scholarly journals A game co-design method to elicit knowledge for the contextualization of spatial models

2021 ◽  
pp. 239980832110413
Author(s):  
Carissa J Champlin ◽  
Johannes Flacke ◽  
Geert PMR Dewulf
Keyword(s):  
Information Needs ◽  
Design Method ◽  
Spatial Models ◽  
Nominal Group ◽  
Planning Support ◽  
Knowledge Based ◽  
Group Settings ◽  
Knowledge Based Planning ◽  
Game Environment ◽  
Context Specific

A frequent criticism of knowledge-based planning tools is the apparent mismatch between information frameworks used in their spatial models and the information needs of planning actors. Increasingly, these actors are contributing their context-specific knowledge during the development of such tools. Transferring this knowledge from actors to the model remains a challenge. This study establishes a set of design requirements for knowledge elicitation in small group settings and introduces game co-design as a method allowing planning actors and planning support experts to meet halfway between the technology and user domains in the so-called third space. We present an initial case where in three nominal group sessions, actors encountered and critiqued parameterized assumptions of their planning issues in a tangible game environment. Findings indicate that the method can elicit different types of knowledge (divergence) about a spatial system in operationalized terms (formalization). We discuss the potential of tangible game co-design as a modeling as learning exercise and its complementarity to dedicated digital technologies for more holistic planning support.

Knowledge-Based Planning Using Planning Support Systems

2011 ◽  
pp. 203-217 ◽  
Author(s):  
Guido Vonk ◽  
Stan Geertman
Keyword(s):  
Support Systems ◽  
Planning Support Systems ◽  
Planning Practice ◽  
Planning Support ◽  
Knowledge Based ◽  
Knowledge Based Planning ◽  
Potential Benefits ◽  
Planning Processes

Planning support systems are geo-information-based tools to support those involved in planning tasks. Many see planning support systems capable of improving the handling of knowledge and information in planning processes. Better handling of knowledge and information could help those involved in planning to handle the ever-increasing complexity of planning tasks. In spite of these potential benefits, planning support systems have not yet become widely used in planning practice. A major problem contributing to the arrear in planning support systems use is the profound miscommunication that exists between planners, planning support systems developers, and researchers. Currently there exists little insight in how to solve this problem and enhance the use of planning support systems. This chapter provides practiceoriented lessons about PSS use. Those willing to apply planning support systems in planning practice may use these lessons to enhance the successful use of planning support systems and the handling of knowledge and information in planning.

Knowledge-Based Planning

1993 ◽  
Author(s):  
Drew McDermott ◽  
Gregory Hager
Keyword(s):  
Knowledge Based ◽  
Knowledge Based Planning

scholarly journals OpenKBP: The open‐access knowledge‐based planning grand challenge and dataset

2021 ◽  
Author(s):  
Aaron Babier ◽  
Binghao Zhang ◽  
Rafid Mahmood ◽  
Kevin L. Moore ◽  
Thomas G. Purdie ◽  
...  
Keyword(s):  
Open Access ◽  
Grand Challenge ◽  
Knowledge Based ◽  
Knowledge Based Planning

scholarly journals Evaluation of a highly refined prediction model in knowledge-based volumetric modulated arc therapy planning for cervical cancer

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Mingli Wang ◽  
Huikuan Gu ◽  
Jiang Hu ◽  
Jian Liang ◽  
Sisi Xu ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Prediction Model ◽  
Volumetric Modulated Arc Therapy ◽  
Model Evolution ◽  
Prediction Ability ◽  
Knowledge Based ◽  
Proposed Model ◽  
Arc Therapy ◽  
Knowledge Based Planning

