A Sensory Data-Driven Rule-Based Strategy for Error Recovery in Robotic Assembly

Humans use different domain languages to represent, explore, and communicate scientific concepts. During the last few hundred years, chemists compiled the language of chemical synthesis inferring a series of “reaction rules” from knowing how atoms rearrange during a chemical transformation, a process called atom-mapping. Atom-mapping is a laborious experimental task and, when tackled with computational methods, requires continuous annotation of chemical reactions and the extension of logically consistent directives. Here, we demonstrate that Transformer Neural Networks learn atom-mapping information between products and reactants without supervision or human labeling. Using the Transformer attention weights, we build a chemically agnostic, attention-guided reaction mapper and extract coherent chemical grammar from unannotated sets of reactions. Our method shows remarkable performance in terms of accuracy and speed, even for strongly imbalanced and chemically complex reactions with nontrivial atom-mapping. It provides the missing link between data-driven and rule-based approaches for numerous chemical reaction tasks.

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HISTORICAL RETE NETWORKS TO SUPPORT THE DEBUGGING OF FORWARD-CHAINING RULE-BASED PROGRAMS

International Journal of Artificial Intelligence Tools ◽

10.1142/s0218213093000059 ◽

1993 ◽

Vol 02 (01) ◽

pp. 47-70

Author(s):

SHARON M. TUTTLE ◽

CHRISTOPH F. EICK

Keyword(s):

Working Memory ◽

Data Driven ◽

Rule Based ◽

Historical Information ◽

Forward Chaining ◽

Changing Environments ◽

Time Performance ◽

Inference Network ◽

One Step ◽

Explanation System

Forward-chaining rule-based programs, being data-driven, can function in changing environments in which backward-chaining rule-based programs would have problems. But, degugging forward-chaining programs can be tedious; to debug a forward-chaining rule-based program, certain ‘historical’ information about the program run is needed. Programmers should be able to directly request such information, instead of having to rerun the program one step at a time or search a trace of run details. As a first step in designing an explanation system for answering such questions, this paper discusses how a forward-chaining program run’s ‘historical’ details can be stored in its Rete inference network, used to match rule conditions to working memory. This can be done without seriously affecting the network’s run-time performance. We call this generalization of the Rete network a historical Rete network. Various algorithms for maintaining this network are discussed, along with how it can be used during debugging, and a debugging tool, MIRO, that incorporates these techniques is also discussed.

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Rule-Based Data-Driven Analytics for Wide-Area Fault Detection Using Synchrophasor Data

IEEE Transactions on Industry Applications ◽

10.1109/tia.2016.2644621 ◽

2017 ◽

Vol 53 (3) ◽

pp. 1789-1798 ◽

Cited By ~ 19

Author(s):

Xiaodong Liang ◽

Scott A. Wallace ◽

Duc Nguyen

Keyword(s):

Fault Detection ◽

Data Driven ◽

Wide Area ◽

Rule Based

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Rule based classification of neurodegenerative diseases using data driven gait features

Health and Technology ◽

10.1007/s12553-018-0274-y ◽

2018 ◽

Vol 9 (4) ◽

pp. 547-560 ◽

Cited By ~ 1

Author(s):

Kartikay Gupta ◽

Aayushi Khajuria ◽

Niladri Chatterjee ◽

Pradeep Joshi ◽

Deepak Joshi

Keyword(s):

Neurodegenerative Diseases ◽

Data Driven ◽

Rule Based ◽

Gait Features ◽

Using Data

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Data driven automatic parameter inference for robotic assembly programs

Procedia CIRP ◽

10.1016/j.procir.2021.11.267 ◽

2021 ◽

Vol 104 ◽

pp. 1583-1588

Author(s):

Philipp Stephan ◽

Jessica Fisch ◽

Alperen Can ◽

Oliver Heimann ◽

Gregor Thiele ◽

...

Keyword(s):

Data Driven ◽

Robotic Assembly ◽

Parameter Inference

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Comparative analysis of molecular representations in prediction of cancer drug combination synergy and sensitivity

10.1101/2021.04.16.439299 ◽

2021 ◽

Author(s):

Bulat Zagidullin ◽

Ziyan Wang ◽

Yuanfang Guan ◽

Esa Pitkänen ◽

Jing Tang

Keyword(s):

Drug Combination ◽

High Throughput Screening ◽

Data Driven ◽

Cancer Drug ◽

Molecular Fingerprints ◽

Rule Based ◽

Drug Synergy ◽

The Past ◽

Optimal Representation ◽

Preclinical Drug Development

Application of machine and deep learning (ML/DL) methods in drug discovery and cancer research has gained a considerable amount of attention in the past years. As the field grows, it becomes crucial to systematically evaluate the performance of novel DL solutions in relation to established techniques. To this end we compare rule-based and data-driven molecular representations in prediction of drug combination sensitivity and drug synergy scores using standardized results of 14 high throughput screening studies, comprising 64,200 unique combinations of 4,153 molecules tested in 112 cancer cell lines. We evaluate the clustering performance of molecular fingerprints and quantify their similarity by adapting Centred Kernel Alignment metric. Our work demonstrates that in order to identify an optimal representation type it is necessary to supplement quantitative benchmark results with qualitative considerations, such as model interpretability and robustness, which may vary between and throughout preclinical drug development projects.

