Pub Date : 2023-07-01DOI: 10.1609/icaps.v33i1.27203
A. Lindsay
The PDDL modelling problem is known to be challenging, time consuming and error prone. This has led researchers to investigate methods of supporting the modelling process. One particular avenue is to adapt tools and techniques that have proven useful in software engineering to support the modelling process. We observe that concepts, such as inheritance and modularity have not been fully explored in the context of modelling PDDL planning models. Within software engineering these concepts help to organise and provide structure to code, which can make it easier to read, debug, and reuse code. In this work we consider inheritance and modularity and their use in PDDL action descriptions, and how these can have a similar impact on the PDDL modelling process. We define an extension to PDDL and develop appropriate tools to compile models using these extensions, both directly from the command line and through the Visual Studio Code PDDL extension. We report on our use of inheritance and modularity when modelling a planning model for a companion robot scenario. We also discuss the benefits of exploiting the inheritance hierarchy in other modules within our robot system.
众所周知，PDDL建模问题具有挑战性、耗时且容易出错。这促使研究人员研究支持建模过程的方法。一个特别的途径是采用在软件工程中被证明有用的工具和技术来支持建模过程。我们注意到，在PDDL规划模型建模的背景下，继承和模块化等概念还没有得到充分的探讨。在软件工程中，这些概念有助于组织和提供代码结构，从而使代码更容易阅读、调试和重用。在这项工作中，我们考虑继承和模块化及其在PDDL动作描述中的使用，以及它们如何对PDDL建模过程产生类似的影响。我们定义了PDDL的扩展，并开发了适当的工具来使用这些扩展来编译模型，既可以直接从命令行，也可以通过Visual Studio Code PDDL扩展。我们报告了在为同伴机器人场景建模规划模型时使用继承和模块化。我们还讨论了在机器人系统中的其他模块中利用继承层次结构的好处。
{"title":"On Using Action Inheritance and Modularity in PDDL Domain Modelling","authors":"A. Lindsay","doi":"10.1609/icaps.v33i1.27203","DOIUrl":"https://doi.org/10.1609/icaps.v33i1.27203","url":null,"abstract":"The PDDL modelling problem is known to be challenging, time consuming and error prone. This has led researchers to investigate methods of supporting the modelling process. One particular avenue is to adapt tools and techniques that have proven useful in software engineering to support the modelling process. We observe that concepts, such as inheritance and modularity have not been fully explored in the context of modelling PDDL planning models. Within software engineering these concepts help to organise and provide structure to code, which can make it easier to read, debug, and reuse code. In this work we consider inheritance and modularity and their use in PDDL action descriptions, and how these can have a similar impact on the PDDL modelling process. We\u0000define an extension to PDDL and develop appropriate tools to compile models using these extensions, both directly from the command line and through the Visual Studio Code PDDL extension. We report on our use of inheritance and modularity when modelling a planning model for a companion robot scenario. We also discuss the benefits of exploiting the inheritance hierarchy in other modules within our robot system.","PeriodicalId":239898,"journal":{"name":"International Conference on Automated Planning and Scheduling","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122713180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-01DOI: 10.1609/icaps.v33i1.27221
S. Sreedharan, Christian Muise, Subbarao Kambhampati
We revisit two concepts popularly used within the context of classical planning, namely action justification and causal links. While these concepts have come to underpin some of the most popular notions of explanations in classical planning, these notions are restricted to sequential plans. To address this shortcoming, we propose a generalization of these concepts that is applicable to state-action policies. We introduce algorithms that can identify justified actions and causal links contributed by such actions for policies generated for Fully Observable Non-Deterministic (FOND) planning problems. We also present an empirical evaluation that demonstrates the computational characteristics of these algorithms on standard FOND benchmarks.
