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Mauro Vallati

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52 papers
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52

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.

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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.

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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.

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IJCAI Conference 2025 Conference Paper

A Survey on Model Repair in AI Planning

  • Pascal Bercher
  • Sarath Sreedharan
  • Mauro Vallati

Accurate planning models are a prerequisite for the appropriate functioning of AI planning applications. Creating these models is, however, a tedious and error-prone task -- even for planning experts. This makes the provision of automated modeling support essential. In this work, we differentiate between approaches that learn models from scratch (called domain model acquisition) and those that repair flawed or incomplete ones. We survey approaches for the latter, including those that can be used for domain repair but have been developed for other applications, discuss possible optimization metrics (i. e. , which repaired model to aim at), and conclude with lines of research we believe deserve more attention.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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JELIA Conference 2023 Conference Paper

Comparing Planning Domain Models Using Answer Set Programming

  • Lukás Chrpa
  • Carmine Dodaro
  • Marco Maratea
  • Marco Mochi
  • Mauro Vallati

Abstract Automated planning is a prominent area of Artificial Intelligence, and an important component for intelligent autonomous agents. A critical aspect of domain-independent planning is the domain model, that encodes a formal representation of domain knowledge needed to reason upon a given problem. Despite the crucial role of domain models in automated planning, there is lack of tools supporting knowledge engineering process by comparing different versions of the models, in particular, determining and highlighting differences the models have. In this paper, we build on the notion of strong equivalence of domain models and formalise a novel concept of similarity of domain models. To measure the similarity of two models, we introduce a directed graph representation of lifted domain models that allows to formulate the domain model similarity problem as a variant of the graph edit distance problem. We propose an Answer Set Programming approach to optimally solve the domain model similarity problem, that identifies the minimum number of modifications the models need to become strongly equivalent, and we demonstrate the capabilities of the approach on a range of benchmark models.

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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.

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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.

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KER Journal 2023 Journal Article

Reformulation techniques for automated planning: a systematic review

  • Diaeddin Alarnaouti
  • George Baryannis
  • Mauro Vallati

Abstract Automated planning is a prominent area of Artificial Intelligence and an important component for intelligent autonomous agents. A cornerstone of domain-independent planning is the separation between planning logic, that is the automated reasoning side, and the knowledge model, that encodes a formal representation of domain knowledge needed to reason upon a given problem to synthesize a solution plan. Such a separation enables the use of reformulation techniques, which transform how a model is represented in order to improve the efficiency of plan generation. Over the past decades, significant research effort has been devoted to the design of reformulation techniques. In this paper, we present a systematic review of the large body of work on reformulation techniques for classical planning, aiming to provide a holistic view of the field and to foster future research in the area. As a tangible outcome, we provide a qualitative comparison of the existing classes of techniques, that can help researchers gain an overview of their strengths and weaknesses.

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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.

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JAIR Journal 2022 Journal Article

Planning with Critical Section Macros: Theory and Practice

  • Lukas Chrpa
  • Mauro Vallati

Macro-operators (macros) are a well-known technique for enhancing performance of planning engines by providing “short-cuts” in the state space. Existing macro learning systems usually generate macros by considering most frequent action sequences in training plans. Unfortunately, frequent action sequences might not capture meaningful activities as a whole, leading to a limited beneficial impact for the planning process. In this paper, inspired by resource locking in critical sections in parallel computing, we propose a technique that generates macros able to capture whole activities in which limited resources (e.g., a robotic hand, or a truck) are used. Specifically, such a Critical Section macro starts by locking the resource (e.g., grabbing an object), continues by using the resource (e.g., manipulating the object) and finishes by releasing the resource (e.g., dropping the object). Hence, such a macro bridges states in which the resource is locked and cannot be used. We also introduce versions of Critical Section macros dealing with multiple resources and phased locks. Usefulness of macros is evaluated using a range of state-of-the-art planners, and a large number of benchmarks from the deterministic and learning tracks of recent editions of the International Planning Competition.

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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).

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SoCS Conference 2022 Conference Paper

Urban Traffic Control via Planning with Global State Constraints (Extended Abstract)

  • Franc Ivankovic
  • Mauro Vallati
  • Lukás Chrpa
  • Marco Roveri

Planning with global state constraints is an extension of classical planning such that some properties of each state are derived via a set of rules common to all states. This approach is important for the application of planning techniques in manipulating cyber-physical systems, and has been shown to be effective in practice. Urban Traffic Control (UTC) deals with the control and management of traffic in urban regions, and includes the optimisation of traffic signals configuration to minimise traffic congestion and travel delays. In this paper, we briefly introduce how to cast the UTC problem into the formalism of planning with global state constraints, and we perform a preliminary experimental evaluation considering significant scenarios taken from the literature, and a new one based on real-world data. The results show that the approach is feasible, and the quality of generated solutions has been confirmed in simulation using existing symbolic models.

