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Documents authored by Beck, J. Christopher

Document
DOI: 10.4230/LIPIcs.CP.2024.12
Using Constraint Programming for Disjunctive Scheduling in Temporal AI Planning

Authors: Adam Francis Green, J. Christopher Beck, and Amanda Coles

Published in: LIPIcs, Volume 307, 30th International Conference on Principles and Practice of Constraint Programming (CP 2024)

Abstract
We present a novel scheduling model that leverages Constraint Programming (CP) to enhance problem solving performance in Temporal Planning. Building on the established strategy of decomposing causal and temporal reasoning, our approach abstracts two common fact structures present in many Temporal Planning problems - Semaphores and Envelopes - and performs temporal reasoning in a CP-based scheduler. At each search node in a heuristic search for a temporal plan, we construct and solve a Constraint Satisfaction Problem (CSP) and integrate feedback from the CP-based scheduler to guide the causal planning search towards a solution. Through experimental analysis, we validate the impact of these advances, demonstrating a significant reduction in both the number of states searched and in search time alongside an increase in problem-solving coverage.
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Adam Francis Green, J. Christopher Beck, and Amanda Coles. Using Constraint Programming for Disjunctive Scheduling in Temporal AI Planning. In 30th International Conference on Principles and Practice of Constraint Programming (CP 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 307, pp. 12:1-12:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{francisgreen_et_al:LIPIcs.CP.2024.12,
  author =	{Francis Green, Adam and Beck, J. Christopher and Coles, Amanda},
  title =	{{Using Constraint Programming for Disjunctive Scheduling in Temporal AI Planning}},
  booktitle =	{30th International Conference on Principles and Practice of Constraint Programming (CP 2024)},
  pages =	{12:1--12:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-336-2},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{307},
  editor =	{Shaw, Paul},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2024.12},
  URN =		{urn:nbn:de:0030-drops-206974},
  doi =		{10.4230/LIPIcs.CP.2024.12},
  annote =	{Keywords: AI Planning, Temporal-Numeric Planning, Constraint Programming, Scheduling}
}
Document
DOI: 10.4230/LIPIcs.CP.2024.32
Solving LBBD Master Problems with Constraint Programming and Domain-Independent Dynamic Programming

Authors: Jiachen Zhang and J. Christopher Beck

Published in: LIPIcs, Volume 307, 30th International Conference on Principles and Practice of Constraint Programming (CP 2024)

Abstract
We investigate using Constraint Programming (CP) and Domain-Independent Dynamic Programming (DIDP) to solve the master problem in Logic-based Benders Decomposition (LBBD) models, in particular addressing the challenge of feasibility cut formulation. For CP, we exploit key variable manipulation, constraint-based expressions, and global constraints to construct three combinatorial cut encodings. For the state-based DIDP model, we propose two cut encoding approaches: using additional preconditions of state transitions or adding state constraints. Each of these approaches can be modeled using integer numeric variables or set variables, resulting in four novel encodings. We apply the three CP variants and four DIDP variants to simple assembly line balancing problems with sequence-dependent setup times type-1 (SUALBP-1). Experimental results show all approaches outperform a mixed-integer programming (MIP) based master problem and the state-of-the-art monolithic MIP model, with the three CP variants being superior to all of the DIDP approaches.
Cite as

Jiachen Zhang and J. Christopher Beck. Solving LBBD Master Problems with Constraint Programming and Domain-Independent Dynamic Programming. In 30th International Conference on Principles and Practice of Constraint Programming (CP 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 307, pp. 32:1-32:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{zhang_et_al:LIPIcs.CP.2024.32,
  author =	{Zhang, Jiachen and Beck, J. Christopher},
  title =	{{Solving LBBD Master Problems with Constraint Programming and Domain-Independent Dynamic Programming}},
  booktitle =	{30th International Conference on Principles and Practice of Constraint Programming (CP 2024)},
  pages =	{32:1--32:21},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-336-2},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{307},
  editor =	{Shaw, Paul},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2024.32},
  URN =		{urn:nbn:de:0030-drops-207171},
  doi =		{10.4230/LIPIcs.CP.2024.32},
  annote =	{Keywords: constraint programming, domain-independent dynamic programming, logic-based Benders decomposition, assembly line balancing, sequence-dependent setup}
}
Document
DOI: 10.4230/LIPIcs.CP.2023.17
Optimization Models for Pickup-And-Delivery Problems with Reconfigurable Capacities