{"title":"Generalizing Action Justification and Causal Links to Policies","authors":"S. Sreedharan, Christian Muise, Subbarao Kambhampati","doi":"10.1609/icaps.v33i1.27221","DOIUrl":"https://doi.org/10.1609/icaps.v33i1.27221","url":null,"abstract":"We revisit two concepts popularly used within the context of classical planning, namely action justification and causal links. While these concepts have come to underpin some of the most popular notions of explanations in classical planning, these notions are restricted to sequential plans. To address this shortcoming, we propose a generalization of these concepts that is applicable to state-action policies. We introduce algorithms that can identify justified actions and causal links contributed by such actions for policies generated for Fully Observable Non-Deterministic (FOND) planning problems. We also present an empirical evaluation that demonstrates the computational characteristics of these algorithms on standard FOND benchmarks.","PeriodicalId":239898,"journal":{"name":"International Conference on Automated Planning and Scheduling","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114222528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-01DOI: 10.1609/icaps.v33i1.27207
Andrew Murray, A. Arulselvan, Michael Cashmore, M. Roper, J. Frank
Probabilistic Simple Temporal Networks (PSTN) represent scheduling problems under temporal uncertainty. Strong controllability (SC) of PSTNs involves finding a schedule to a PSTN that maximises the probability that all constraints are satisfied (robustness). Previous approaches to this problem assume independence of probabilistic durations, and approximate the risk by bounding it above using Boole’s inequality. This gives no guarantee of finding the schedule optimising robustness, and fails to consider correlations between probabilistic durations that frequently arise in practical applications. In this paper, we formally define the Correlated Simple Temporal Network (Corr-STN) which generalises the PSTN by removing the restriction of independence. We show that the problem of Corr-STN SC is convex for a large class of multivariate (log-concave) distributions. We then introduce an algorithm capable of finding optimal SC schedules to Corr-STNs, using the column generation method. Finally, we validate our approach on a number of Corr-STNs and find that our method offers more robust solutions when compared with prior approaches.
{"title":"A Column Generation Approach to Correlated Simple Temporal Networks","authors":"Andrew Murray, A. Arulselvan, Michael Cashmore, M. Roper, J. Frank","doi":"10.1609/icaps.v33i1.27207","DOIUrl":"https://doi.org/10.1609/icaps.v33i1.27207","url":null,"abstract":"Probabilistic Simple Temporal Networks (PSTN) represent scheduling problems under temporal uncertainty. Strong controllability (SC) of PSTNs involves finding a schedule to a PSTN that maximises the probability that all constraints are satisfied (robustness). Previous approaches to this problem assume independence of probabilistic durations, and approximate the risk by bounding it above using Boole’s inequality. This gives no guarantee of finding the schedule optimising robustness, and fails to consider correlations between probabilistic durations that frequently arise in practical applications. In this paper, we formally define the Correlated Simple Temporal Network (Corr-STN) which generalises the PSTN by removing the restriction of independence. We show that the problem of Corr-STN SC is convex for a large class of multivariate (log-concave) distributions. We then introduce an algorithm capable of finding optimal SC schedules to Corr-STNs, using the column generation method. Finally, we validate our approach on a number of Corr-STNs and find that our method offers more robust solutions when compared with prior approaches.","PeriodicalId":239898,"journal":{"name":"International Conference on Automated Planning and Scheduling","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130002084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-01DOI: 10.1609/icaps.v33i1.27216
B. Say
The deployment of automated planning in safety critical systems has resulted in the need for the development of robust automated planners that can (i) accurately model complex systems under uncertainty, and (ii) provide formal guarantees on the model they act on. In this paper, we introduce a robust automated planner that can represent such stochastic systems with metric specifications and constrained continuous-time nonlinear dynamics over mixed (i.e., real and discrete valued) concurrent action spaces. The planner uses inverse transform sampling to model uncertainty, and has the capability of performing bi-objective optimization to first enforce the constraints of the problem as best as possible, and second optimize the metric of interest. Theoretically, we show that the planner terminates in finite time and provides formal guarantees on its solution. Experimentally, we demonstrate the capability of the planner to robustly control four complex physical systems under uncertainty.