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SoCS Conference 2021 Conference Paper

A Planning-based Approach for In-Station Train Dispatching

  • Matteo Cardellini
  • Marco Maratea
  • Mauro Vallati
  • Gianluca Boleto
  • Luca Oneto

In-station train dispatching is the problem of optimising the effective utilisation of available railway infrastructures for mitigating incidents and delays. In this paper, we describe an approach for dealing with the in-station dispatching problem by means of automated planning techniques.

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FLAP Journal 2021 Journal Article

An Exploratory Study on the Use of Artificial Intelligence to Initiate Legal Understanding for Business Development.

  • Alessia Grassi
  • Mauro Vallati

Given the dynamic environment and the ever-changing international context, it is pivotal for companies to be able to quickly and effectively identify potential threats and opportunities. This can be done via environmental scanning, that allows to develop potential scenarios which help in proactively plan responses to potential risks. Yet, the process of scanning, and the design and analysis of scenarios, is extremely expensive, as it has to be done manually. Therefore, they cannot be exploited as often as they should to deliver the maximal benefit to a company. In this chapter, we propose the use of Artificial Intelligence (AI) techniques to support the PESTLE analysis, a managerial tool used to identify those external factors which might affect a company. In particular, we focus on one of the environments scanned through PESTLE, the legal environment, and how AI can support this time and labour consuming process.

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KER Journal 2021 Journal Article

Effective grounding for hybrid planning problems represented in PDDL+

  • Enrico Scala
  • Mauro Vallati

Abstract Automated planning is the field of Artificial Intelligence (AI) that focuses on identifying sequences of actions allowing to reach a goal state from a given initial state. The need of using such techniques in real-world applications has brought popular languages for expressing automated planning problems to provide direct support for continuous and discrete state variables, along with changes that can be either instantaneous or durative. PDDL+ (Planning Domain Definition Language +) models support the encoding of such representations, but the resulting planning problems are notoriously difficult for AI planners to cope with due to non-linear dependencies arising from the variables and infinite search spaces. This difficulty is exacerbated by the potentially huge fully ground representations used by modern planners in order to effectively explore the search space, which can make some problems impossible to tackle. This paper investigates two grounding techniques for PDDL+ problems, both aimed at reducing the size of the full ground representation by reasoning over the lifted, more abstract problem structure. The first method extends the simple mechanism of invariant analysis to limit the groundings of operators upfront. The second method proposes to tackle the grounding process through a PDDL+ to classical planning abstraction; this allows us to leverage the amount of research done in the classical planning area. Our empirical analysis studies the effect of these novel approaches over both real-world hybrid applications and synthetic PDDL+ problems took from standard benchmarks of the planning community; our results reveal that not only the techniques improve the running time of previous grounding mechanisms but also let the planner extend the reach to problems that were not solvable before.

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ICAPS Conference 2021 Conference Paper

In-Station Train Dispatching: A PDDL+ Planning Approach

  • Matteo Cardellini
  • Marco Maratea
  • Mauro Vallati
  • Gianluca Boleto
  • Luca Oneto

In railway networks, stations are probably the most critical points for interconnecting trains' routes: in a restricted geographical area, a potentially large number of trains have to stop according to an official timetable, with the concrete risk of accumulating delays that can then have a knockout effect on the rest of the network. In this context, in-station train dispatching plays a central role in maximising the effective utilisation of available railway infrastructures and in mitigating the impact of incidents and delays. Unfortunately, in-station train dispatching is still largely handled manually by human operators in charge of a group of stations. In this paper we make a step towards supporting the operator with some automatic tool, by describing an approach for performing in-station dispatching by means of automated planning techniques. Given the mixed discrete-continuous nature of the problem, we employ PDDL+ for the specification of the problem, and the ENHSP planning engine enhanced by domain-specific solving techniques. Results on a range of scenarios, using real-data of a station of the North West of Italy, show the potential of our approach.