Authors: Arnoosh Golestanian, Giovanni Lo Bianco, Chengyu Tao, and J. Christopher Beck

Published in: LIPIcs, Volume 280, 29th International Conference on Principles and Practice of Constraint Programming (CP 2023)

Abstract
When a transportation service accommodates both people and goods, operators sometimes opt for vehicles that can be dynamically reconfigured for different demands. Motivated by air service in remote communities in Canada’s north, we define a pickup-and-delivery problem in which aircraft can add or remove seats during a multi-stop trip to accommodate varying demands. Given the demand for people and cargo as well as a seat inventory at each location, the problem consists in finding a tour that picks up and delivers all demand while potentially reconfiguring the vehicle capacity at each location by adding or removing seats. We develop a total of six models using three different approaches: constraint programming, mixed integer programming, and domain-independent dynamic programming. Our numerical experiments indicate that domain-independent dynamic programming is able to substantially outperform the other technologies on both solution quality and run-time on a set of randomly generated instances spanning the size of real problems in northern Canada.
Cite as

Arnoosh Golestanian, Giovanni Lo Bianco, Chengyu Tao, and J. Christopher Beck. Optimization Models for Pickup-And-Delivery Problems with Reconfigurable Capacities. In 29th International Conference on Principles and Practice of Constraint Programming (CP 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 280, pp. 17:1-17:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{golestanian_et_al:LIPIcs.CP.2023.17,
  author =	{Golestanian, Arnoosh and Bianco, Giovanni Lo and Tao, Chengyu and Beck, J. Christopher},
  title =	{{Optimization Models for Pickup-And-Delivery Problems with Reconfigurable Capacities}},
  booktitle =	{29th International Conference on Principles and Practice of Constraint Programming (CP 2023)},
  pages =	{17:1--17:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-300-3},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{280},
  editor =	{Yap, Roland H. C.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2023.17},
  URN =		{urn:nbn:de:0030-drops-190542},
  doi =		{10.4230/LIPIcs.CP.2023.17},
  annote =	{Keywords: Pickup and delivery, Dial-a-ride problem, Optimization}
}
Document
DOI: 10.4230/LIPIcs.CP.2023.23
Large Neighborhood Beam Search for Domain-Independent Dynamic Programming

Authors: Ryo Kuroiwa and J. Christopher Beck

Published in: LIPIcs, Volume 280, 29th International Conference on Principles and Practice of Constraint Programming (CP 2023)

Abstract
Large neighborhood search (LNS) is an algorithmic framework that removes a part of a solution and performs search in the induced search space to find a better solution. While LNS shows strong performance in constraint programming, little work has combined LNS with state space search. We propose large neighborhood beam search (LNBS), a combination of LNS and state space search. Given a solution path, LNBS removes a partial path between two states and then performs beam search to find a better partial path. We apply LNBS to domain-independent dynamic programming (DIDP), a recently proposed generic framework for combinatorial optimization based on dynamic programming. We empirically show that LNBS finds better quality solutions than a state-of-the-art DIDP solver in five out of nine benchmark problem types with a total of 8570 problem instances. In particular, LNBS shows a significant improvement over the existing state-of-the-art DIDP solver in routing and scheduling problems.
Cite as

Ryo Kuroiwa and J. Christopher Beck. Large Neighborhood Beam Search for Domain-Independent Dynamic Programming. In 29th International Conference on Principles and Practice of Constraint Programming (CP 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 280, pp. 23:1-23:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{kuroiwa_et_al:LIPIcs.CP.2023.23,
  author =	{Kuroiwa, Ryo and Beck, J. Christopher},
  title =	{{Large Neighborhood Beam Search for Domain-Independent Dynamic Programming}},
  booktitle =	{29th International Conference on Principles and Practice of Constraint Programming (CP 2023)},
  pages =	{23:1--23:22},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-300-3},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{280},
  editor =	{Yap, Roland H. C.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2023.23},
  URN =		{urn:nbn:de:0030-drops-190605},
  doi =		{10.4230/LIPIcs.CP.2023.23},
  annote =	{Keywords: Large Neighborhood Search, Dynamic Programming, State Space Search, Combinatorial Optimization}
}
Document
DOI: 10.4230/LIPIcs.CP.2021.35
Counterfactual Explanations via Inverse Constraint Programming

Authors: Anton Korikov and J. Christopher Beck

Published in: LIPIcs, Volume 210, 27th International Conference on Principles and Practice of Constraint Programming (CP 2021)

