance when agents with very different speeds need to cooperate or interact in a shared environment, and a more flexible approach that can accommodate more deltas is necessary. To address the needs of this class of hybrid planning problems, in this paper we introduce a reformulation approach that allows the encapsulation of different levels of discretisation in PDDL+ models, hence allowing any domain-independent planning engine to reap the benefits. Further, we provide the community with a new set of benchmarks that highlights the limits of fixed discretisation.

JAIR Journal 2023 Journal Article

A Practical Approach to Discretised PDDL+ Problems by Translation to Numeric Planning

• Francesco Percassi
• Enrico Scala
• Mauro Vallati

PDDL+ models are advanced models of hybrid systems and the resulting problems are notoriously difficult for planning engines to cope with. An additional limiting factor for the exploitation of PDDL+ approaches in real-world applications is the restricted number of domain-independent planning engines that can reason upon those models. With the aim of deepening the understanding of PDDL+ models, in this work, we study a novel mapping between a time discretisation of pddl+ and numeric planning as for PDDL2.1 (level 2). The proposed mapping not only clarifies the relationship between these two formalisms but also enables the use of a wider pool of engines, thus fostering the use of hybrid planning in real-world applications. Our experimental analysis shows the usefulness of the proposed translation and demonstrates the potential of the approach for improving the solvability of complex PDDL+ instances.

IJCAI Conference 2023 Conference Paper

Automated Planning for Generating and Simulating Traffic Signal Strategies

• Saumya Bhatnagar
• Rongge Guo
• Keith McCabe
• Thomas McCluskey
• Francesco Percassi
• Mauro Vallati

There is a growing interest in the use of AI techniques for urban traffic control, with a particular focus on traffic signal optimisation. Model-based approaches such as planning demonstrated to be capable of dealing in real-time with unexpected or unusual traffic conditions, as well as with the usual traffic patterns. Further, the knowledge models on which such techniques rely to generate traffic signal strategies are in fact simulation models of traffic, hence can be used by traffic authorities to test and compare different approaches. In this work, we present a framework that relies on automated planning to generate and simulate traffic signal strategies in a urban region. To demonstrate the capabilities of the framework, we consider real-world data collected from sensors deployed in a major corridor of the Kirklees region of the United Kingdom.

ICAPS Conference 2023 Conference Paper

Fixing Plans for PDDL+ Problems: Theoretical and Practical Implications

• Francesco Percassi
• Enrico Scala
• Mauro Vallati

The plan, execution, and replan framework has proven to be extremely valuable in complex real-world applications, where the dynamics of the environment cannot be fully encoded in the domain model. However, this comes at the cost of regenerating plans from scratch, which can be expensive when expressive formalisms like PDDL+ are used. Given the complexity of generating PDDL+ plans, it would be ideal to reuse as much as possible of an existing plan, rather than generating a new one from scratch every time. To support more effective exploitation of the plan, execution, and replan framework in PDDL+, in this paper, we introduce the problem of discretized PDDL+ plan fixing, which allows one to fix existing plans according to some defined constraints. We demonstrate the theoretical implications of the introduced notion and introduce reformulations to address the problem using domain-independent planning engines. Our results show that such reformulations can outperform replanning from scratch and unlock planning engines to solve more problems with fine-grained discretizations.

ICAPS Conference 2023 Conference Paper

Goal Recognition as a Deep Learning Task: The GRNet Approach

• Mattia Chiari
• Alfonso Emilio Gerevini
• Francesco Percassi
• Luca Putelli
• Ivan Serina
• Matteo Olivato

Recognising the goal of an agent from a trace of observations is an important task with many applications. The state-of-the-art approach to goal recognition (GR) relies on the application of automated planning techniques. We study an alternative approach, called GRNet, where GR is formulated as a classification task addressed by machine learning. GRNet is primarily aimed at solving GR instances more accurately and more quickly by learning how to solve them in a given domain, which is specified by a set of propositions and a set of action names. The goal classification instances in the domain are solved by a Recurrent Neural Network (RNN). The only information required as input of the trained RNN is a trace of action labels, each one indicating just the name of an observed action. A run of the RNN processes a trace of observed actions to compute how likely it is that each domain proposition is part of the agent

SoCS Conference 2023 Conference Paper

On the Notion of Fixability of PDDL+ Plans [Extended Abstract]

• Francesco Percassi
• Enrico Scala
• Mauro Vallati

PDDL+ is an expressive formalism that allows for the use of planning in hybrid discrete-continuous domains. To cope with unexpected situations, it is crucial for deployed planning-based systems to efficiently repair existing plans. In this paper, we revisit a recently proposed FIXABILITY framework for expressing and solving problems from validation to rescheduling of actions in PDDL+ plans.