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IJCAI Conference 2021 Conference Paper

Skeptical Reasoning with Preferred Semantics in Abstract Argumentation without Computing Preferred Extensions

  • Matthias Thimm
  • Federico Cerutti
  • Mauro Vallati

We address the problem of deciding skeptical acceptance wrt. preferred semantics of an argument in abstract argumentation frameworks, i. e. , the problem of deciding whether an argument is contained in all maximally admissible sets, a. k. a. preferred extensions. State-of-the-art algorithms solve this problem with iterative calls to an external SAT-solver to determine preferred extensions. We provide a new characterisation of skeptical acceptance wrt. preferred semantics that does not involve the notion of a preferred extension. We then develop a new algorithm that also relies on iterative calls to an external SAT-solver but avoids the costly part of maximising admissible sets. We present the results of an experimental evaluation that shows that this new approach significantly outperforms the state of the art. We also apply similar ideas to develop a new algorithm for computing the ideal extension.

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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.

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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.

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AIJ Journal 2019 Journal Article

How we designed winning algorithms for abstract argumentation and which insight we attained

  • Federico Cerutti
  • Massimiliano Giacomin
  • Mauro Vallati

In this paper we illustrate the design choices that led to the development of ArgSemSAT, the winner of the preferred semantics track at the 2017 International Competition on Computational Models of Arguments (ICCMA 2017), a biennial contest on problems associated to the Dung's model of abstract argumentation frameworks, widely recognised as a fundamental reference in computational argumentation. The algorithms of ArgSemSAT are based on multiple calls to a SAT solver to compute complete labellings, and on encoding constraints to drive the search towards the solution of decision and enumeration problems. In this paper we focus on preferred semantics (and incidentally stable as well), one of the most popular and complex semantics for identifying acceptable arguments. We discuss our design methodology that includes a systematic exploration and empirical evaluation of labelling encodings, algorithmic variations and SAT solver choices. In designing the successful ArgSemSAT, we discover that: (1) there is a labelling encoding that appears to be universally better than other, logically equivalent ones; (2) composition of different techniques such as AllSAT and enumerating stable extensions when searching for preferred semantics brings advantages; (3) injecting domain specific knowledge in the algorithm design can lead to significant improvements.

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AAAI Conference 2019 Conference Paper

Improving Domain-Independent Planning via Critical Section Macro-Operators

  • Lukáš Chrpa
  • Mauro Vallati

Macro-operators, macros for short, are a well-known technique for enhancing performance of planning engines by providing “short-cuts” in the state space. Existing macro learning systems usually generate macros from most frequent sequences of actions in training plans. Such approach priorities frequently used sequences of actions over meaningful activities to be performed for solving planning tasks. This paper presents a technique that, inspired by resource locking in critical sections in parallel computing, learns macros capturing activities in which a limited resource (e. g. , a robotic hand) is used. In particular, such macros capture the whole activity in which the resource is “locked” (e. g. , the robotic hand is holding an object) and thus “bridge” states in which the resource is locked and cannot be used. We also introduce an “aggressive” variant of our technique that removes original operators superseded by macros from the domain model. Usefulness of macros is evaluated on several stateof-the-art planners, and a wide range of benchmarks from the learning tracks of the 2008 and 2011 editions of the International Planning Competition.

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KER Journal 2019 Journal Article

Predictive models and abstract argumentation: the case of high-complexity semantics

  • Mauro Vallati
  • Federico Cerutti
  • Massimiliano Giacomin

Abstract In this paper, we describe how predictive models can be positively exploited in abstract argumentation. In particular, we present two main sets of results. On one side, we show that predictive models are effective for performing algorithm selection in order to determine which approach is better to enumerate the preferred extensions of a given argumentation framework. On the other side, we show that predictive models predict significant aspects of the solution to the preferred extensions enumeration problem. By exploiting an extensive set of argumentation framework features—that is, values that summarize a potentially important property of a framework—the proposed approach is able to provide an accurate prediction about which algorithm would be faster on a given problem instance, as well as of the structure of the solution, where the complete knowledge of such structure would require a computationally hard problem to be solved. Improving the ability of existing argumentation-based systems to support human sense-making and decision processes is just one of the possible exploitations of such knowledge obtained in an inexpensive way.