{"title":"Robust Metric Hybrid Planning in Stochastic Nonlinear Domains Using Mathematical Optimization","authors":"B. Say","doi":"10.1609/icaps.v33i1.27216","DOIUrl":"https://doi.org/10.1609/icaps.v33i1.27216","url":null,"abstract":"The deployment of automated planning in safety critical systems has resulted in the need for the development of robust automated planners that can (i) accurately model complex systems under uncertainty, and (ii) provide formal guarantees on the model they act on. In this paper, we introduce a robust automated planner that can represent such stochastic systems with metric specifications and constrained continuous-time nonlinear dynamics over mixed (i.e., real and discrete valued) concurrent action spaces. The planner uses inverse transform sampling to model uncertainty, and has the capability of performing bi-objective optimization to first enforce the constraints of the problem as best as possible, and second optimize the metric of interest. Theoretically, we show that the planner terminates in finite time and provides formal guarantees on its solution. Experimentally, we demonstrate the capability of the planner to robustly control four complex physical systems under uncertainty.","PeriodicalId":239898,"journal":{"name":"International Conference on Automated Planning and Scheduling","volume":"126 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131249644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-01DOI: 10.1609/icaps.v33i1.27178
Johannes Blum, Ruoying Li, Sabine Storandt
Several algorithms for path planning in grid networks rely on graph decomposition to reduce the search space size; either by constructing a search data structure on the components, or by using component information for A* guidance. The focus is usually on obtaining components of roughly equal size with few boundary nodes each. In this paper, we consider the problem of splitting a graph into convex components. A convex component is characterized by the property that for all pairs of its members, the shortest path between them is also contained in it. Thus, given a source node, a target node, and a (small) convex component that contains both of them, path planning can be restricted to this component without compromising optimality. We prove that it is NP-hard to find a balanced node separator that splits a given graph into convex components. However, we also present and evaluate heuristics for (hierarchical) convex decomposition of grid networks that perform well across various benchmarks. Moreover, we describe how existing path planning methods can benefit from the computation of convex components. As one main outcome, we show that contraction hierarchies become up to an order of magnitude faster on large grids when the contraction order is derived from a convex graph decomposition.
{"title":"Convexity Hierarchies in Grid Networks","authors":"Johannes Blum, Ruoying Li, Sabine Storandt","doi":"10.1609/icaps.v33i1.27178","DOIUrl":"https://doi.org/10.1609/icaps.v33i1.27178","url":null,"abstract":"Several algorithms for path planning in grid networks rely on graph decomposition to reduce the search space size; either by constructing a search data structure on the components, or by using component information for A* guidance.\u0000The focus is usually on obtaining components of roughly equal size with few boundary nodes each. In this paper, we consider the problem of splitting a graph into convex components. A convex component is characterized by the property that for all pairs of its members, the shortest path between them is also contained in it. Thus, given a source node, a target node, and a (small) convex component that contains both of them, path planning can be restricted to this component without compromising optimality. We prove that it is NP-hard to find a balanced node separator that splits a given graph into convex components. However, we also present and evaluate heuristics for (hierarchical) convex decomposition of grid networks that perform well across various benchmarks. Moreover, we describe how existing path planning methods can benefit from the computation of convex components. As one main outcome, we show that contraction hierarchies become up to an order of magnitude faster on large grids when the contraction order is derived from a convex graph decomposition.","PeriodicalId":239898,"journal":{"name":"International Conference on Automated Planning and Scheduling","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124434131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-01DOI: 10.1609/icaps.v33i1.27185
Jan Eisenhut, Á. Torralba, M. Christakis, Jörg Hoffmann
Testing is a promising way to gain trust in learned action policies π. Prior work on action-policy testing in AI planning formalized bugs as states t where π is sub-optimal with respect to a given testing objective. Deciding whether or not t is a bug is as hard as (optimal) planning itself. How can we design test oracles able to recognize some states t to be bugs efficiently? Recent work introduced metamorphic oracles which compare policy behavior on state pairs (s,t) where t is easier to solve; if π performs worse on t than on s, we know that t is a bug. Here, we show how to automatically design such oracles in classical planning, based on simulation relations between states. We introduce two oracle families of this kind: first, morphing query states t to obtain suitable s; second, maintaining and comparing upper bounds on h* across the states encountered during testing. Our experiments on ASNet policies show that these oracles can find bugs much more quickly than the existing alternatives, which are search-based; and that the combination of our oracles with search-based ones almost consistently dominates all other oracles.