AIJ Journal 2023 Journal Article

Width-based search for multi agent privacy-preserving planning

• Alfonso E. Gerevini
• Nir Lipovetzky
• Francesco Percassi
• Alessandro Saetti
• Ivan Serina

SoCS Conference 2022 Conference Paper

On the Reformulation of Discretised PDDL+ to Numeric Planning (Extended Abstract)

• Francesco Percassi
• Enrico Scala
• Mauro Vallati

PDDL+ is an expressive planning formalism that enables the modelling of hybrid discrete-continuous domains. The resulting models are notoriously difficult to cope with, and few planning engines are natively supporting PDDL+. To foster the use of PDDL+, this paper revisits a set of recently proposed translations allowing to reformulate a PDDL+ task into a PDDL2. 1 one. Such translations permit the use of a wider set of engines to solve complex hybrid problems.

SoCS Conference 2022 Conference Paper

On the Use of Width-Based Search for Multi Agent Privacy-Preserving Planning (Extended Abstract)

• Alfonso Emilio Gerevini
• Nir Lipovetzky
• Francesco Percassi
• Alessandro Saetti
• Ivan Serina

The aim of decentralised multi-agent (DMA) planning is to coordinate a set of agents to jointly achieve a goal while preserving their privacy. Blind search algorithms, such as width-based search, have recently proved to be very effective in the context of centralised automated planning, especially when combined with goal-oriented techniques. In this paper, we discuss a recent line of research in which the usage of width-based search has been extensively studied in the context of DMA planning, addressing the challenges related to the agents

ICAPS Conference 2022 Conference Paper

The Power of Reformulation: From Validation to Planning in PDDL+

• Francesco Percassi
• Enrico Scala
• Mauro Vallati

PDDL+ allows the formal specification of systems representing mixed discrete-continuous representation, under both discrete and continuous dynamics; this expressiveness is pivotal in real-world applications. An important aspect is the capability of validating plans obtained by planning systems, and assessing their compliance against the domain's model. Unfortunately, a very limited number of validation tools are capable of dealing with PDDL+ tasks. To overcome this problem, in this work we propose an approach that allows exploiting any domain-independent PDDL+ or PDDL2. 1 planning engine for validating PDDL+ plans. We introduce a set of translations that, given a PDDL+ plan and the corresponding PDDL+ task, generate a new PDDL+ or PDDL2. 1 whose solvability is bound to the validity of the considered plan. We empirically evaluate the usefulness of the proposed approach on a range of PDDL+ benchmarks under an interpretation of time that can be either continuous (through a PDDL+ translation) or discrete (through a PDDL+ or a PDDL2. 1 translation).

ICAPS Conference 2021 Conference Paper

On Planning with Qualitative State-Trajectory Constraints in PDDL3 by Compiling them Away

• Luigi Bonassi
• Alfonso Emilio Gerevini
• Francesco Percassi
• Enrico Scala

We tackle the problem of classical planning with qualitative state-trajectory constraints as those that can be expressed in PDDL3. These kinds of constraints allow a user to formally specify which temporal properties a plan has to conform with through a class of LTL formulae. We study a compilation-based approach that does not resort to automata for representing and dealing with such properties, as other approaches do, and generates a classical planning problem with conditional effects that is solvable iff the original PDDL3 problem is. Our compilation exploits a regression operator to revise the actions' preconditions and conditional effects in a way to (i) prohibit executions that irreversibly violate temporal constraints (ii) be sensitive to executions that traverse those necessary subgoals implied by the temporal specification. An experimental analysis shows that our approach performs better than other state-of-the-art approaches over the majority of the considered benchmark domains.

ICAPS Conference 2021 Conference Paper

Translations from Discretised PDDL+ to Numeric Planning

• Francesco Percassi
• Enrico Scala
• Mauro Vallati

Hybrid PDDL+ models are amongst the most advanced models of systems and the resulting problems are notoriously difficult for planning engines to cope with. An additional limiting factor for the exploitation of PDDL+ approaches in real-world applications is the restricted number of domain-independent planning engines that can reason upon those models. With the aim of deepening the understanding of PDDL+ models, in this work we study a novel mapping between a time discretisation of PDDL+ and numeric planning as for PDDL2. 1 (level 2). The proposed mapping not only clarifies the relationship between these two formalisms, but also enables the use of a wider pool of engines, thus fostering the use of hybrid planning in real-world applications. Our experimental analysis shows the usefulness of the proposed translation, and demonstrates the potential of the approach for improving the solvability of complex PDDL+ instances.