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AIIM Journal 2019 Journal Article

Towards a modular decision support system for radiomics: A case study on rectal cancer

  • Roberto Gatta
  • Mauro Vallati
  • Nicola Dinapoli
  • Carlotta Masciocchi
  • Jacopo Lenkowicz
  • Davide Cusumano
  • Calogero Casá
  • Alessandra Farchione

Following the personalized medicine paradigm, there is a growing interest in medical agents capable of predicting the effect of therapies on patients, by exploiting the amount of data that is now available for each patient. In disciplines like oncology, where images and scans are available, the exploitation of medical images can provide an additional source of potentially useful information. The study and analysis of features extracted by medical images, exploited for predictive purposes, is termed radiomics. A number of tools are available for supporting some of the steps of the radiomics process, but there is a lack of approaches which are able to deal with all the steps of the process. In this paper, we introduce a medical agent-based decision support system capable of handling the whole radiomics process. The proposed system is tested on two independent data sets of patients treated for rectal cancer. Experimental results indicate that the system is able to generate highly performant centre-specific predictive model, and show the issues related to differences in data sets collected by different centres, and how such issues can affect the performance of the generated predictive models.

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ICAPS Conference 2018 Conference Paper

A General Approach for Configuring PDDL Problem Models

  • Mauro Vallati
  • Ivan Serina

The development of a large number of domain-independentplanners is leading to the use of planning engines in a widerange of applications. This is despite the complexity issues inherent in plan generation, which are exacerbated by the separation of planner logic from domain knowledge. However, this separation supports the use of reformulation and configuration techniques, which transform the model representation in order to improve the planner's performance. In this paper, we investigate how the performance of domain-independent planners can be improved by problem model configuration. We introduce a fully automated method for this configuration task, that considers problem-specific aspects extracted by exploiting a problem- and domain-independent representation of the instance. Our extensive experimental analysis shows that this reformulation technique can have a significant impact on planners' performance.

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KER Journal 2018 Journal Article

What you always wanted to know about the deterministic part of the International Planning Competition (IPC) 2014 (but were too afraid to ask)

  • Mauro Vallati
  • Lukáš Chrpa
  • Thomas L. McCluskey

Abstract The International Planning Competition (IPC) is a prominent event of the artificial intelligence planning community that has been organized since 1998; it aims at fostering the development and comparison of planning approaches, assessing the state-of-the-art in planning and identifying new challenging benchmarks. IPC has a strong impact also outside the planning community, by providing a large number of ready-to-use planning engines and testing pioneering applications of planning techniques. This paper focusses on the deterministic part of IPC 2014, and describes format, participants, benchmarks as well as a thorough analysis of the results. Generally, results of the competition indicates some significant progress, but they also highlight issues and challenges that the planning community will have to face in the future.

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IJCAI Conference 2017 Conference Paper

Automated Planning for Urban Traffic Management

  • Thomas L. McCluskey
  • Mauro Vallati
  • Santiago Franco

The global growth in urbanisation increases the demand for services including road transport infrastructure, presenting challenges in terms of mobility. Optimising the exploitation of urban road network, while attempting to minimise the effects of traffic emissions, is a great challenge. SimplyfAI was a UK research council grant funded project which was aimed towards solving air quality problems caused by road traffic emissions. Large cities such as Manchester struggle to meet air quality limits as the range of available traffic management devices is limited. In the study, we investigated the application of linked data to enrich environmental and traffic data feeds, and we used this with automated planning tools to enable traffic to be managed at a region level. The management will have the aim of avoiding air pollution problems before they occur. This demo focuses on the planning component, and in particular the engineering and validation aspects, that were pivotal for the success of the project.

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ICAPS Conference 2017 Conference Paper

Embedding Automated Planning within Urban Traffic Management Operations

  • Thomas Leo McCluskey
  • Mauro Vallati

This paper is an experience report on the results of an industry-led collaborative project aimed at automating the control of traffic flow within a large city centre. A major focus of the automation was to deal with abnormal or unexpected events such as roadworks, road closures or excessive demand, resulting in periods of saturation of the network within some region of the city. We describe the resulting system which works by sourcing and semantically enriching urban traffic data, and uses the derived knowledge as input to an automated planning component to generate light signal control strategies in real time. This paper reports on the development surrounding the planning component, and in particular the engineering, configuration and validation issues that arose in the application. It discusses a range of lessons learned from the experience of deploying automated planning in the road transport area, under the direction of transport operators and technology developers.

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SoCS Conference 2017 Conference Paper

Improving a Planner's Performance through Online Heuristic Configuration of Domain Models

  • Mauro Vallati
  • Lukás Chrpa
  • Thomas Leo McCluskey

The separation of planner logic from domain knowledge supports the use of reformulation and configuration techniques, such as macro-actions and entanglements, which transform the model representation in order to improve a planner’s performance. One drawback of such an approach is that it may require a potentially expensive training phase. In this paper, we introduce heuristic approaches for the online configuration of planning domain models. The proposed heuristics consider different aspects of PDDL-encoded operators for reordering such operators in the domain model, relying on the assumption that the way in which operators are encoded carries useful information about their expected use.

