Comparing HTN Planning Method With Grammar-Based Approach in Generative Design

2000 ◽  
Author(s):  
Hai Shi ◽  
Linda C. Schmidt
Keyword(s):  
Artificial Intelligence ◽  
Conceptual Design ◽  
Planning Method ◽  
Future Research ◽  
Planning Problem ◽  
Generative Design ◽  
Artificial Intelligence Planning ◽  
Design Alternatives ◽  
Htn Planning ◽  
Initial Comparison

Abstract In mechanical conceptual design, the more design alternatives generated, the higher the benefit to designers. In this paper we explore the use of HTN planning, an artificial intelligence planning method, to perform generative conceptual design. The HTN planning method is “goal driven” while the grammar method is “feasibility driven”. We mapped a grammar-based generative method for conceptual design of Meccano carts into an HTN planning problem format. An initial comparison of the two methods is provided in this paper. Exploring the use of a planning method provides a benchmark for future research in generative design.

Pipesworld: Applying Planning Systems to Pipeline Transportation

2004 ◽  
Author(s):  
Ruy L. Milidiu´ ◽  
Frederico dos Santos Liporace
Keyword(s):  
Artificial Intelligence ◽  
Transportation Problem ◽  
General Purpose ◽  
Benchmark Problems ◽  
Planning Problem ◽  
Due Dates ◽  
Planning Systems ◽  
Pipeline Transportation ◽  
Specific Knowledge ◽  
Artificial Intelligence Planning

Most transportation problems consist of moving carriers of stationary cargo. Pipelines are unique in the sense that they are stationary carriers of moving cargo. As a consequence, the planning problem of these systems has singularities that make it very challenging. In this paper we present the Pipesworld model, a transportation problem inspired by the transportation of petroleum derivatives in Petrobras’ pipelines. Pipesworld takes into account important features like product interface constraints, limited product storage capacities and due dates for product delivery. The relevance and unique characteristics of Pipesworld has been recognized by the Artificial Intelligence planning community. Pipesworld has been selected as one of the benchmark problems to be used in the Fourth International Planning Competition, a biannual event to benchmark the state-of-the-art general purpose artificial planning systems. We report the results obtained by general purpose artificial intelligence planning systems when applied to the Pipesworld instances. We also analyze how different modelling techniques may be used to significantly improve the planners’ performance. Although the basic algorithms of these planners do not incorporate any specific knowledge about the pipeline transportation problem, the results obtained so far are quite satisfactory. We also describe our current work in developing Plumber, a dedicated solver, aimed to tackle effective operational situations. Plumber uses general purpose planning techniques but also incorporates domain specific knowledge and may work together with a human expert during the planning process. By applying Plumber to the Pipesworld instances, we compare its performance against general purpose planning systems. Preliminary tests with a first version of Plumber shows that it already outperforms Fast-Forward (FF), one of the best available general purpose planning systems. This shows that improved versions of Plumber have the potential to effectively deal with pipeline transportation operational scenarios.

scholarly journals Reduce Artificial Intelligence Planning Effort by using Map-Reduce Paradigm

2021 ◽  
Vol 10 (7) ◽  
pp. 24-32
Author(s):  
Mohamed Elkawkagy* ◽  
Elbeh Heba
Keyword(s):  
Artificial Intelligence ◽  
Search Space ◽  
Processing Technique ◽  
Substantial Improvement ◽  
Domain Model ◽  
Map Reduce ◽  
Planning Problem ◽  
Ai Planning ◽  
Hierarchical Planning ◽  
Artificial Intelligence Planning

While several approaches have been developed to enhance the efficiency of hierarchical Artificial Intelligence planning (AI-planning), complex problems in AI-planning are challenging to overcome. To find a solution plan, the hierarchical planner produces a huge search space that may be infinite. A planner whose small search space is likely to be more efficient than a planner produces a large search space. In this paper, we will present a new approach to integrating hierarchical AI-planning with the map-reduce paradigm. In the mapping part, we will apply the proposed clustering technique to divide the hierarchical planning problem into smaller problems, so-called sub-problems. A pre-processing technique is conducted for each sub-problem to reduce a declarative hierarchical planning domain model and then find an individual solution for each so-called sub-problem sub-plan. In the reduction part, the conflict between sub-plans is resolved to provide a general solution plan to the given hierarchical AI-planning problem. Preprocessing phase helps the planner cut off the hierarchical planning search space for each sub-problem by removing the compulsory literal elements that help the hierarchical planner seek a solution. The proposed approach has been fully implemented successfully, and some experimental results findings will be provided as proof of our approach's substantial improvement inefficiency.

scholarly journals EVALUATION OF TOPOLOGY OPTIMIZATION AND GENERATIVE DESIGN TOOLS AS SUPPORT FOR CONCEPTUAL DESIGN

2020 ◽  
Vol 1 ◽  
pp. 451-460
Author(s):  
D. Vlah ◽  
R. Žavbi ◽  
N. Vukašinović
Keyword(s):  
Topology Optimization ◽  
Conceptual Design ◽  
Design Tools ◽  
Generative Design ◽  
Early Design ◽  
The Past ◽  
Design Alternatives ◽  
Early Phases