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Integrating Rule-Based Systems and Data Analytics Tools Using Open Standard PMML

Volume 1A: 35th Computers and Information in Engineering Conference ◽

10.1115/detc2015-46412 ◽

2015 ◽

Cited By ~ 2

Author(s):

Yunpeng Li ◽

Utpal Roy ◽

Y. Tina Lee ◽

Sudarsan Rachuri

Keyword(s):

Expert System ◽

Expert Systems ◽

Data Analytics ◽

Domain Knowledge ◽

Prediction Models ◽

Data Driven ◽

Sensor Data ◽

Formal Representation ◽

Rule Based ◽

Open Standard

Rule-based expert systems such as CLIPS (C Language Integrated Production System) are 1) based on inductive (if-then) rules to elicit domain knowledge and 2) designed to reason new knowledge based on existing knowledge and given inputs. Recently, data mining techniques have been advocated for discovering knowledge from massive historical or real-time sensor data. Combining top-down expert-driven rule models with bottom-up data-driven prediction models facilitates enrichment and improvement of the predefined knowledge in an expert system with data-driven insights. However, combining is possible only if there is a common and formal representation of these models so that they are capable of being exchanged, reused, and orchestrated among different authoring tools. This paper investigates the open standard PMML (Predictive Model Mockup Language) in integrating rule-based expert systems with data analytics tools, so that a decision maker would have access to powerful tools in dealing with both reasoning-intensive tasks and data-intensive tasks. We present a process planning use case in the manufacturing domain, which is originally implemented as a CLIPS-based expert system. Different paradigms in interpreting expert system facts and rules as PMML models (and vice versa), as well as challenges in representing and composing these models, have been explored. They will be discussed in detail.

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Pipe Fault Prediction for Water Transmission Mains

Water ◽

10.3390/w12102861 ◽

2020 ◽

Vol 12 (10) ◽

pp. 2861

Author(s):

Ariel Gorenstein ◽

Meir Kalech ◽

Daniela Fuchs Hanusch ◽

Sharon Hassid

Keyword(s):

Prediction Model ◽

Real World ◽

Fault Prediction ◽

Data Driven ◽

Segmentation Method ◽

Reliable Prediction ◽

Rule Based ◽

Maintenance Strategies ◽

Water Transmission ◽

Proactive Maintenance

Every network of supply waterlines experiences thousands of yearly bursts, breaks, leakages, and other failures. These failures waste a great amount of resources, as not only the waterlines need to be repaired, but also water is wasted and the distribution service is interrupted. For that reason, many water facilities employ proactive maintenance strategies in their networks, where they replace likely-to-fail pipes in advance to prevent the failures. In this paper, we aim to establish a reliable prediction model that can accurately predict faults in waterlines prior to their occurrence. We propose a specific segmentation method for long transmission mains, as well as three data-driven models and one rule-based prediction model. We evaluate a real world waterline network used in Israel, operated by Mekorot company, using three common metrics. The results show that the data-driven algorithms outperform the rule-based model by at least 5% in each of the metrics. Additionally, their prediction becomes more accurate as they are trained with more data, but enhancing these data with geographically related features does not improve the accuracy further.

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Rule-Based Data-Driven Thematic Mapping Technique

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.756-759.4476 ◽

2013 ◽

Vol 756-759 ◽

pp. 4476-4481

Author(s):

Hong Yun Zeng ◽

Lv Hua Wang ◽

Zhi Qiang Xie ◽

Wei Bo Zeng

Keyword(s):

Information Science ◽

Spatial Information ◽

Engineering Application ◽

Data Driven ◽

Mapping Technique ◽

River Channels ◽

Computer Mapping ◽

Rule Based ◽

Thematic Mapping ◽

Representation Technique

Mapping automation is not only a key objective of cartology, but also a study hotspot of geo-spatial information science at present. Here, we take thematic mapping of river channels (pipelines) passing through Yunnan in Kunming City for example to study and discuss, with regard to the effect required by complicated mapping encountering in the process of project implementation and the requirements on high quality of mapping data, the representation of rule-based data-driven computer mapping by the Representation technique of Arcgis9.3 to partly accomplish such mapping task that needs a lot of manual editing in traditional mapping mode, especially achieve the some kinds of mapping effect that cannot be achieved by traditional mapping without destructing GIS spatial information. The findings indicate that, the representation technique of rule-based driven computer mapping can reflect the advantages of both GIS spatial database establishment and mapping. This technique can make map rapidly according to different data requirements, and achieve the effect of traditional mapping. Due to less demand on manpower and financial capacity, such technique has a broad prospect of promotion and engineering application.

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