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Mobile Apps as Personal Assistant Agents: the JaCa-Android Framework for programming Agents-based applications on mobile devices

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Abstract

A relevant application domain for agent-based software is given by mobile and wearable applications. In this context, the impressive progress of technologies in the last decade makes it possible to explore the use of agent-oriented programming languages and frameworks based on cognitive architectures, such as the Belief–Desire–Intention (BDI) one. Accordingly, in this paper we provide a comprehensive description of the JaCa-Android approach, a framework based on the JaCaMo platform that allows for designing and programming smart mobile apps using cognitive agents based on the BDI architecture and the Agents & Artifacts environment conceptual model. In these years, the framework has been applied in real-world projects and application domains, and extended and evolved accordingly. The aim of the paper is to report our experience about designing and programming mobile apps as personal assistant agents, as well as to discuss in detail the architecture of the framework.

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Notes

  1. Available here: https://github.com/pslabunibo/jaca-android.

  2. https://github.com/micro-agents/micro-agents/wiki.

  3. https://developer.android.com/reference/android/app/Activity.

  4. https://developer.android.com/reference/android/app/Service.

  5. https://developer.android.com/guide/topics/manifest/manifest-intro.

  6. https://developer.android.com/studio/.

  7. The “M. Bufalini” Hospital in Cesena, Italy.

  8. The experimentation started in April 2017.

  9. https://www.vuzix.com/products/blade-smart-glasses.

  10. In June 2020, Google Android is the leading mobile operating system worldwide, with a 72,52% of global marker share (source: https://gs.statcounter.com/os-market-share/mobile/worldwide, retrieved in June 2020).

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Croatti, A., Ricci, A. Mobile Apps as Personal Assistant Agents: the JaCa-Android Framework for programming Agents-based applications on mobile devices. Auton Agent Multi-Agent Syst 34, 48 (2020). https://doi.org/10.1007/s10458-020-09474-7

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