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Agent-Oriented Visual Programming for the Web of Things

Type
conference paper
Date Issued
2022
Author(s)
Burattini, Samuele
Ricci, Alessandro
Mayer, Simon  orcid-logo
Vachtsevanou, Danai  
Lemee, Jérémy  
Ciortea, Andrei  
Croatti, Angelo
Abstract
In this paper we introduce and discuss an approach for multiagent-oriented visual programming. This aims at enabling individuals without programming experience but with knowledge in specific target domains to design and (re)configure autonomous software. We argue that, compared to procedural programming, it should be simpler for users to create programs when agent abstractions are employed. The underlying rationale is that these abstractions, and specifically the belief-desire-intention architecture that is aligned with human practical reasoning, match more closely with people’s everyday experience in interacting with other agents and artifacts in the real world. On top of this, we designed and implemented a visual programming system for agents that hides the technicalities of agent-oriented programming using a block-based visual development environment that is built on the JaCaMo platform. To further validate the proposed solution, we integrate the Web of Things (WoT) to let users create autonomous behaviour on top of physical mashups of devices, following the trends in industrial end-user programming. Finally, we report on a pilot user study where we verified that novice users are indeed able to make use of this development environment to create multi-agent systems to solve simple automation tasks.
Language
English
HSG Classification
contribution to scientific community
Event Title
Engineering Multi-Agent Systems 2022
Event Location
Auckland, New Zealand
Event Date
9-10 May 2022
URL
https://www.alexandria.unisg.ch/handle/20.500.14171/109205
Subject(s)

computer science

Division(s)

ICS - Institute of Co...

Eprints ID
266966
File(s)
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open.access

Name

Agent-Oriented-Visual-Programming-for-the-Web-of-Things-EMAS-2022.pdf

Size

296.93 KB

Format

Adobe PDF

Checksum (MD5)

b96a2f44e851dee9783426a6c49be24e

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