Arrow Research search

Author name cluster

Matteo Cardellini

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

9 papers
2 author rows

Possible papers

9

AIJ Journal 2026 Journal Article

Symbolic pattern planning

  • Matteo Cardellini
  • Enrico Giunchiglia
  • Marco Maratea

In this paper, we propose a novel approach for solving automated planning problems, called Symbolic Pattern Planning. Given a deterministic planning problem Π, we propose to compute a plan by first fixing a pattern –defined as an arbitrary sequence of actions– and then define a formula encoding the state resulting from the sequential execution of the actions in the pattern, starting from an arbitrary initial state. By allowing each action in the pattern to be executed consecutively zero, one or possibly more times, and by imposing the conditions on the initial and goal states, we can check whether the pattern allows determining a valid plan or whether the pattern needs to be extended and the procedure iterated. We ground our proposal in the numeric planning setting, we prove the correctness and also the completeness of the procedure (provided at each iteration the pattern is extended with a complete sequence of actions), and we define procedures for the pattern selection and for computing quality plans. When exploiting the planning as satisfiability approach, we show that our encoding allows to determine a valid plan in a number of iterations which is never higher than the one needed by the state-of-the-art rolled-up or relaxed-relaxed-∃ symbolic encodings. On the experimental side, we run an extensive analysis which included the problems and systems involved in the numeric track of the 2023 International Planning Competition, showing that the results validate the theoretical findings and that our planner Patty has remarkably good comparative performances.

Details DOI

KR Conference 2025 Conference Paper

Pushing the Envelope in Numeric Pattern Planning

  • Matteo Cardellini
  • Enrico Giunchiglia

In this paper, we present a symbolic search-based procedure for numeric planning based on Symbolic Pattern Planning (SPP). In SPP, a pattern is a sequence of actions used to define a logic formula whose models correspond to sequences of applicable actions and reachable states. Here, starting from the empty pattern, we iteratively extend and compress it using search techniques until a goal state is reached. We prove the correctness and completeness of the procedure and demonstrate its good performance compared to both the original SPP approach and other publicly available numeric planners on the 2023 International Planning Competition Agile track.

PDF Details DOI

IJCAI Conference 2025 Conference Paper

Rolling in Classical Planning with Conditional Effects and Constraints

  • Matteo Cardellini
  • Enrico Giunchiglia

In classical planning, conditional effects (CEs) allow modelling non-idempotent actions, where the resulting state may depend on how many times each action is consecutively repeated. Though CEs have been widely studied in the literature, no one has ever studied how to exploit rolling, i. e. , how to effectively model the consecutive repetition of an action. In this paper, we fill this void by (i) showing that planning with CEs remains PSPACE-complete even in the limit case of problems with a single action, (ii) presenting a correct and complete planning as satisfiability encoding exploiting rolling while effectively dealing with constraints imposed on the set of reachable states, and (iii) theoretically and empirically showing its substantial benefits.

PDF Details DOI

AAAI Conference 2025 Conference Paper

Temporal Numeric Planning with Patterns

  • Matteo Cardellini
  • Enrico Giunchiglia

We consider temporal numeric planning problems Π expressed in PDDL2.1, and show how it is possible to produce SMT formulas (i) whose models correspond to valid plans of Π, and (ii) which extends the recently proposed planning with patterns approach from the numeric to the temporal case. We prove the correctness and completeness of the approach and that it outperforms all the publicly available temporal planners on 10 domains with required concurrency.

PDF Details DOI

AAAI Conference 2024 Conference Paper

Symbolic Numeric Planning with Patterns

  • Matteo Cardellini
  • Enrico Giunchiglia
  • Marco Maratea

In this paper, we propose a novel approach for solving linear numeric planning problems, called Symbolic Pattern Planning. Given a planning problem Pi, a bound n and a pattern --defined as an arbitrary sequence of actions-- we encode the problem of finding a plan for Pi with bound n as a formula with fewer variables and/or clauses than the state-of-the-art rolled-up and relaxed-relaxed-exists encodings. More importantly, we prove that for any given bound, it is never the case that the latter two encodings allow finding a valid plan while ours does not. On the experimental side, we consider 6 other planning systems --including the ones which participated in this year's International Planning Competition (IPC)-- and we show that our planner Patty has remarkably good comparative performances on this year's IPC problems.

PDF Details DOI

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.

Details

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.

Details

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.

Details

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.

Details
v2026.09.13