Train Scheduling with Hybrid ASP

Abels, Dirk; Jordi, Julian; Ostrowski, Max; Schaub, Torsten; Toletti, Ambra; Wanko, Philipp

doi:10.1007/978-3-030-20528-7_1

Cited by 36 publications

(35 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, we tackle instances with approximately six hours of the full train schedule on a railway network covering approximately 200 km. For brevity, we omit slight grounding and propagation optimizations in the encoding presented in Section 4; the full encoding and instance set is available at https://github.com/potassco/train-scheduling-with-hy We use the best optimization configuration determined in (Abels et al 2019). In detail, we use two threads, one running with a model-guided optimization strategy iteratively producing models of descending cost until the optimum is found, and the other with a core-guided optimization strategy (Andres et al 2012) relying on successively identifying and relaxing unsatisfiable cores until a model is obtained.…”

Section: Methodsmentioning

confidence: 99%

“…7 Table 1 shows the average runtime (T) over all 25 instances, which includes grounding and solving, grounding time (GT), choices (CH) and conflicts (CO) for individual domain-specific 5 Both are development versions available on https://github.com/potassco/{clingoDL,clingo}/ with commit hashs #f1a185e and #f8a51134, respectively. 6 In particular, we dropped two heuristics proposed in (Abels et al 2019) since they did not improve solving performance with the new resource area and height-based encoding. 7 https://github.com/potassco/train-scheduling-with-hybrid-asp/.…”

Section: Methodsmentioning

confidence: 99%

“…Row INS contains the names of the 25 instances. Instance names starting with P are the nine instances originally used in (Abels et al 2019) that were published by SBB; they only contain part of the railway network between Zürich, Chur and Luzern. Two instances among them are marked with R and are reduced versions of the largest instance, viz.…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Train Scheduling with Hybrid Answer Set Programming

Abels¹,

Jordi²,

Ostrowski³

et al. 2020

Theory and Practice of Logic Programming

Self Cite

View full text Add to dashboard Cite

We present a solution to real-world train scheduling problems, involving routing, scheduling, and optimization, based on Answer Set Programming (ASP). To this end, we pursue a hybrid approach that extends ASP with difference constraints to account for a fine-grained timing. More precisely, we exemplarily show how the hybrid ASP system clingo [DL] can be used to tackle demanding planning-and-scheduling problems. In particular, we investigate how to boost performance by combining distinct ASP solving techniques, such as approximations and heuristics, with preprocessing and encoding techniques for tackling large-scale, real-world train scheduling instances.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Train Scheduling with Hybrid Answer Set Programming

Abels¹,

Jordi²,

Ostrowski³

et al. 2020

Theory and Practice of Logic Programming

Self Cite

View full text Add to dashboard Cite

show abstract

“…A more recent attempt to circumvent the grounding bottleneck is by extending ASP with specific problem solvers (e.g. temporal (Cabalar et al 2019), or difference-logic (Abels et al 2019) reasoners) (Gebser et al 2016) and then manually reformulating part of the original problem in the added formalism. Besides the need to develop and integrate the specific problem solvers, it requires users of ASP to be knowledgeable in another (unrelated) formalism to solve their problems.…”

Section: Related Workmentioning

confidence: 99%

Advancing Lazy-Grounding ASP Solving Techniques – Restarts, Phase Saving, Heuristics, and More

Weinzierl¹,

Taupe²,

Friedrich³

2020

Theory and Practice of Logic Programming

View full text Add to dashboard Cite

Answer-Set Programming (ASP) is a powerful and expressive knowledge representation paradigm with a significant number of applications in logic-based AI. The traditional ground-and-solve approach, however, requires ASP programs to be grounded upfront and thus suffers from the so-called grounding bottleneck (i.e., ASP programs easily exhaust all available memory and thus become unsolvable). As a remedy, lazy-grounding ASP solvers have been developed, but many state-of-the-art techniques for grounded ASP solving have not been available to them yet. In this work we present, for the first time, adaptions to the lazy-grounding setting for many important techniques, like restarts, phase saving, domain-independent heuristics, and learned-clause deletion. Furthermore, we investigate their effects and in general observe a large improvement in solving capabilities and also uncover negative effects in certain cases, indicating the need for portfolio solving as known from other solvers.

show abstract

“…ASP is already used in time-related solving in a lot of different contexts, from planning trains [2] to moving robots in wharehouse [25] [18]. There exist some dedicated tools like Telingo [6] that are used for planning.…”

Section: Introductionmentioning

confidence: 99%

Formalisation of Action with Durations in Answer Set Programming

Tignon¹

2021

Electron. Proc. Theor. Comput. Sci.

View full text Add to dashboard Cite

In this paper, I will discuss the work I am currently doing as a Ph.D. student at the University of Potsdam, under the tutoring of T. Schaub. I'm currently looking into action description in ASP. More precisely, my goal is to explore how to represent actions with durations in ASP, in different contexts. Right now, I'm focused on Multi-Agent Path Finding (MAPF), looking at how to represent speeds for different agents and contexts.Before tackling duration, I wanted to explore and compare different representations of action taking in ASP. For this, I started comparing different simple encodings tackling the MAPF problem. Even in simple code, choices and assumptions have been made in their creations. The objective of my work is to present the consequences of those design decisions in terms of performance and knowledge representation. As far as I know, there is no current research on this topic.Besides that, I'm also exploring different ways to represent duration and to solve related problems. I planed to compare them the same way I described before. I also want this to help me find innovative and effective ways to solve problems with duration.

show abstract

Train Scheduling with Hybrid ASP

Cited by 36 publications

References 9 publications

Train Scheduling with Hybrid Answer Set Programming

Train Scheduling with Hybrid Answer Set Programming

Advancing Lazy-Grounding ASP Solving Techniques – Restarts, Phase Saving, Heuristics, and More

Formalisation of Action with Durations in Answer Set Programming

Contact Info

Product

Resources

About