We study planning in a fragment of PDDL with qualitative state-trajectory constraints, capturing safety requirements, task ordering conditions, and intermediate sub-goals commonly found in real-world problems. A prominent approach to tackle such problems is to compile their constraints away, leading to a problem that is supported by state-of-the-art planners. Unfortunately, existing compilers do not scale on problems with a large number of objects and high-arity actions, as they necessitate grounding the problem before compilation. To address this issue, we propose two methods for compiling away constraints without grounding, making them suitable for large-scale planning problems. We prove the correctness of our compilers and outline their worst-case time complexity. Moreover, we present a reproducible empirical evaluation on the domains used in the latest International Planning Competition. Our results demonstrate that our methods are efficient and produce planning specifications that are orders of magnitude more succinct than the ones produced by compilers that ground the domain, while remaining competitive when used for planning with a state-of-the-art planner.

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.

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.

This paper studies the use of lazy greedy best-first search for numeric planning problems in combination with relaxation-based heuristics, helpful actions, and up-to-jumping actions. In particular, the new search schema that we study, whilst postponing evaluation of the heuristic at expansion time, focuses the search over those states that are reached by helpful and up-to-jumping actions. In addition, we revisit linear abstractions by improving the balance between computation time and information, providing guidance in non-simple numeric planning problems, too. The new search schema compares favorably over the IPC-23 benchmarks with alternative complete heuristic search planners from the literature.

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.

We study planning for temporally extended goals expressed in Pure-Past Linear Temporal Logic ( ppltl ) in the context of deterministic (i.e., classical) and fully observable nondeterministic (FOND) domains. ppltl is the variant of Linear-time Temporal Logic on finite traces ( ltl f ) that refers to the past rather than the future. Although ppltl is as expressive as ltl f , we show that it is computationally much more effective for planning. In particular, we show that checking the validity of a plan for a ppltl formula is Markovian. This is achieved by introducing a linear number of additional propositional variables that capture the validity of the entire formula in a modular fashion. The solution encoding introduces only a linear number of new fluents proportional to the size of the ppltl goal and does not require any additional spurious action. We implement our solution technique in a system called Plan4Past , which can be used alongside state-of-the-art classical and FOND planners. Our empirical analysis demonstrates the practical effectiveness of Plan4Past in both classical and FOND problems, showing that the resulting planner performs overall better than other planning approaches for ltl f goals.

This paper studies an approach to planning with PDDL3 constraints involving mixed propositional and numeric conditions, as well as metric time constraints. We show how the whole PDDL3 with instantaneous actions can be compiled away into a numeric planning problem without PDDL3 constraints, enabling the use of any state-of-the-art numeric planner that is agnostic to the existence of PDDL3. Our solution exploits the concept of regression. In addition to a basic compilation, we present an optimized variant based on the observation that it is possible to make the compilation sensitive to the structure of the problem to solve; this can be done by reasoning on the interactions between the problem actions and the constraints. The resulting optimization substantially reduces the size of the planning task. We experimentally observe that our approach significantly outperforms existing state-of-the-art planners supporting the same class of constraints over known benchmark domains, settling a new state-of-the-art planning system for PDDL3.

In this paper, we introduce Shielded FOND planning (S-FOND), which is the problem of computing a strategy to reach a final-state goal while preserving a safety specification called shield. In particular, we characterize shields as Pure-Past Linear Temporal Logic formulas that must hold in every prefix of a state trace induced by a solution strategy, thus capturing the whole safety fragment of Linear Temporal Logic formulas over finite traces. We propose three solution encodings for handling S-FOND problems: the first, which is our baseline, simply views a shield as a temporally extended goal; the second, instead, blocks the execution of further actions when the shield gets violated, and the third prevents the execution of actions that could violate the shield by using the notion of regression. We formally prove the correctness of each encoding and experimentally prove their effectiveness over a set of benchmark shields.

Recently, Pure-Past Temporal Logic (PPLTL) has proven highly effective in specifying temporally extended goals in deterministic planning domains. In this paper, we show its effectiveness also for fully observable nondeterministic (FOND) planning, both for strong and strong-cyclic plans. We present a notably simple encoding of FOND planning for PPLTL goals into standard FOND planning for final-state goals. The encoding only introduces few fluents (at most linear in the PPLTL goal) without adding any spurious action and allows planners to lazily build the relevant part of the deterministic automaton for the goal formula on-the-fly during the search. We formally prove its correctness, implement it in a tool called Plan4Past, and experimentally show its practical effectiveness.

In automated planning the ability of expressing constraints on the structure of the desired plans is important to deal with solution quality, as well as to express control knowledge. In PDDL3, this is supported through state-trajectory constraints corresponding to a class of LTLf formulae. In this paper, first we introduce a formalism to express trajectory constraints over actions in the plan, rather than over traversed states; Then we investigate compilation-based methods to deal with such constraints in propositional planning, and propose a new simple effective method. Finally, we experimentally study the usefulness of our action-trajectory constraints as a tool to express control knowledge. The experimental results show that the performance of a classical planner can be significantly improved by exploiting knowledge expressed by action constraints and handled by our compilation method, while the same knowledge turns out to be less beneficial when specified as state constraints and handled by two state-of-the-art systems supporting state constraints.