Abstract Background and purpose To explore whether a highly refined dose volume histograms (DVH) prediction model can improve the accuracy and reliability of knowledge-based volumetric modulated arc therapy (VMAT) planning for cervical cancer. Methods and materials The proposed model underwent repeated refining through progressive training until the training samples increased from initial 25 prior plans up to 100 cases. The estimated DVHs derived from the prediction models of different runs of training were compared in 35 new cervical cancer patients to analyze the effect of such an interactive plan and model evolution method. The reliability and efficiency of knowledge-based planning (KBP) using this highly refined model in improving the consistency and quality of the VMAT plans were also evaluated. Results The prediction ability was reinforced with the increased number of refinements in terms of normal tissue sparing. With enhanced prediction accuracy, more than 60% of automatic plan-6 (AP-6) plans (22/35) can be directly approved for clinical treatment without any manual revision. The plan quality scores for clinically approved plans (CPs) and manual plans (MPs) were on average 89.02 ± 4.83 and 86.48 ± 3.92 (p < 0.001). Knowledge-based planning significantly reduced the Dmean and V18 Gy for kidney (L/R), the Dmean, V30 Gy, and V40 Gy for bladder, rectum, and femoral head (L/R). Conclusion The proposed model evolution method provides a practical way for the KBP to enhance its prediction ability with minimal human intervene. This highly refined prediction model can better guide KBP in improving the consistency and quality of the VMAT plans.

scholarly journals Automated Knowledge-Based Intensity-Modulated Proton Planning: An International Multicenter Benchmarking Study

Cancers ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 420 ◽  
Author(s):  
Alexander Delaney ◽  
Lei Dong ◽  
Anthony Mascia ◽  
Wei Zou ◽  
Yongbin Zhang ◽  
...  
Keyword(s):  
Target Volume ◽  
Single Center ◽  
Intensity Modulated Proton Therapy ◽  
Intensity Modulated ◽  
Knowledge Based ◽  
Model Library ◽  
Beam Angle Optimization ◽  
Knowledge Based Planning ◽  
Automated Knowledge ◽  
Comparable Quality

Background: Radiotherapy treatment planning is increasingly automated and knowledge-based planning has been shown to match and sometimes improve upon manual clinical plans, with increased consistency and efficiency. In this study, we benchmarked a novel prototype knowledge-based intensity-modulated proton therapy (IMPT) planning solution, against three international proton centers. Methods: A model library was constructed, comprising 50 head and neck cancer (HNC) manual IMPT plans from a single center. Three external-centers each provided seven manual benchmark IMPT plans. A knowledge-based plan (KBP) using a standard beam arrangement for each patient was compared with the benchmark plan on the basis of planning target volume (PTV) coverage and homogeneity and mean organ-at-risk (OAR) dose. Results: PTV coverage and homogeneity of KBPs and benchmark plans were comparable. KBP mean OAR dose was lower in 32/54, 45/48 and 38/53 OARs from center-A, -B and -C, with 23/32, 38/45 and 23/38 being >2 Gy improvements, respectively. In isolated cases the standard beam arrangement or an OAR not being included in the model or being contoured differently, led to higher individual KBP OAR doses. Generating a KBP typically required <10 min. Conclusions: A knowledge-based IMPT planning solution using a single-center model could efficiently generate plans of comparable quality to manual HNC IMPT plans from centers with differing planning aims. Occasional higher KBP OAR doses highlight the need for beam angle optimization and manual review of KBPs. The solution furthermore demonstrated the potential for robust optimization.

Design of Mine Ventilation Systems Based on Artificial Intelligence

2014 ◽  
Vol 614 ◽  
pp. 107-112
Author(s):  
Xiao Yu Yin ◽  
Xian Ping Xie ◽  
Zhen Li ◽  
Jian Gong Li ◽  
Ting Jun Wang ◽  
...  
Keyword(s):  
Expert System ◽  
Computer Aided Design ◽  
Design Method ◽  
Mine Ventilation ◽  
Design System ◽  
Knowledge Based ◽  
Aided Design ◽  
And Control ◽  
Intelligent Computer ◽  
Ventilation Systems

Expert systems, or knowledge based systems, are programs in which the answer to a user-posed question is reached by logical or plausible inference rather than strictly by calculation, although calculation routines can form a major part of an expert system. Based on the integration of expert system technology and optimization technology, an intelligent computer aided design method for mine ventilation systems is proposed in this paper. Firstly, the structure and control algorithm of the intelligent design system are explored. Secondly, the knowledge types required for the mine ventilation expert system and the acquiring method of knowledge are discussed. Finally, the inference method of this expert system is put forward.

Sign in / Sign up

Export Citation Format

Share Document