AbstractNowadays, a large number of different tools that support early phases of design are available to engineers. In the past decade a specialized set of CAD-based tools were developed, that support the ideation process by generating different design alternatives according to the criteria given by the designer. Two types of tools are discussed in this paper: topology optimization and generative design tools. To investigate to what extent these tools are suitable for use in early design phases and what are the main differences between them, a study was conducted on an industrial case.

scholarly journals Review on the Use of Artificial Intelligence to Predict Fire Performance of Construction Materials and Their Flame Retardancy

Molecules ◽  
2021 ◽  
Vol 26 (4) ◽  
pp. 1022
Author(s):  
Hoang T. Nguyen ◽  
Kate T. Q. Nguyen ◽  
Tu C. Le ◽  
Guomin Zhang
Keyword(s):  
Artificial Intelligence ◽  
Flame Retardancy ◽  
Material Science ◽  
Construction Materials ◽  
Engineering Problem ◽  
Future Research ◽  
Fire Performance ◽  
Structural Components ◽  
Comprehensive Overview ◽  
Fire Engineering

The evaluation and interpretation of the behavior of construction materials under fire conditions have been complicated. Over the last few years, artificial intelligence (AI) has emerged as a reliable method to tackle this engineering problem. This review summarizes existing studies that applied AI to predict the fire performance of different construction materials (e.g., concrete, steel, timber, and composites). The prediction of the flame retardancy of some structural components such as beams, columns, slabs, and connections by utilizing AI-based models is also discussed. The end of this review offers insights on the advantages, existing challenges, and recommendations for the development of AI techniques used to evaluate the fire performance of construction materials and their flame retardancy. This review offers a comprehensive overview to researchers in the fields of fire engineering and material science, and it encourages them to explore and consider the use of AI in future research projects.

scholarly journals Artificial intelligence in hospitals: providing a status quo of ethical considerations in academia to guide future research

AI & Society ◽  
2021 ◽  
Author(s):  
Milad Mirbabaie ◽  
Lennart Hofeditz ◽  
Nicholas R. J. Frick ◽  
Stefan Stieglitz
Keyword(s):  
Artificial Intelligence ◽  
Citation Network ◽  
Status Quo ◽  
Biomedical Ethics ◽  
Future Research ◽  
Ethical Considerations ◽  
Ethical Discourse ◽  
The Status ◽  
Clinical Environments ◽  
Research Questions

AbstractThe application of artificial intelligence (AI) in hospitals yields many advantages but also confronts healthcare with ethical questions and challenges. While various disciplines have conducted specific research on the ethical considerations of AI in hospitals, the literature still requires a holistic overview. By conducting a systematic discourse approach highlighted by expert interviews with healthcare specialists, we identified the status quo of interdisciplinary research in academia on ethical considerations and dimensions of AI in hospitals. We found 15 fundamental manuscripts by constructing a citation network for the ethical discourse, and we extracted actionable principles and their relationships. We provide an agenda to guide academia, framed under the principles of biomedical ethics. We provide an understanding of the current ethical discourse of AI in clinical environments, identify where further research is pressingly needed, and discuss additional research questions that should be addressed. We also guide practitioners to acknowledge AI-related benefits in hospitals and to understand the related ethical concerns.

scholarly journals Artificial Intelligence, 3D Documentation, and Rock Art—Approaching and Reflecting on the Automation of Identification and Classification of Rock Art Images

2021 ◽  
Author(s):  
Christian Horn ◽  
Oscar Ivarsson ◽  
Cecilia Lindhé ◽  
Rich Potter ◽  
Ashely Green ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Bronze Age ◽  
Data Augmentation ◽  
Rock Art ◽  
Training Data ◽  
Future Research ◽  
Rock Surface ◽  
Southern Scandinavia ◽  
Bounding Boxes

AbstractRock art carvings, which are best described as petroglyphs, were produced by removing parts of the rock surface to create a negative relief. This tradition was particularly strong during the Nordic Bronze Age (1700–550 BC) in southern Scandinavia with over 20,000 boats and thousands of humans, animals, wagons, etc. This vivid and highly engaging material provides quantitative data of high potential to understand Bronze Age social structures and ideologies. The ability to provide the technically best possible documentation and to automate identification and classification of images would help to take full advantage of the research potential of petroglyphs in southern Scandinavia and elsewhere. We, therefore, attempted to train a model that locates and classifies image objects using faster region-based convolutional neural network (Faster-RCNN) based on data produced by a novel method to improve visualizing the content of 3D documentations. A newly created layer of 3D rock art documentation provides the best data currently available and has reduced inscribed bias compared to older methods. Several models were trained based on input images annotated with bounding boxes produced with different parameters to find the best solution. The data included 4305 individual images in 408 scans of rock art sites. To enhance the models and enrich the training data, we used data augmentation and transfer learning. The successful models perform exceptionally well on boats and circles, as well as with human figures and wheels. This work was an interdisciplinary undertaking which led to important reflections about archaeology, digital humanities, and artificial intelligence. The reflections and the success represented by the trained models open novel avenues for future research on rock art.

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