ICAPS Conference 2020 Conference Paper

Generating and Exploiting Cost Predictions in Heuristic State-Space Planning

• Francesco Percassi
• Alfonso Emilio Gerevini
• Enrico Scala
• Ivan Serina
• Mauro Vallati

This paper proposes and investigates a novel way of combining machine learning and heuristic search to improve domain-independent planning. On the learning side, we use learning to predict the plan cost of a good solution for a given instance. On the planning side, we propose a bound-sensitive heuristic function that exploits such a prediction in a state-space planner. Our function combines the input prediction (derived inductively) with some pieces of information gathered during search (derived deductively). As the prediction can sometimes be grossly inaccurate, the function also provides means to recognise when the provided information is actually misguiding the search. Our experimental analysis demonstrates the usefulness of the proposed approach in a standard heuristic best-first search schema.

ICAPS Conference 2019 Conference Paper

Best-First Width Search for Multi Agent Privacy-Preserving Planning

• Alfonso Emilio Gerevini
• Nir Lipovetzky
• Francesco Percassi
• Alessandro Saetti
• Ivan Serina

In multi-agent planning, preserving the agents’ privacy has become an increasingly popular research topic. For preserving the agents’ privacy, agents jointly compute a plan that achieves mutual goals by keeping certain information private to the individual agents. Unfortunately, this can severely restrict the accuracy of the heuristic functions used while searching for solutions. It has been recently shown that, for centralized planning, the performance of goal oriented search can be improved by combining goal oriented search and width-based search. The combination of these techniques has been called best-first width search. In this paper, we investigate the usage of best-first width search in the context of (decentralised) multi-agent privacy-preserving planning, addressing the challenges related to the agents’ privacy and performance. In particular, we show that best-first width search is a very effective approach over several benchmark domains, even when the search is driven by heuristics that roughly estimate the distance from goal states, computed without using the private information of other agents. An experimental study analyses the effectiveness of our techniques and compares them with the state-of-the-art.

SoCS Conference 2019 Conference Paper

Novelty Messages Filtering for Multi Agent Privacy-Preserving Plannin

• Alfonso Emilio Gerevini
• Nir Lipovetzky
• Nico Peli
• Francesco Percassi
• Alessandro Saetti
• Ivan Serina

In multi-agent planning, agents jointly compute a plan that achieves mutual goals, keeping certain information private to the individual agents. Agents

ICAPS Conference 2019 Conference Paper

On Compiling Away PDDL3 Soft Trajectory Constraints without Using Automata

• Francesco Percassi
• Alfonso Emilio Gerevini

We address the problem of propositional planning extended with the class of soft temporally extended goals supported in PDDL3, also called qualitative preferences since IPC-5. Such preferences are useful to characterise plan quality by allowing the user to express certain soft constraints on the state trajectory of the desired solution plans. We propose and evaluate a compilation approach that extends previous work on compiling soft reachability goals and always goals to the full set of PDDL3 qualitative preferences. This approach directly compiles qualitative preferences into propositional planning without using automata to represent the trajectory constraints. Moreover, since no numeric fluent is used, it allows many existing STRIPS planners to immediately address planning with preferences without changing their algorithms or code. An experimental analysis presented in the paper evaluates the performance of state-of-the-art propositional planners using our compilation of qualitative preferences. The results indicate that the proposed approach is highly competitive with respect to current planners that natively support the considered class of preferences, as well as with a recent automata-based compilation approach.

SoCS Conference 2017 Conference Paper

Improving Plan Quality through Heuristics for Guiding and Pruning the Search: A Study Using LAMA

• Francesco Percassi
• Alfonso Emilio Gerevini
• Hector Geffner

Admissible heuristics are essential for optimal planning in the context of search algorithms like A*, and they can also be used in the context of suboptimal planning in order to find quality-bounded solutions. In satisfacing planning, on the other hand, admissible heuristics are not exploited by the best-first search algorithms of existing planners even when a time window is available for improving the first solution found. For example, in the well-know planner LAMA, better solutions within such a time window are sought by restarting a Weighted-A* search guided by inadmissible heuristics, each time a better solution is found. In this paper, we investigate the use of admissible heuristics in the context of LAMA for pruning nodes that cannot lead to better solutions. The revised search of LAMA is experimentally evaluated using two alternative admissible heuristics for pruning and three types of problems: planning with soft goals, planning with action costs, and planning with both action costs and soft goals. Soft goals are compiled into hard goals following the approach of Keyder and Geffner. The empirical results show that the use of admissible heuristics in LAMA can be of great help to improve the planner performance.