An Agent-based Architecture for AI-Enhanced Automated Testing for XR Systems, a Short Paper
| Autor: | Samira Shirzadehhajimahmood, Saba Gholizadeh Ansari, Rui Prada, Pedro M. Fernandes, I. S. W. B. Prasetya |
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| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
agent based testing business.industry Computer science 05 social sciences Short paper 050301 education AI for testing games 020207 software engineering 02 engineering and technology automated testing XR systems Software Engineering (cs.SE) Computer Science - Software Engineering AI for automated testing 0202 electrical engineering electronic engineering information engineering Natural (music) Architecture Software engineering business 0503 education |
| Zdroj: | ICST Workshops |
| DOI: | 10.48550/arxiv.2104.06132 |
| Popis: | This short paper presents an architectural overview of an agent-based framework called iv4XR for automated testing that is currently under development by an H2020 project with the same name. The framework's intended main use case of is testing the family of Extended Reality (XR) based systems (e.g. 3D games, VR sytems, AR systems), though the approach can indeed be adapted to target other types of interactive systems. The framework is unique in that it is an agent-based system. Agents are inherently reactive, and therefore are arguably a natural match to deal with interactive systems. Moreover, it is also a natural vessel for mounting and combining different AI capabilities, e.g. reasoning, navigation, and learning. |
| Databáze: | OpenAIRE |
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