Assistant Agents to Advice Users in Hybrid Structured 3D Virtual Environments
Hybrid structured 3D Virtual Environments model serious activities in immersive 3D spaces, where participants are human and SW agents, and their interactions are regulated by an OCMAS (Organization Centered Multi-Agent System). In this context, both OCMAS social model and the tasks that users need t...
| Autores: | , , , |
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| Tipo de documento: | artigo |
| Estado: | Versión aceptada para publicación |
| Data de publicação: | 2014 |
| País: | España |
| Recursos: | Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
| Repositório: | Recercat. Dipósit de la Recerca de Catalunya |
| OAI Identifier: | oai:recercat.cat:2445/124913 |
| Acesso em linha: | https://hdl.handle.net/2445/124913 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Visualització tridimensional Intel·ligència artificial Interacció persona-ordinador Three-dimensional display systems Artificial intelligence Human-computer interaction |
| Resumo: | Hybrid structured 3D Virtual Environments model serious activities in immersive 3D spaces, where participants are human and SW agents, and their interactions are regulated by an OCMAS (Organization Centered Multi-Agent System). In this context, both OCMAS social model and the tasks that users need to accomplish can be rather complex, and thus, users may benefit from having an assistance service. Hence, we propose Personal Assistant agents (PA) which, based on knowledge about the OCMAS specification and current system state, provide the user with an advice (a plan) to achieve her goal. Additionally, we implement this service with PLAN-EA, an Extension of the $A^{\ast}$ algorithm that generates plans for a user whose actions may depend on other users' actions. Thus, PAs provide plans that do not only include assisted user actions but other users' ones. We illustrate our approach by means of v-mWater -an online water market- and make a comparative analysis, with and without assistance, where efficiency -in terms of number of user actions- shows an improvement (7 vs 10.8), efficacy -percentage of completed tasks- also improves (93% vs 77%), and assistance's overall satisfaction is positive. |
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