Visual search tree profiling

Shishmarev, Maxim; Mears, Christopher; Tack, Guido; Banda, Maria García de la

doi:10.1007/s10601-015-9202-1

Cited by 9 publications

(3 citation statements)

References 16 publications

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“…The area of constraint programming, in contrast to NLP, received a lot of attention from visualization practitioners. Most of the work done in this area focuses on visualizing the search tree resulting from constraint programs (Carro and Hermenegildo 2000b;Pu and Lalanne 2000;Simonis et al 2010;Shishmarev et al 2016;Goodwin et al 2017). However, these techniques cannot be applied to NLP, due to the differences in modeling and solvers (Heipcke 1999).…”

Section: A B C D E F G Hmentioning

confidence: 99%

Visual analytics for nonlinear programming in robot motion planning

Hägele

Abdelaal

Oguz

et al. 2021

J Vis

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Nonlinear programming is a complex methodology where a problem is mathematically expressed in terms of optimality while imposing constraints on feasibility. Such problems are formulated by humans and solved by optimization algorithms. We support domain experts in their challenging tasks of understanding and troubleshooting optimization runs of intricate and high-dimensional nonlinear programs through a visual analytics system. The system was designed for our collaborators’ robot motion planning problems, but is domain agnostic in most parts of the visualizations. It allows for an exploration of the iterative solving process of a nonlinear program through several linked views of the computational process. We give insights into this design study, demonstrate our system for selected real-world cases, and discuss the extension of visualization and visual analytics methods for nonlinear programming. Graphic abstract

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Section: A B C D E F G Hmentioning

confidence: 99%

Visual analytics for nonlinear programming in robot motion planning

Hägele

Abdelaal

Oguz

et al. 2021

J Vis

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“…However their system only focuses on visualizing the solving process and not modeling. Recently Goodwin et al [25] described a user-centered design process for tools that visualize the solving process, building on earlier work by Shishmarev et al [72]. From an Information Visualization perspective Goodwin et al [25] looked at how different visualizations could be useful in the process of profiling constraint models.…”

Section: Constraint Programming and Related Areasmentioning

confidence: 99%

How People Visually Represent Discrete Constraint Problems

Zhu

Nacenta

Akgün

et al. 2020

IEEE Trans. Visual. Comput. Graphics

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Problems such as timetabling or personnel allocation can be modeled and solved using discrete constraint programming languages. However, while existing constraint solving software solves such problems quickly in many cases, these systems involve specialized languages that require significant time and effort to learn and apply. These languages are typically text-based and often difficult to interpret and understand quickly, especially for people without engineering or mathematics backgrounds. Visualization could provide an alternative way to model and understand such problems. Although many visual programming languages exist for procedural languages, visual encoding of problem specifications has not received much attention. Future problem visualization languages could represent problem elements and their constraints unambiguously, but without unnecessary cognitive burdens for those needing to translate their problem's mental representation into diagrams. As a first step towards such languages, we executed a study that catalogs how people represent constraint problems graphically. We studied three groups with different expertise: non-computer scientists, computer scientists and constraint programmers and analyzed their marks on paper (e.g., arrows), gestures (e.g., pointing) and the mappings to problem concepts (e.g., containers, sets). We provide foundations to guide future tool designs allowing people to effectively grasp, model and solve problems through visual representations.

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“…The DiS-CiPl project provides extensive visual functionalities to develop, test, and debug constraint logic programs such as displaying variables' states, effect of constraints and global constraints, event propagation at each node of the search tree, and identifying isomorphic subtrees [11,38]. Many useful methodologies from the DiSCiPl project are implemented in CP-Viz [39] and other works [37]. The OZ Explorer displays the search tree allowing the user to access detailed information about each tree node and to collapse and expand failing trees for closer examination [34].…”

Section: Background and Related Workmentioning

confidence: 99%

Visualizations to Summarize Search Behavior

Howell

Choueiry

2020

Lecture Notes in Computer Science

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In this paper, we argue that metrics that assess the performance of backtrack search for solving a Constraint Satisfaction Problem should not be visualized and examined only at the end of search, but their evolution should be tracked throughout the search process in order to provide a more complete picture of the behavior of search. We describe a process that organizes search history by automatically recognizing qualitatively significant changes in the metrics that assess search performance. To this end, we introduce a criterion for quantifying change between two time instants and a summarization technique for organizing the history of search at controllable levels of abstraction. We validate our approach in the context of two algorithms for enforcing consistency: one that is activated by a surge of backtracking and the second that modifies the structure of the constraint graph. We also introduce a new visualization for exposing the behavior of variable ordering heuristics and validate its usefulness both as a standalone tool and when displayed alongside search history.

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Visual search tree profiling

Cited by 9 publications

References 16 publications

Visual analytics for nonlinear programming in robot motion planning

Visual analytics for nonlinear programming in robot motion planning

How People Visually Represent Discrete Constraint Problems

Visualizations to Summarize Search Behavior

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