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ICAPS Conference 2017 Conference Paper

On the Exploitation of Automated Planning for Reducing Machine Tools Energy Consumption between Manufacturing Operations

  • Simon Parkinson
  • Andrew Longstaff
  • Simon Fletcher
  • Mauro Vallati
  • Lukás Chrpa

There has recently been an increased emphasis on reducing energy consumption in manufacturing, driven by the fluctuations in energy costs and the growing importance given to environmental impact of manufactured goods. Lots of attention has been given to the reduction of machine tools energy consumption, as they require large amounts of energy to perform manufacturing tasks. One area that has received relatively little interest, yet could harness great potential, is reducing energy consumption by planning machine activities between manufacturing operations, while the machine is not in use. The intuitive option --which is currently exploited in manufacturing-- is to leave the machine in a normal operating state in anticipation of the next manufacturing job. However, this is far from optimal due to the thermal deformation phenomenon, which usually require an energy-intensive warm-up cycle in order to bring all the components (e. g. spindle motor) into a suitable (stable) state for actual machining. Evidently, the use of this strategy comes with the associated commercial and environmental repercussions. In this paper, we investigate the exploitability of automated planning techniques for planning machine activities between manufacturing operations. We present a PDDL 2. 2 formulation of the task that considers energy consumption, thermal deformation, and accuracy. We then demonstrate the effectiveness of the proposed approach using a case study which considers real-world data.

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AAAI Conference 2016 Conference Paper

Efficient Macroscopic Urban Traffic Models for Reducing Congestion: A PDDL+ Planning Approach

  • Mauro Vallati
  • Daniele Magazzeni
  • Bart De Schutter
  • Lukas Chrpa
  • Thomas McCluskey

The global growth in urbanisation increases the demand for services including road transport infrastructure, presenting challenges in terms of mobility. In this scenario, optimising the exploitation of urban road networks is a pivotal challenge. Existing urban traf- fic control approaches, based on complex mathematical models, can effectively deal with planned-ahead events, but are not able to cope with unexpected situations –such as roads blocked due to car accidents or weather-related events– because of their huge computational requirements. Therefore, such unexpected situations are mainly dealt with manually, or by exploiting pre-computed policies. Our goal is to show the feasibility of using mixed discrete-continuous planning to deal with unexpected circumstances in urban traffic control. We present a PDDL+ formulation of urban traffic control, where continuous processes are used to model flows of cars, and show how planning can be used to efficiently reduce congestion of specified roads by controlling traffic light green phases. We present simulation results on two networks (one of them considers Manchester city centre) that demonstrate the effectiveness of the approach, compared with fixed-time and reactive techniques.

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KR Conference 2016 Short Paper

jArgSemSAT: an Efficient Off-The-Shelf Solver for Abstract Argumentation Frameworks

  • Federico Cerutti
  • Mauro Vallati
  • Massimiliano Giacomin

In this report from the field we describe jArgSemSAT, a Java re-implementation of ArgSemSAT. We show that jArgSemSAT can be easily integrated in existing argumentation systems (1) as an off-the-shelf, standalone, library; (2) as a Tweety compatible library; and (3) as a fast and robust web service freely available on the Web. The performance section shows that—despite being written in Java—jArgSemSAT is very efficient w. r. t. preferred semantics, which has associated problems with high computational complexity.

ECAI Conference 2016 Conference Paper

Solving Set Optimization Problems by Cardinality Optimization with an Application to Argumentation

  • Wolfgang Faber 0001
  • Mauro Vallati
  • Federico Cerutti 0001
  • Massimiliano Giacomin

Optimization-minimization or maximization-in the lattice of subsets is a frequent operation in Artificial Intelligence tasks. Examples are subset-minimal model-based diagnosis, nonmonotonic reasoning by means of circumscription, or preferred extensions in abstract argumentation. Finding the optimum among many admissible solutions is often harder than finding admissible solutions with respect to both computational complexity and methodology. This paper addresses the former issue by means of an effective method for finding subset-optimal solutions. It is based on the relationship between cardinality-optimal and subset-optimal solutions, and the fact that many logic-based declarative programming systems provide constructs for finding cardinality-optimal solutions, for example maximum satisfiability (MaxSAT) or weak constraints in Answer Set Programming (ASP). Clearly each cardinality-optimal solution is also a subset-optimal one, and if the language also allows for the addition of particular restricting constructs (both MaxSAT and ASP do) then all subset-optimal solutions can be found by an iterative computation of cardinality-optimal solutions. As a showcase, the computation of preferred extensions of abstract argumentation frameworks using the proposed method is studied.

