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Combinatorial reconfiguration is the problem of transforming one solution of a combinatorial problem into another, where each transformation may only apply small changes to a solution and may not leave the solution space. An important example is the independent set reconfiguration (ISR) problem, where an independent set of a graph (a subset of its vertices without edges between them) has to be transformed into another by a sequence of transformations that can replace a vertex in the current subset such that the new subset is still an independent set. The 1st Combinatorial Reconfiguration Challenge (CoRe Challenge 2022) was a competition focused on the ISR problem. The PARIS team successfully participated with two solvers that model the ISR problem as a planning task and employ different planning techniques for solving it. In this work, we describe these models and solvers. For a fair comparison to competing ISR approaches, we re-run the entire competition under equal computational conditions. Besides showcasing the success of planning technology, we hope that this work will create a cross-fertilization of the two research fields.

Cost partitioning is the foundation of today’s strongest heuristics for optimal classical planning. However, computing a cost partitioning for each evaluated state is prohibitively expensive in practice. Thus, existing approaches make an approximation and compute a cost partitioning only for a set of sampled states, and then reuse the resulting heuristics for all other states evaluated during the search. In this paper, we present exact methods for cost partitioning heuristics based on linear programming that fully preserve heuristic accuracy while minimizing computational cost. Specifically, we focus on saturated post-hoc optimization and establish several sufficient conditions for when reusing a cost partitioning computed for one state preserves the estimates for other states, mainly based on a sensitivity analysis of the underlying linear program. Our experiments demonstrate that our theoretical results transfer into practice, and that our exact cost partitioning algorithms are competitive with the strongest approximations currently available, while usually requiring fewer linear program evaluations.

Matrix multiplication is a fundamental operation of linear algebra, with applications ranging from quantum physics to artificial intelligence. Given its importance, enormous resources have been invested in the search for faster matrix multiplication algorithms. Recently, this search has been cast as a single-player game. By learning how to play this game efficiently, the newly-introduced AlphaTensor reinforcement learning agent is able to discover many new faster algorithms. In this paper, we show that finding matrix multiplication algorithms can also be cast as a classical planning problem. Based on this observation, we introduce a challenging benchmark suite for classical planning and evaluate state-of-the-art planning techniques on it. We analyze the strengths and limitations of different planning approaches in this domain and show that we can use classical planning to find lower bounds and concrete algorithms for matrix multiplication.

Since its introduction, partial satisfaction planning (PSP), including both oversubscription (OSP) and net-benefit, has received significant attention in the classical planning community. However, hierarchical aspects have been mostly ignored in this context, although several problem domains that form the main motivation for PSP, such as the rover domain, have an inherent hierarchical structure. In this paper, we are taking the necessary steps for facilitating this research direction. First, we formally define hierarchical partial satisfaction planning problems and discuss the usefulness and necessity of this formalism. Second, we present a carefully structured set of benchmarks consisting of OSP and net-benefit problems with hierarchical structure. We describe and analyze the different domains of the benchmark set and the desiderata that are met to provide an interesting and challenging starting point for upcoming research. Third, we introduce various planning techniques that can solve hierarchical OSP problems and investigate their empirical behaviour on our proposed benchmark.

Since no classical planner consistently outperforms all others, it is important to select a planner that works well for a given classical planning task. The two strongest approaches for planner selection use image and graph convolutional neural networks. They have the drawback that the learned models are complicated and uninterpretable. To obtain explainable models, we identify a small set of simple task features and show that elementary and interpretable machine learning techniques can use these features to solve roughly as many tasks as the complex approaches based on neural networks., Funding: Swiss National Science Foundation (SNSF) as part of the project "Certified Correctness and Guaranteed Performance for Domain-Independent Planning" (CCGP-Plan); TAILOR - EU [952215]; Wallenberg AI, Autonomous Systems and Software Program (WASP) - Knut and Alice Wallenberg Foundation; DFG [389792660, TRR 248]

Lifted planners are useful to solve tasks that are too hard to ground. Still, computing informative lifted heuristics is difficult: directly adapting ground heuristics to the lifted setting is often too expensive, and extracting heuristics from the lifted representation can be uninformative. A natural alternative for lifted planners is to use width-based search. These algorithms are among the strongest for ground planning, even the variants that do not access the action model. In this work, we adapt best-first width search to the lifted setting and show that this yields state-of-the-art performance for hard-to-ground planning tasks.

