Designing toolkits for teaching programming concepts to children using robots has received growing attention in recent years. However, teaching preschool children computational concepts, such as non-determinism and ev...
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This paper presents a framework for the design of the planning and controlling components in a flexible manufacturing system. A hierarchical planning structure is presented which consists of six layers: the layer of t...
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ISBN:
(纸本)0444818146
This paper presents a framework for the design of the planning and controlling components in a flexible manufacturing system. A hierarchical planning structure is presented which consists of six layers: the layer of the production planning and control system, the layer of the shop floor control system, the task coordination layer, the task planning layer, the task execution layer and the machine control layer. The last four layers mentioned are present separately for each manufacturing device which is able to execute tasks autonomously. For the abstract system which consists of the task coordination component, the task planner, the task execution component, the machine control component, and the flexible manufacturing device itself the term auto;rzomous system is introduced. Special emphasis is placed on solving the scheduling problem in the shop floor control system and task planning in the task planning layer of the autonomous systems. The implementation of the planning and controlling components of the hierarchical planning structure was done for the example of the model of a manufacturing plant [4, 7] with the help of the rule-based multi-agent system MAGSY [8, 6], which is briefly described at the end of the paper.
[Auto Generated] page ACKNOWLEDGMENTS iv LIST OF TABLES vii LIST OF FIGURES viii ABSTRACT x LINTRODUCTION: THE TRIGGERMAN PROJECT 1 1 . 1 rules and Active Databases Overview: 1 L L 1 Integrity Constraint Checking and ...
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[Auto Generated] page ACKNOWLEDGMENTS iv LIST OF TABLES vii LIST OF FIGURES viii ABSTRACT x LINTRODUCTION: THE TRIGGERMAN PROJECT 1 1 . 1 rules and Active Databases Overview: 1 L L 1 Integrity Constraint Checking and Repair: 1 1.1.2 Time and Temporal Issues: 2 1.1.3 Materialized View Maintenance: 3 1.1.4 Advantages and Shortcomings of Existing rule Systems: 4 1.2 Extensibility and Extensible Databases Background: 6 1.3 TriggerMan: 9 1.3.1 The TriggerMan Environment: 11 1.3.2 The TriggerMan Physi
Spreadsheets are widely used in science, engineering, business, and other activities. Overall, they conceal a large volume of data in a form intended to be interpreted by humans. We present a novel software platform f...
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Spreadsheets are widely used in science, engineering, business, and other activities. Overall, they conceal a large volume of data in a form intended to be interpreted by humans. We present a novel software platform facilitated for liberating such data. It provides rule-based spreadsheet data extraction and transformation to a structured form. Its core consists of a flexible table object model and a domainspecific rule language for table analysis. They serve to represent knowledge of table layout and content features, as well as their interpretation depending on transformation goals. This enables processing arbitrary tables originating from various domains. Our empirical results demonstrate that one ruleset can be applied to process arbitrary tables having the same features of layout, style, or content. The paper also describes two applications using the software platform to develop programs for rule-based converting data from arbitrary spreadsheet tables. (C) 2019 The Authors. Published by Elsevier B.V.
The information extraction from unstructured text segments is a complex task. Although manual information extraction often produces the best results, it is harder to manage biomedical data extraction manually because ...
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The information extraction from unstructured text segments is a complex task. Although manual information extraction often produces the best results, it is harder to manage biomedical data extraction manually because of the exponential increase in data size. Thus, there is a need for automatic tools and techniques for information extraction in biomedical text mining. Relation extraction is a significant area under biomedical information extraction that has gained much importance in the last two decades. A lot of work has been done on biomedical relation extraction focusing on rule-based and machine learning techniques. In the last decade, the focus has changed to hybrid approaches showing better results. This research presents a hybrid feature set for classification of relations between biomedical entities. The main contribution of this research is done in the semantic feature set where verb phrases are ranked using Unified Medical Language System (UMLS) and a ranking algorithm. Support Vector Machine and Naive Bayes, the two effective machine learning techniques, are used to classify these relations. Our approach has been validated on the standard biomedical text corpus obtained from MEDLINE 2001. Conclusively, it can be articulated that our framework outperforms all state-of-the-art approaches used for relation extraction on the same corpus.
An automatic method is presented for detecting myocardial ischemia, which can be considered as the early symptom of acute coronary events. Myocardial ischemia commonly manifests as ST- and T-wave changes on ECG signal...
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An automatic method is presented for detecting myocardial ischemia, which can be considered as the early symptom of acute coronary events. Myocardial ischemia commonly manifests as ST- and T-wave changes on ECG signals. The methods in this study are proposed to detect abnormal ECG beats using knowledge-based features and classification methods. A novel classification method, sparse representation-based classification (SRC), is involved to improve the performance of the existing algorithms. A comparison was made between two classification methods, SRC and support-vector machine (SVM), using rule-based vectors as input feature space. The two methods are proposed with quantitative evaluation to validate their performances. The results of SRC method encompassed with rule-based features demonstrate higher sensitivity than that of SVM. However, the specificity and precision are a trade-off. Moreover, SRC method is less dependent on the selection of rule-based features and can achieve high performance using fewer features. The overall performances of the two methods proposed in this study are better than the previous methods.
We describe our efforts to use rule-based programming to produce a model of Jumbo, a run-time program generation (RTPG) system for Java. Jumbo incorporates RTPG following the simple principle that the regular compiler...
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We describe our efforts to use rule-based programming to produce a model of Jumbo, a run-time program generation (RTPG) system for Java. Jumbo incorporates RTPG following the simple principle that the regular compiler - or, rather, its back-end - can be used both for ordinary, static compilation and for run-time compilation. This tends to produce a run-time compiler that is inefficient but potentially subject to improvement by partial evaluation. However, the complexity of the language and compiler have made it difficult for us to achieve actual optimization. The model, written in Maude, preserves all the essential ingredients of Jumbo, but operates on a simplified language, called Mumbo. The simplification in the language together with Maude's support for code rewriting has allowed us to make rapid progress. We discuss the model in detail, the kinds of optimizations we have obtained, and the impact on the Jumbo project.
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 tediou...
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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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