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AAAI Conference 2015 Conference Paper

Exploiting Parallelism for Hard Problems in Abstract Argumentation

  • Federico Cerutti
  • Ilias Tachmazidis
  • Mauro Vallati
  • Sotirios Batsakis
  • Massimiliano Giacomin
  • Grigoris Antoniou

Abstract argumentation framework (AF) is a unifying framework able to encompass a variety of nonmonotonic reasoning approaches, logic programming and computational argumentation. Yet, efficient approaches for most of the decision and enumeration problems associated to AFs are missing, thus limiting the efficacy of argumentation-based approaches in real domains. In this paper, we present an algorithm for enumerating the preferred extensions of abstract argumentation frameworks which exploits parallel computation. To this purpose, the SCC-recursive semantics definition schema is adopted, where extensions are defined at the level of specific sub-frameworks. The algorithm shows significant performance improvements in large frameworks, in terms of number of solutions found and speedup.

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SoCS Conference 2015 Conference Paper

Exploring the Synergy between Two Modular Learning Techniques for Automated Planning

  • Raquel Fuentetaja 0001
  • Lukás Chrpa
  • Thomas Leo McCluskey
  • Mauro Vallati

In the last decade the emphasis on improving the operational performance of domain independent automated planners has been in developing complex techniques which merge a range of different strategies. This quest for operational advantage, driven by the regular international planning competitions, has not made it easy to study, understand and predict what combinations of techniques will have what effect on a planner’s behaviour in a particular application domain. In this paper, we consider two machine learning techniques for planner performance improvement, and exploit a modular approach to their combination in order to facilitate the analysis of the impact of each individual component. We believe this can contribute to the development of more transparent planning engines, which are designed using modular, interchangeable, and well-founded components. Specifically, we combined two previously unrelated learning techniques, entanglements and relational decision trees, to guide a “vanilla” search algorithm. We report on a large experimental analysis which demonstrates the effectiveness of the approach in terms of performance improvements, resulting in a very competitive planning configuration despite the use of a more modular and transparent architecture. This gives insights on the strengths and weaknesses of the considered approaches, that will help their future exploitation.

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ICAPS Conference 2015 Conference Paper

Identifying and Exploiting Features for Effective Plan Retrieval in Case-Based Planning

  • Mauro Vallati
  • Ivan Serina
  • Alessandro Saetti
  • Alfonso Emilio Gerevini

Case-Based planning can fruitfully exploit knowledge gained by solving a large number of problems, storing the corresponding solutions in a plan library and reusing them for solving similar planning problems in the future. Case-based planning is extremely effective when similar reuse candidates can be efficiently chosen. In this paper, we study an innovative technique based on planning problem features for efficiently retrieving solved planning problems (and relative plans) from large plan libraries. Since existing planning features are not always able to effectively distinguish between problems within the same planning domain, we introduce anew class of features. Our experimental analysis shows that the proposed features-based retrieval approach can significantly improve the performance of a state-of-the-art case-based planning system.

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IJCAI Conference 2015 Conference Paper

On the Effective Configuration of Planning Domain Models

  • Mauro Vallati
  • Frank Hutter
  • Lukas Chrpa
  • Thomas Leo McCluskey

The development of domain-independent planners within the AI Planning community is leading to “off the shelf” technology that can be used in a wide range of applications. Moreover, it allows a modular approach – in which planners and domain knowledge are modules of larger software applications – that facilitates substitutions or improvements of individual modules without changing the rest of the system. This approach also supports the use of reformulation and configuration techniques, which transform how a model is represented in order to improve the efficiency of plan generation. In this paper, we investigate how the performance of planners is affected by domain model configuration. We introduce a fully automated method for this configuration task, and show in an extensive experimental analysis with six planners and seven domains that this process (which can, in principle, be combined with other forms of reformulation and configuration) can have a remarkable impact on performance across planners. Furthermore, studying the obtained domain model configurations can provide useful information to effectively engineer planning domain models.