Previous work introduced the concept of progress states. After expanding a progress state, a greedy best-first search (GBFS) will only expand states with lower heuristic values. Current methods can identify progress states only for a single task and only after a solution for the task has been found. We introduce a novel approach that learns a description logic formula characterizing all progress states in a classical planning domain. Using the learned formulas in a GBFS to break ties in favor of progress states often significantly reduces the search effort.

Cartesian counterexample-guided abstraction refinement (CEGAR) yields strong heuristics for optimal classical planning. CEGAR repeatedly finds counterexamples, i.e., abstract plans that fail for the concrete task. Although there are usually many such abstract plans to choose from, the refinement strategy from previous work is to choose an arbitrary optimal one. In this work, we show that an informed refinement strategy is critical in theory and practice. We demonstrate that it is possible to execute all optimal abstract plans in the concrete task simultaneously, and present methods to minimize the time and number of refinement steps until we find a concrete solution. The resulting algorithm solves more tasks than the previous state of the art for Cartesian CEGAR, both during refinement and when used as a heuristic in an A* search.

Heuristic search is a successful approach to cost-optimal planning. Bidirectional heuristic search algorithms have been around for a long time, but only recent advances have led to algorithms like BAE* that have the potential to outperform unidirectional heuristic search algorithms like A* in practice. In this work, we analyze BAE* for classical planning and the challenges associated with the underlying assumption of an explicit state representation. We show that it is crucial to use mutexes and reachability analysis to reduce the potentially exponential number of goal states, which makes it possible to create an explicit representation of a reversed planning task that can be used for the backward search of BAE*. Our empirical evaluation shows that BAE* solves more instances than A* in multiple domains with significantly fewer node expansions, demonstrating the usefulness of BAE* in planning.

In top-k planning, the objective is to determine a set of k cheapest plans that provide several good alternatives to choose from. Such a solution set often contains plans that visit at least one state more than once. Depending on the application, plans with such loops are of little importance because they are dominated by a loopless representative and can prevent more meaningful plans from being found. In this paper, we motivate and introduce loopless top-k planning. We show how to enhance the state-of-the-art symbolic top-k planner, symK, to obtain an efficient, sound and complete algorithm for loopless top-k planning. An empirical evaluation shows that our proposed approach has a higher k-coverage than a generate-and-test approach that uses an ordinary top-k planner, which we show to be incomplete in the presence of zero-cost loops.

ion heuristics are the state of the art in optimal classical planning asheuristic search. Despite their success for explicit-state search, though,abstraction heuristics are not available for decoupled state-space search, anorthogonal reduction technique that can lead to exponential savings by decomposingplanning tasks. In this paper, we show how to compute pattern database (PDB)heuristics for decoupled states. The main challenge lies in how to additively employmultiple patterns, which is crucial for strong search guidance of the heuristics. Weshow that in the general case, for arbitrary collections of PDBs, computing theheuristic for a decoupled state is exponential in the number of leaf components ofdecoupled search. We derive several variants of decoupled PDB heuristics that allowto additively combine PDBs avoiding this blow-up and evaluate them empirically.

Decoupled search decomposes a classical planning task by partitioning its variables such that the dependencies between the resulting factors form a star topology. In this topology, a single center factor can interact arbitrarily with a set of leaf factors. The leaves, however, can interact with each other only indirectly via the center. In this work, we generalize this structural requirement and allow arbitrary topologies. The components must not overlap, i.e., each state variable is assigned to exactly one factor, but the interaction between factors is not restricted. We show how this generalization is connected to star topologies, which implies the correctness of decoupled search with this novel type of decomposition. We introduce factoring methods that automatically identify these topologies on a given planning task. Empirically, the generalized factorings lead to increased applicability of decoupled search on standard IPC benchmarks, as well as to superior performance compared to known factoring methods.

Recent work in classical planning has introduced dedicated techniques for detecting unsolvable states, i.e., states from which no goal state can be reached. We approach the problem from a generalized planning perspective and learn first-order-like formulas that characterize unsolvability for entire planning domains. We show how to cast the problem as a self-supervised classification task. Our training data is automatically generated and labeled by exhaustive exploration of small instances of each domain, and candidate features are automatically computed from the predicates used to define the domain. We investigate three learning algorithms with different properties and compare them to heuristics from the literature. Our empirical results show that our approach often captures important classes of unsolvable states with high classification accuracy. Additionally, the logical form of our heuristics makes them easy to interpret and reason about, and can be used to show that the characterizations learned in some domains capture exactly all unsolvable states of the domain.