Abstract
It is increasingly recognized that automated decision making systems cannot be black boxes: users require insight into the reasons that decisions are made. Explainable AI (XAI) has developed a number of approaches to this challenge, including the framework of counterfactual explanations where an explanation takes the form of the minimal change to the world required for a user’s desired decisions to be made. Building on recent work, we show that for a user query specifying an assignment to a subset of variables, a counterfactual explanation can be found using inverse optimization. Thus, we develop inverse constraint programming (CP): to our knowledge, the first definition and treatment of inverse optimization in constraint programming. We modify a cutting plane algorithm for inverse mixed-integer programming (MIP), resulting in both pure and hybrid inverse CP algorithms. We evaluate the performance of these algorithms in generating counterfactual explanations for two combinatorial optimization problems: the 0-1 knapsack problem and single machine scheduling with release dates. Our numerical experiments show that a MIP-CP hybrid approach extended with a novel early stopping criteria can substantially out-perform a MIP approach particularly when CP is the state of the art for the underlying optimization problem.
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Anton Korikov and J. Christopher Beck. Counterfactual Explanations via Inverse Constraint Programming. In 27th International Conference on Principles and Practice of Constraint Programming (CP 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 210, pp. 35:1-35:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{korikov_et_al:LIPIcs.CP.2021.35,
  author =	{Korikov, Anton and Beck, J. Christopher},
  title =	{{Counterfactual Explanations via Inverse Constraint Programming}},
  booktitle =	{27th International Conference on Principles and Practice of Constraint Programming (CP 2021)},
  pages =	{35:1--35:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-211-2},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{210},
  editor =	{Michel, Laurent D.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2021.35},
  URN =		{urn:nbn:de:0030-drops-153261},
  doi =		{10.4230/LIPIcs.CP.2021.35},
  annote =	{Keywords: Explanation, Inverse Optimization, Scheduling}
}
Document
DOI: 10.4230/DagRep.8.2.26
Planning and Operations Research (Dagstuhl Seminar 18071)

Authors: J. Christopher Beck, Daniele Magazzeni, Gabriele Röger, and Willem-Jan Van Hoeve

Published in: Dagstuhl Reports, Volume 8, Issue 2 (2018)

Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 18071 "Planning and Operations Research". The seminar brought together researchers in the areas of Artificial Intelligence (AI) Planning, Constraint Programming, and Operations Research. All three areas have in common that they deal with complex systems where a huge space of interacting options makes it almost impossible to humans to take optimal or even good decisions. From a historical perspective, operations research stems from the application of mathematical methods to (mostly) industrial applications while planning and constraint programming emerged as subfields of artificial intelligence where the emphasis was traditionally more on symbolic and logical search techniques for the intelligent selection and sequencing of actions to achieve a set of goals. Therefore operations research often focuses on the allocation of scarce resources such as transportation capacity, machine availability, production materials, or money, while planning focuses on the right choice of actions from a large space of possibilities. While this difference results in problems in different complexity classes, it is often possible to cast the same problem as an OR, CP, or planning problem. In this seminar, we investigated the commonalities and the overlap between the different areas to learn from each other's expertise, bring the communities closer together, and transfer knowledge about solution techniques that can be applied in all areas.
Cite as

J. Christopher Beck, Daniele Magazzeni, Gabriele Röger, and Willem-Jan Van Hoeve. Planning and Operations Research (Dagstuhl Seminar 18071). In Dagstuhl Reports, Volume 8, Issue 2, pp. 26-63, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@Article{beck_et_al:DagRep.8.2.26,
  author =	{Beck, J. Christopher and Magazzeni, Daniele and R\"{o}ger, Gabriele and Van Hoeve, Willem-Jan},
  title =	{{Planning and Operations Research (Dagstuhl Seminar 18071)}},
  pages =	{26--63},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2018},
  volume =	{8},
  number =	{2},
  editor =	{Beck, J. Christopher and Magazzeni, Daniele and R\"{o}ger, Gabriele and Van Hoeve, Willem-Jan},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.8.2.26},
  URN =		{urn:nbn:de:0030-drops-92894},
  doi =		{10.4230/DagRep.8.2.26},
  annote =	{Keywords: Artificial Intelligence, Automated Planning and Scheduling, Constraint Programming, Dynamic Programming, Heuristic Search, Mixed Integer Programming, Operations Research, Optimization, Real-world Applications, Reasoning under Uncertainty}
}
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