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IJCAI Conference 2015 Conference Paper

On the Online Generation of Effective Macro-Operators

  • Luk
  • aacute;
  • scaron; Chrpa
  • Mauro Vallati
  • Thomas Leo McCluskey

Macro-operator (“macro”, for short) generation is a well-known technique that is used to speed-up the planning process. Most published work on using macros in automated planning relies on an offline learning phase where training plans, that is, solutions of simple problems, are used to generate the macros. However, there might not always be a place to accommodate training. In this paper we propose OMA, an efficient method for generating useful macros without an offline learning phase, by utilising lessons learnt from existing macro learning techniques. Empirical evaluation with IPC benchmarks demonstrates performance improvement in a range of state-of-the-art planning engines, and provides insights into what macros can be generated without training.

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SoCS Conference 2015 Conference Paper

Towards a Reformulation Based Approach for Efficient Numeric Planning: Numeric Outer Entanglements

  • Lukás Chrpa
  • Enrico Scala
  • Mauro Vallati

Restricting the search space has shown to be an effective approach for improving the performance of automated planning systems. A planner-independent technique for pruning the search space is domain and problem reformulation. Recently, Outer Entanglements, which are relations between planning operators and initial or goal predicates, have been introduced as a reformulation technique for eliminating potential undesirable instances of planning operators, and thus restricting the search space. Reformulation techniques, however, have been mainly applied in classical planning, although many real-world planning applications require to deal with numerical information. In this paper, we investigate the usefulness of reformulation approaches in planning with numerical fluents. In particular, we propose and extension of the notion of outer entanglements for handling numeric fluents. An empirical evaluation, which involves 150 instances from 5 domains, shows promising results.

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KR Conference 2014 Conference Paper

A SCC Recursive Meta-Algorithm for Computing Preferred Labellings in Abstract Argumentation

  • Federico Cerutti
  • Massimiliano Giacomin
  • Mauro Vallati
  • Marina Zanella

This paper presents a meta-algorithm for the computation of preferred labellings, based on the general recursive schema for argumentation semantics called SCC-Recursiveness. The idea is to recursively decompose a framework so as to compute semantics labellings on restricted sub-frameworks, in order to reduce the computational effort. The meta-algorithm can be instantiated with a specific “base algorithm”, applied to the base case of the recursion, which can be obtained by generalizing existing algorithms in order to compute labellings in restricted sub-frameworks. We devise for this purpose a generalization of a SAT-based algorithm, and provide an empirical investigation to show the significant improvement of performances obtained by exploiting the SCCrecursive schema.

ECAI Conference 2014 Conference Paper

Argumentation Frameworks Features: an Initial Study

  • Mauro Vallati
  • Federico Cerutti 0001
  • Massimiliano Giacomin

Semantics extensions are the outcome of the argumentation reasoning process: enumerating them is generally an intractable problem. For preferred semantics two efficient algorithms have been recently proposed, PrefSAT and SCC-P, with significant runtime variations. This preliminary work aims at investigating the reasons (argumentation framework features) for such variations. Remarkably, we observed that few features have a strong impact, and those exploited by the most performing algorithm are not the most relevant.

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ICAPS Conference 2014 Conference Paper

Improved Features for Runtime Prediction of Domain-Independent Planners

  • Chris Fawcett
  • Mauro Vallati
  • Frank Hutter
  • Jörg Hoffmann 0001
  • Holger H. Hoos
  • Kevin Leyton-Brown

State-of-the-art planners often exhibit substantial runtime variation, making it useful to be able to efficiently predict how long a given planner will take to run on a given instance. In other areas of AI, such needs are met by building so-called empirical performance models (EPMs), statistical models derived from sets of problem instances and performance observations. Historically, such models have been less accurate for predicting the running times of planners. A key hurdle has been a relative weakness in instance features for characterizing instances: mappings from problem instances to real numbers that serve as the starting point for learning an EPM. We propose a new, extensive set of instance features for planning, and investigate its effectiveness across a range of model families. We built EPMs for various prominent planning systems on several thousand benchmark problems from the planning literature and from IPC benchmark sets, and conclude that our models predict runtime much more accurately than the previous state of the art. We also study the relative importance of these features.