Recent work in classical planning has introduced dedicated techniques for detecting unsolvable states, i.e., states from which no goal state can be reached. We approach the problem from a generalized planning perspective and learn first-order-like formulas that characterize unsolvability for entire planning domains. We show how to cast the problem as a self-supervised classification task. Our training data is automatically generated and labeled by exhaustive exploration of small instances of each domain, and candidate features are automatically computed from the predicates used to define the domain. We investigate three learning algorithms with different properties and compare them to heuristics from the literature. Our empirical results show that our approach often captures important classes of unsolvable states with high classification accuracy. Additionally, the logical form of our heuristics makes them easy to interpret and reason about, and can be used to show that the characterizations learned in some domains capture exactly all unsolvable states of the domain.

Recent work in classical planning has introduced dedicated techniques for detecting unsolvable states, i.e., states from which no goal state can be reached. We approach the problem from a generalized planning perspective and learn first-order-like formulas that characterize unsolvability for entire planning domains. We show how to cast the problem as a self-supervised classification task. Our training data is automatically generated and labeled by exhaustive exploration of small instances of each domain, and candidate features are automatically computed from the predicates used to define the domain. We investigate three learning algorithms with different properties and compare them to heuristics from the literature. Our empirical results show that our approach often captures important classes of unsolvable states with high classification accuracy. Additionally, the logical form of our heuristics makes them easy to interpret and reason about, and can be used to show that the characterizations learned in some domains capture exactly all unsolvable states of the domain.

Recent work in classical planning has introduced dedicated techniques for detecting unsolvable states, i.e., states from which no goal state can be reached. We approach the problem from a generalized planning perspective and learn first-order-like formulas that characterize unsolvability for entire planning domains. We show how to cast the problem as a self-supervised classification task. Our training data is automatically generated and labeled by exhaustive exploration of small instances of each domain, and candidate features are automatically computed from the predicates used to define the domain. We investigate three learning algorithms with different properties and compare them to heuristics from the literature. Our empirical results show that our approach often captures important classes of unsolvable states with high classification accuracy. Additionally, the logical form of our heuristics makes them easy to interpret and reason about, and can be used to show that the characterizations learned in some domains capture exactly all unsolvable states of the domain.

Recent work in classical planning has introduced dedicated techniques for detecting unsolvable states, i.e., states from which no goal state can be reached. We approach the problem from a generalized planning perspective and learn first-order-like formulas that characterize unsolvability for entire planning domains. We show how to cast the problem as a self-supervised classification task. Our training data is automatically generated and labeled by exhaustive exploration of small instances of each domain, and candidate features are automatically computed from the predicates used to define the domain. We investigate three learning algorithms with different properties and compare them to heuristics from the literature. Our empirical results show that our approach often captures important classes of unsolvable states with high classification accuracy. Additionally, the logical form of our heuristics makes them easy to interpret and reason about, and can be used to show that the characterizations learned in some domains capture exactly all unsolvable states of the domain.

We consider a general and industrially motivated class of planning problems involving a combination of requirements that can be essential to autonomous robotic systems planning to act in the real world: Support for temporal uncertainty where nature determines the eventual duration of an action, resource consumption with a non-linear relationship to durations, and the need to select appropriate values for control parameters that affect time requirements and resource usage. To this end, an existing planner is extended with support for Simple Temporal Networks with Uncertainty, Timed Initial Literals, and temporal coverage goals. Control parameters are lifted from the main combinatorial planning problem into a constraint satisfaction problem that connects them to resource usage. Constraint processing is then integrated and interleaved with verification of temporal feasibility, using projections for partial temporal awareness in the constraint solver., NFFP6, CADICS, ELLIIT, SSF Smart Systems

This thesis considers computer-assisted troubleshooting of heavy vehicles such as trucks and buses. In this setting, the person that is troubleshooting a vehicle problem is assisted by a computer that is capable of listing possible faults that can explain the problem and gives recommendations of which actions to take in order to solve the problem such that the expected cost of restoring the vehicle is low. To achieve this, such a system must be capable of solving two problems: the diagnosis problem of finding which the possible faults are and the decision problem of deciding which action should be taken. The diagnosis problem has been approached using Bayesian network models. Frameworks have been developed for the case when the vehicle is in the workshop only and for remote diagnosis when the vehicle is monitored during longer periods of time. The decision problem has been solved by creating planners that select actions such that the expected cost of repairing the vehicle is minimized. New methods, algorithms, and models have been developed for improving the performance of the planner. The theory developed has been evaluated on models of an auxiliary braking system, a fuel injection system, and an engine temperature control and monitoring system.