{"title":"Automatic Metamorphic Test Oracles for Action-Policy Testing","authors":"Jan Eisenhut, Á. Torralba, M. Christakis, Jörg Hoffmann","doi":"10.1609/icaps.v33i1.27185","DOIUrl":"https://doi.org/10.1609/icaps.v33i1.27185","url":null,"abstract":"Testing is a promising way to gain trust in learned action policies π. \u0000Prior work on action-policy testing in AI planning formalized bugs\u0000as states t where π is sub-optimal with respect to a given testing\u0000objective. Deciding whether or not t is a bug is as hard as (optimal)\u0000planning itself. How can we design test oracles able to recognize some\u0000states t to be bugs efficiently? Recent work introduced metamorphic\u0000oracles which compare policy behavior on state pairs (s,t) where t is\u0000easier to solve; if π performs worse on t than on s, we know that t\u0000is a bug. Here, we show how to automatically design such oracles in\u0000classical planning, based on simulation relations between states. We\u0000introduce two oracle families of this kind: first, morphing query\u0000states t to obtain suitable s; second, maintaining and comparing upper\u0000bounds on h* across the states encountered during testing. Our\u0000experiments on ASNet policies show that these oracles can find bugs\u0000much more quickly than the existing alternatives, which are\u0000search-based; and that the combination of our oracles with\u0000search-based ones almost consistently dominates all other oracles.","PeriodicalId":239898,"journal":{"name":"International Conference on Automated Planning and Scheduling","volume":"214 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116172521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-01DOI: 10.1609/icaps.v33i1.27175
Dor Atzmon, Shahaf S. Shperberg, Netanel Sabah, Ariel Felner, Nathan R Sturtevant
In this paper, we develop theoretical foundations for bidirectional bounded-suboptimal search (BiBSS) based on recent advancements in optimal bidirectional search. In addition, we introduce a BiBSS variant of the prominent meet-in-the-middle (MM) algorithm, called Weighted MM (WMM). We show that WMM has an interesting property of being W-restrained, and study it empirically.
{"title":"W-restrained Bidirectional Bounded-Suboptimal Heuristic Search","authors":"Dor Atzmon, Shahaf S. Shperberg, Netanel Sabah, Ariel Felner, Nathan R Sturtevant","doi":"10.1609/icaps.v33i1.27175","DOIUrl":"https://doi.org/10.1609/icaps.v33i1.27175","url":null,"abstract":"In this paper, we develop theoretical foundations for bidirectional bounded-suboptimal search (BiBSS) based on recent advancements in optimal bidirectional search. In addition, we introduce a BiBSS variant of the prominent meet-in-the-middle (MM) algorithm, called Weighted MM (WMM). We show that WMM has an interesting property of being W-restrained, and study it empirically.","PeriodicalId":239898,"journal":{"name":"International Conference on Automated Planning and Scheduling","volume":"35 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123709682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-01DOI: 10.1609/icaps.v33i1.27231
Lucas Kletzander, Nysret Musliu
When large sets of constraints and objectives are combined in a practical optimization problem, managing all these potentially conflicting goals can become very difficult and might require to solve an instance multiple times. First, an instance might be infeasible with the current constraints, in which case our system introduces a novel violation score to help identify the constraints that need to be relaxed for the next run. Second, multiple objectives are often combined using a linear combination with hand-crafted weights, which are very difficult to set such that the result matches the expectations regarding the balance between individual objectives. Instead, the user can tell our system particular thresholds for the expected changes in objectives, e.g., to reduce objective 1 by 10 % while not increasing objective 2 by more than 5 %. Dynamic weight setting automatically adapts the weights to reach these thresholds or uses the violation scores to explain reasons for not reaching thresholds. It can not only be used for soft constraints, but also to determine weights when hard constraints are internally represented as soft constraints in meta-heuristics. While the methodology is general, we have implemented it in the context of a personnel scheduling framework of our industry partner and present a detailed evaluation on the domain of Bus Driver Scheduling, where its benefits can be seen in multiple scenarios.
{"title":"Dynamic Weight Setting for Personnel Scheduling with Many Objectives","authors":"Lucas Kletzander, Nysret Musliu","doi":"10.1609/icaps.v33i1.27231","DOIUrl":"https://doi.org/10.1609/icaps.v33i1.27231","url":null,"abstract":"When large sets of constraints and objectives are combined in a practical optimization problem, managing all these potentially conflicting goals can become very difficult and might require to solve an instance multiple times. First, an instance might be infeasible with the current constraints, in which case our system introduces a novel violation score to help identify the constraints that need to be relaxed for the next run. Second, multiple objectives are often combined using a linear combination with hand-crafted weights, which are very difficult to set such that the result matches the expectations regarding the balance between individual objectives. Instead, the user can tell our system particular thresholds for the expected changes in objectives, e.g., to reduce objective 1 by 10 % while not increasing objective 2 by more than 5 %. Dynamic weight setting automatically adapts the weights to reach these thresholds or uses the violation scores to explain reasons for not reaching thresholds. It can not only be used for soft constraints, but also to determine weights when hard constraints are internally represented as soft constraints in meta-heuristics. While the methodology is general, we have implemented it in the context of a personnel scheduling framework of our industry partner and present a detailed evaluation on the domain of Bus Driver Scheduling, where its benefits can be seen in multiple scenarios.","PeriodicalId":239898,"journal":{"name":"International Conference on Automated Planning and Scheduling","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125890234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-01DOI: 10.1609/icaps.v33i1.27205
Frederico Messa, A. Pereira
In this work, we study fully-observable non-deterministic (FOND) planning, which models uncertainty through actions with non-deterministic effects. We present a best-first heuristic search algorithm called AND* that searches the policy-space of the FOND task to find a solution policy. We generalize the concepts of optimality, admissibility, and goal-awareness for FOND. Using these new concepts, we formalize the concept of heuristic functions that can guide a policy-space search. We analyze different aspects of the general structure of FOND solutions to introduce and characterize a set of FOND heuristics that estimate how far a policy is from becoming a solution. One of these heuristics applies a novel insight. Guided by them AND* returns only solutions with the minimal possible number of mapped states. We systematically study these FOND heuristics theoretically and empirically. We observe that our best heuristic makes AND* much more effective than the straightforward heuristics. We believe that our work allows a better understanding of how to design algorithms and heuristics to solve FOND tasks.
