Extending Datalog/ASP with constraints (CASP) enhances its expressiveness and performance but it is not straightforward as the grounding phase removes variables and the links among them. We incorporate constraints int...
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FlowUML is a logic-based system to validate information flow policies at the requirements specification phase of UML based designs. It uses Horn clauses to specify information flow polices that can be checked against ...
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ISBN:
(纸本)9728865252
FlowUML is a logic-based system to validate information flow policies at the requirements specification phase of UML based designs. It uses Horn clauses to specify information flow polices that can be checked against flow information extracted from UML sequence diagrams. FlowUML policies can be written at a coarse grain level of caller-callee relationships or at a finer level involving passed attributes.
This book constitutes the thoroughly refereed post-conference proceedings of the Thirdinternationalworkshop on Graph Structures for knowledgerepresentation and Reasoning, GKR 2013, held in Beijing, China, in August...
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ISBN:
(数字)9783319045344
ISBN:
(纸本)9783319045337
This book constitutes the thoroughly refereed post-conference proceedings of the Thirdinternationalworkshop on Graph Structures for knowledgerepresentation and Reasoning, GKR 2013, held in Beijing, China, in August 2013, associated with IJCAI 2013, the 23rdinternational Joint Conference on Artificial Intelligence. The 12 revised full papers presented were carefully reviewed and selected for inclusion in the book. The papers feature current research involved in the development and application of graph-based knowledgerepresentation formalisms and reasoning techniques. They address the following topics: representations of constraint satisfaction problems; formal concept analysis; conceptual graphs; and argumentation frameworks.
Bug fixing is one of the most time-consuming and resource-intensive tasks in the software development life cycle. Automated Program Repair (APR) might be able to help in this process, but it still has to overcome many...
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ISBN:
(纸本)9798350302141
Bug fixing is one of the most time-consuming and resource-intensive tasks in the software development life cycle. Automated Program Repair (APR) might be able to help in this process, but it still has to overcome many obstacles. Deep learning models have shown promise for automated program repair in recent years, but their effectiveness can depend on the representation of the source code used as input. In this paper, we conduct an experimental study to compare the performance of deep learning models on two popular programming languages, Java and JavaScript, using three different code representations: raw text, command sequences, and abstract syntax trees (ASTs). We also experiment with varying models, including T5, CodeT5, (for solving sequence-to-sequence tasks) RoBERTa, and GPTNeo (to encode/decode AST graph information). We evaluate the models on a set of real-world defects from open-source projects and compare the performance, and the repair patches generated by the models. Our results show that training on command sequence representation outperforms most other configurations. We achieve a best of 19.88% accuracy on the java-small dataset, and 11.87% on java-medium, using text representation. Using command sequence representation, we achieve 30.64% on javasmall and 18.53% on the medium dataset. However, when representing the source with ast+text information, our models significantly underperform compared to other representations, achieving results below one percent. Our findings contribute to a better understanding of the strengths and limitations of deep learning models for automated program repair and provide practical guidance for their use in practice.
Humans are smart in revising their knowledge and concepts based on observations when they find conflicts. This ability to repair representations is also important for AI agents so that they can represent their environ...
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This book constitutes the thoroughly refereed post-conference proceedings of the Second internationalworkshop on Graph Structures for knowledgerepresentation and Reasoning, GKR 2011, held in Barcelona, Spain, in Jul...
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ISBN:
(数字)9783642294495
ISBN:
(纸本)9783642294488
This book constitutes the thoroughly refereed post-conference proceedings of the Second internationalworkshop on Graph Structures for knowledgerepresentation and Reasoning, GKR 2011, held in Barcelona, Spain, in July 2011 as satellite event of IJCAI 2011, the 22nd international Joint Conference on Artificial Intelligence. The 7 revised full papers presented together with 1 invited paper were carefully reviewed and selected from 12 submissions. The papers feature current research involved in the development and application of graph-based knowledgerepresentation formalisms and reasoning techniques and investigate further developments of knowledgerepresentation and reasoning graph based techniques. Topics addressed are such as: bayesian networks, semantic networks, conceptual graphs, formal concept analysis, cp-nets, gai-nets, euler diagrams, existential graphs all of which have been successfully used in a number of applications (semantic Web, recommender systems, bioinformatics etc.).
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