This thesis considers computer-assisted troubleshooting of heavy vehicles such as trucks and buses. In this setting, the person that is troubleshooting a vehicle problem is assisted by a computer that is capable of listing possible faults that can explain the problem and gives recommendations of which actions to take in order to solve the problem such that the expected cost of restoring the vehicle is low. To achieve this, such a system must be capable of solving two problems: the diagnosis problem of finding which the possible faults are and the decision problem of deciding which action should be taken. The diagnosis problem has been approached using Bayesian network models. Frameworks have been developed for the case when the vehicle is in the workshop only and for remote diagnosis when the vehicle is monitored during longer periods of time. The decision problem has been solved by creating planners that select actions such that the expected cost of repairing the vehicle is minimized. New methods, algorithms, and models have been developed for improving the performance of the planner. The theory developed has been evaluated on models of an auxiliary braking system, a fuel injection system, and an engine temperature control and monitoring system.

This thesis considers computer-assisted troubleshooting of heavy vehicles such as trucks and buses. In this setting, the person that is troubleshooting a vehicle problem is assisted by a computer that is capable of listing possible faults that can explain the problem and gives recommendations of which actions to take in order to solve the problem such that the expected cost of restoring the vehicle is low. To achieve this, such a system must be capable of solving two problems: the diagnosis problem of finding which the possible faults are and the decision problem of deciding which action should be taken. The diagnosis problem has been approached using Bayesian network models. Frameworks have been developed for the case when the vehicle is in the workshop only and for remote diagnosis when the vehicle is monitored during longer periods of time. The decision problem has been solved by creating planners that select actions such that the expected cost of repairing the vehicle is minimized. New methods, algorithms, and models have been developed for improving the performance of the planner. The theory developed has been evaluated on models of an auxiliary braking system, a fuel injection system, and an engine temperature control and monitoring system.

This thesis considers computer-assisted troubleshooting of heavy vehicles such as trucks and buses. In this setting, the person that is troubleshooting a vehicle problem is assisted by a computer that is capable of listing possible faults that can explain the problem and gives recommendations of which actions to take in order to solve the problem such that the expected cost of restoring the vehicle is low. To achieve this, such a system must be capable of solving two problems: the diagnosis problem of finding which the possible faults are and the decision problem of deciding which action should be taken. The diagnosis problem has been approached using Bayesian network models. Frameworks have been developed for the case when the vehicle is in the workshop only and for remote diagnosis when the vehicle is monitored during longer periods of time. The decision problem has been solved by creating planners that select actions such that the expected cost of repairing the vehicle is minimized. New methods, algorithms, and models have been developed for improving the performance of the planner. The theory developed has been evaluated on models of an auxiliary braking system, a fuel injection system, and an engine temperature control and monitoring system.

This thesis considers computer-assisted troubleshooting of heavy vehicles such as trucks and buses. In this setting, the person that is troubleshooting a vehicle problem is assisted by a computer that is capable of listing possible faults that can explain the problem and gives recommendations of which actions to take in order to solve the problem such that the expected cost of restoring the vehicle is low. To achieve this, such a system must be capable of solving two problems: the diagnosis problem of finding which the possible faults are and the decision problem of deciding which action should be taken. The diagnosis problem has been approached using Bayesian network models. Frameworks have been developed for the case when the vehicle is in the workshop only and for remote diagnosis when the vehicle is monitored during longer periods of time. The decision problem has been solved by creating planners that select actions such that the expected cost of repairing the vehicle is minimized. New methods, algorithms, and models have been developed for improving the performance of the planner. The theory developed has been evaluated on models of an auxiliary braking system, a fuel injection system, and an engine temperature control and monitoring system.