The advancement of technology has brought about ease of life with the help of technology automation which can be applied virtually in all aspects of living. With the emergence of internet of things, prototyping real s...
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The upcoming digital transformation of the modern industry will principally build upon the softwaresystems. Certainly, any software system should commit to being fully reliable and free from any deficiency such as so...
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In this abstract, we briefly discuss the relationship between Agent-Oriented softwareengineering, with special attention to the Belief-Desire-Intention paradigm, and the world of autonomic computing. Although these t...
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ISBN:
(数字)9798350389760
ISBN:
(纸本)9798350389777
In this abstract, we briefly discuss the relationship between Agent-Oriented softwareengineering, with special attention to the Belief-Desire-Intention paradigm, and the world of autonomic computing. Although these two worlds seem on paper to be complementary, in the sense that BDI sounds like a highly effective paradigm to implement autonomic systems, in practice, they are not widespread as technological choices in the community. We argue that the reason is mostly related to the current state of the tooling rather than the paradigm, and we propose a novel approach to build tools for BDI agent-oriented programming that could ease adoption in the autonomic and self-organising systems community.
In this article, the issues of developing a technology for creating digital prototypes of green energy facilities and their use in the educational process for training of specialists in the energy industry during lect...
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Recommender systems, which make precise recommendations through historical interaction data of users and items, have been widely used in real life. But at the same time, because of the open nature of the systems, they...
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Automatic text summarization aims at producing a shorter version of a document (or a document set). Extractive summarizers compile summaries by extracting a subset of sentences from a given text, while abstractive sum...
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Cross-domain recommendation (CDR) offers a promising solution to the data sparsity problem by enabling knowledge transfer between source and target domains. However, many recent CDR models overlook crucial issues such...
Distributed computing refers to the solution to a problem using distributed systems of autonomous and heterogeneous computers that are important for communication, networking, and workstation functioning. Distributed ...
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Artificial Intelligence, the name itself depicts the meaning that providing the knowledge of human to the machine artificially. AI is not a sense or feeling but the software or a model evolved to do complex tasks like...
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[Context and Motivation] Recent studies have highlighted transparency and explainability as important quality requirements of AI systems. However, there are still relatively few case studies that describe the current ...
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ISBN:
(纸本)9783030984649;9783030984632
[Context and Motivation] Recent studies have highlighted transparency and explainability as important quality requirements of AI systems. However, there are still relatively few case studies that describe the current state of defining these quality requirements in practice. [Question] The goal of our study was to explore what ethical guidelines organizations have defined for the development of transparent and explainable AI systems. We analyzed the ethical guidelines in 16 organizations representing different industries and public sector. [Results] In the ethical guidelines, the importance of transparency was highlighted by almost all of the organizations, and explainability was considered as an integral part of transparency. Building trust in AI systems was one of the key reasons for developing transparency and explainability, and customers and users were raised as the main target groups of the explanations. The organizations also mentioned developers, partners, and stakeholders as important groups needing explanations. The ethical guidelines contained the following aspects of the AI system that should be explained: the purpose, role of AI, inputs, behavior, data utilized, outputs, and limitations. The guidelines also pointed out that transparency and explainability relate to several other quality requirements, such as trustworthiness, understandability, traceability, privacy, auditability, and fairness. [Contribution] For researchers, this paper provides insights into what organizations consider important in the transparency and, in particular, explainability of AI systems. For practitioners, this study suggests a structured way to define explainability requirements of AI systems.
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