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ICAPS Conference 2014 Conference Paper

MUM: A Technique for Maximising the Utility of Macro-operators by Constrained Generation and Use

  • Lukás Chrpa
  • Mauro Vallati
  • Thomas Leo McCluskey

Research into techniques that reformulate problems to make general solvers more efficiently derive solutions has attracted much attention, in particular when the reformulation process is to some degree solver and domain independent. There are major challenges to overcome when applying such techniques to automated planning, however: reformulation methods such as adding macro-operators (macros, for short) can be detrimental because they tend to increase branching factors during solution search, while other methods such as learning entanglements can limit a planner's space of potentially solvable problems (its coverage) through over-pruning. These techniques may therefore work well with some domain-problem-planner combinations, but work poorly with others. In this paper we introduce a new learning technique (MUM) for synthesising macros from training example plans in order to improve the speed and coverage of domain independent automated planning engines. MUM embodies domain – independent constraints for selecting macro candidates, for generating macros, and for limiting the size of the grounding set of learned macros, therefore maximising the utility of used macros. Our empirical results with IPC benchmark domains and a range of state of the art planners demonstrate the advance that MUM makes to the increased coverage and efficiency of the planners. Comparisons with a previous leading macro learning mechanism further demonstrate MUM's capability.

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SoCS Conference 2013 Conference Paper

Automatic Generation of Efficient Domain-Optimized Planners from Generic Parametrized Planners

  • Mauro Vallati
  • Chris Fawcett
  • Alfonso Emilio Gerevini
  • Holger H. Hoos
  • Alessandro Saetti

When designing state-of-the-art, domain-independent planning systems, many decisions have to be made with respect to the domain analysis or compilation performed during preprocessing, the heuristic functions used during search, and other features of the search algorithm. These design decisions can have a large impact on the performance of the resulting planner. By providing many alternatives for these choices and exposing them as parameters, planning systems can in principle be configured to work well on different domains. However, planners are typically used in default configurations that have been chosen because of their good average performance over a set of benchmark domains, with limited experimentation over the potentially huge range of possible configurations. In this work, we propose a general framework for automatically configuring a parameterized planner, and show that substantial performance gains can be achieved. We apply the framework to the well-known LPG planner, which in the context of this work was expanded to 62 parameters and over 6. 5 x 10^17 possible configurations. By using this highly parameterized planning system in combination with the state-of-the-art automatic algorithm configuration procedure ParamILS, excellent performance on a broad range of well-known benchmark domains was achieved, as also witnessed by the results of the learning track of the 7th International Planning Competition.

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IJCAI Conference 2013 Conference Paper

Exploring Knowledge Engineering Strategies in Designing and Modelling a Road Traffic Accident Management Domain

  • Mohammad M. Shah
  • Lukáš Chrpa
  • Diane Kitchin
  • Thomas L. McCluskey
  • Mauro Vallati

Formulating knowledge for use in AI Planning engines is currently something of an ad-hoc process, where the skills of knowledge engineers and the tools they use may significantly influence the quality of the resulting planning application. There is little in the way of guidelines or standard procedures, however, for knowledge engineers to use when formulating knowledge into planning domain languages such as PDDL. This paper seeks to investigate this process using as a case study a road traffic accident management domain. Managing road accidents requires systematic, sound planning and coordination of resources to improve outcomes for accident victims. We have derived a set of requirements in consultation with stakeholders for the resource coordination part of managing accidents. We evaluate two separate knowledge engineering strategies for encoding the resulting planning domain from the set of requirements: (a) the traditional method of PDDL experts and text editor, and (b) a leading planning GUI with built in UML modelling tools. These strategies are evaluated using process and product metrics, where the domain model (the product) was tested extensively with a range of planning engines. The results give insights into the strengths and weaknesses of the approaches, highlight lessons learned regarding knowledge encoding, and point to important lines of research for knowledge engineering for planning.

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ICAPS Conference 2009 Conference Paper

An Automatically Configurable Portfolio-based Planner with Macro-actions: PbP

  • Alfonso Emilio Gerevini
  • Alessandro Saetti
  • Mauro Vallati

While several powerful domain-independent planners have recently been developed, no one of these clearly outperforms all the others in every known benchmark domain. We present PbP, a multi-planner which automatically configures a portfolio of planners by (1) computing some sets of macro-actions for every planner in the portfolio, (2) selecting a promising combination of planners in the portfolio and relative useful macro-actions, and (3) defining some running time slots for their round-robin scheduling during planning. The configuration relies on some knowledge about the performance of the planners in the portfolio and relative macro-actions which is automatically generated from a training problem set. PbP entered the learning track of IPC-2008 and was the overall winner of this competition track. An experimental study confirms the effectiveness of PbP, and shows that the learned configuration knowledge is useful for PbP.

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