Exploring Directional Path-Consistency for Solving Constraint Networks

Kong, Shufeng; Li, Sanjiang; Sioutis, Michaël

doi:10.1093/comjnl/bxx122

Cited by 3 publications

(1 citation statement)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Constraint propagation is a common technique that utilizes local consistency to eliminate a subset of inconsistent ethics. There are several tried-and-tested consistency algorithms, including arc consistency (AC) [7], singleton arc consistency (SAC) [8], path consistency (PC) [9], and maxRPC [10].…”

Section: Introductionmentioning

confidence: 99%

Two Improved Constraint-Solving Algorithms Based on lmaxRPC3rm

Pan,

Cheng,

Zhang

2023

Symmetry

View full text Add to dashboard Cite

The Constraint Satisfaction Problem (CSP) is a significant research area in artificial intelligence, and includes a large number of symmetric or asymmetric structures. A backtracking search combined with constraint propagation is considered to be the best CSP-solving algorithm, and the consistency algorithm is the main algorithm used in the process of constraint propagation, which is the key factor in constraint-solving efficiency. Max-restricted path consistency (maxRPC) is a well-known and efficient consistency algorithm, whereas the lmaxRPC3rm algorithm is a classic lightweight algorithm for maxRPC. In this paper, we leverage the properties of symmetry to devise an improved pruning strategy aimed at efficiently diminishing the problem’s search space, thus enhancing the overall solving efficiency. Firstly, we propose the maxRPC3sim algorithm, which abandons the two complex data structures used by lmaxRPC3rm. We can render the algorithm to be more concise and competitive compared to the original algorithm while ensuring that it maintains the same average performance. Secondly, inspired by the RCP3 algorithm, we propose the maxRPC3simR algorithm, which uses the idea of residual support to cut down the redundant operation of the lmaxRPC3rm algorithm. Finally, combining the domain/weighted degree (dom/wdeg) heuristic with the activity-based search (ABS) heuristic, a new variable ordering heuristic, ADW, is proposed. Our heuristic prioritizes the selection of variables with symmetry for pruning, further enhancing the algorithm’s pruning capabilities. Experiments were conducted on both random and structural problems separately. The results indicate that our two algorithms generally outperform other algorithms in terms of performance on both problem classes. Moreover, the new heuristic algorithm demonstrates enhanced robustness across different problem types when compared to various existing algorithms.

show abstract

Section: Introductionmentioning

confidence: 99%

Two Improved Constraint-Solving Algorithms Based on lmaxRPC3rm

Pan,

Cheng,

Zhang

2023

Symmetry

View full text Add to dashboard Cite

show abstract

Just-In-Time Constraint-Based Inference for Qualitative Spatial and Temporal Reasoning

Sioutis

2020

Künstl Intell

View full text Add to dashboard Cite

We discuss a research roadmap for going beyond the state of the art in qualitative spatial and temporal reasoning (QSTR). Simply put, QSTR is a major field of study in Artificial Intelligence that abstracts from numerical quantities of space and time by using qualitative descriptions instead (e.g., precedes, contains, is left of); thus, it provides a concise framework that allows for rather inexpensive reasoning about entities located in space or time. Applications of QSTR can be found in a plethora of areas and domains such as smart environments, intelligent vehicles, and unmanned aircraft systems. Our discussion involves researching novel local consistencies in the aforementioned discipline, defining dynamic algorithms pertaining to these consistencies that can allow for efficient reasoning over changing spatio-temporal information, and leveraging the structures of the locally consistent related problems with regard to novel decomposability and theoretical tractability properties. Ultimately, we argue for pushing the envelope in QSTR via defining tools for tackling dynamic variants of the fundamental reasoning problems in this discipline, i.e., problems stated in terms of changing input data. Indeed, time is a continuous flow and spatial objects can change (e.g., in shape, size, or structure) as time passes; therefore, it is pertinent to be able to efficiently reason about dynamic spatio-temporal data. Finally, these tools are to be integrated into the larger context of highly active areas such as neuro-symbolic learning and reasoning, planning, data mining, and robotic applications. Our final goal is to inspire further discussion in the community about constraint-based QSTR in general, and the possible lines of future research that we outline here in particular.

show abstract

Graph constraints refined for transitive relations

Zhen

Zhang

2023

Knowledge-Based Systems

View full text Add to dashboard Cite

Exploring Directional Path-Consistency for Solving Constraint Networks

Cited by 3 publications

References 32 publications

Two Improved Constraint-Solving Algorithms Based on lmaxRPC3rm

Two Improved Constraint-Solving Algorithms Based on lmaxRPC3rm

Just-In-Time Constraint-Based Inference for Qualitative Spatial and Temporal Reasoning

Graph constraints refined for transitive relations

Contact Info

Product

Resources

About