{"title":"A Best-First Search Algorithm for FOND Planning and Heuristic Functions to Optimize Decompressed Solution Size","authors":"Frederico Messa, A. Pereira","doi":"10.1609/icaps.v33i1.27205","DOIUrl":"https://doi.org/10.1609/icaps.v33i1.27205","url":null,"abstract":"In this work, we study fully-observable non-deterministic (FOND) planning, which models uncertainty through actions with non-deterministic effects. We present a best-first heuristic search algorithm called AND* that searches the policy-space of the FOND task to find a solution policy. We generalize the concepts of optimality, admissibility, and goal-awareness for FOND. Using these new concepts, we formalize the concept of heuristic functions that can guide a policy-space search. We analyze different aspects of the general structure of FOND solutions to introduce and characterize a set of FOND heuristics that estimate how far a policy is from becoming a solution. One of these heuristics applies a novel insight. Guided by them AND* returns only solutions with the minimal possible number of mapped states. We systematically study these FOND heuristics theoretically and empirically. We observe that our best heuristic makes AND* much more effective than the straightforward heuristics. We believe that our work allows a better understanding of how to design algorithms and heuristics to solve FOND tasks.","PeriodicalId":239898,"journal":{"name":"International Conference on Automated Planning and Scheduling","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124825579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-01DOI: 10.1609/icaps.v33i1.27204
R. Mencía, Carlos Mencía, Joao Marques-Silva
This paper addresses the tasks of explaining and correcting infeasible one machine sequencing problems with a limit on the makespan. Concretely, the paper studies the computation of high-level explanations and corrections, which are given in terms of irreducible subsets of the set of jobs. To achieve these goals, the paper shows that both tasks can be reduced to the general framework of computing a minimal set over a monotone predicate (MSMP). The reductions enable the use of any general-purpose algorithm for solving MSMP, and three well-known approaches are instantiated for the two tasks. Furthermore, the paper details efficient scheduling techniques aimed at enhancing the performance of the proposed algorithms. The experimental results confirm that the proposed approaches are efficient in practice, and that the scheduling optimizations enable critical performance gains.
{"title":"Efficient Reasoning about Infeasible One Machine Sequencing","authors":"R. Mencía, Carlos Mencía, Joao Marques-Silva","doi":"10.1609/icaps.v33i1.27204","DOIUrl":"https://doi.org/10.1609/icaps.v33i1.27204","url":null,"abstract":"This paper addresses the tasks of explaining and correcting infeasible one machine sequencing problems with a limit on the makespan. Concretely, the paper studies the computation of high-level explanations and corrections, which are given in terms of irreducible subsets of the set of jobs. To achieve these goals, the paper shows that both tasks can be reduced to the general framework of computing a minimal set over a monotone predicate (MSMP). The reductions enable the use of any general-purpose algorithm for solving MSMP, and three well-known approaches are instantiated for the two tasks. Furthermore, the paper details efficient scheduling techniques aimed at enhancing the performance of the proposed algorithms. The experimental results confirm that the proposed approaches are efficient in practice, and that the scheduling optimizations enable critical performance gains.","PeriodicalId":239898,"journal":{"name":"International Conference on Automated Planning and Scheduling","volume":"4221 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129363101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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