Using AI planning and late binding for managing service workflows in intelligent environments

Bidot, Julien; Goumopoulos, Christos; Calemis, Ioannis

doi:10.1109/percom.2011.5767580

Cited by 18 publications

(6 citation statements)

References 16 publications

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“…It consists of a backbone of agents merging information from different sensors ( e.g ., cameras), preprocessing the data, and putting relevant information in a database. VAISTC4 is based on the ATRACO framework ( Goumopoulos et al, 2008 ; Seremeti, Goumopoulos & Kameas, 2009 ; Bidot, Goumopoulos & Calemis, 2011 ), i.e ., an SOA middleware for AmI systems. ATRACO includes an ontology model for the user and environment profiles ( Seremeti, Goumopoulos & Kameas, 2009 ) and allows service composition through AI planning and workflow ( Bidot, Goumopoulos & Calemis, 2011 ).…”

Section: Data Analysis: Systematic Mapmentioning

confidence: 99%

“…VAISTC4 is based on the ATRACO framework ( Goumopoulos et al, 2008 ; Seremeti, Goumopoulos & Kameas, 2009 ; Bidot, Goumopoulos & Calemis, 2011 ), i.e ., an SOA middleware for AmI systems. ATRACO includes an ontology model for the user and environment profiles ( Seremeti, Goumopoulos & Kameas, 2009 ) and allows service composition through AI planning and workflow ( Bidot, Goumopoulos & Calemis, 2011 ). In addition, VAISTC4 provides event channels for registering sensor and effector agents.…”

Section: Data Analysis: Systematic Mapmentioning

confidence: 99%

See 1 more Smart Citation

Ambient intelligence governance review: from service-oriented to self-service

Ponce¹,

Abdulrazak²

2022

PeerJ Computer Science

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The current generation of connected devices and the Internet of Things augment people’s capabilities through ambient intelligence. Ambient Intelligence (AmI) support systems contain applications consuming available services in the environment to serve users. A well-known design of these applications follows a service architecture style and implement artificial intelligence mechanisms to maintain an awareness of the context: The service architecture style enables the distribution of capabilities and facilitates interoperability. Intelligence and context-awareness provide an adaptation of the environment to improve the interaction. Smart objects in distributed deployments and the increasing machine awareness of devices and people context also lead us to architectures, including self-governed policies providing self-service. We have systematically reviewed and analyzed ambient system governance considering service-oriented architecture (SOA) as a reference model. We applied a systematic mapping process obtaining 198 papers for screening (out of 712 obtained after conducting searches in research databases). We then reviewed and categorized 68 papers related to 48 research projects selected by fulfilling ambient intelligence and SOA principles and concepts. This paper presents the result of our analysis, including the existing governance designs, the distribution of adopted characteristics, and the trend to incorporate service in the context-aware process. We also discuss the identified challenges and analyze research directions.

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Section: Data Analysis: Systematic Mapmentioning

confidence: 99%

Section: Data Analysis: Systematic Mapmentioning

confidence: 99%

Ambient intelligence governance review: from service-oriented to self-service

Ponce¹,

Abdulrazak²

2022

PeerJ Computer Science

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“…While AI Planning has been successfully applied in several domains such as robotics and games, it has not been employed in context-aware systems or ubiquitous systems. Most of the existing work in this domain has focused on web services composition using AI Planning [3,8,13,15,16].…”

Section: Related Workmentioning

confidence: 99%

Enabling Proactivity in Context-aware Middleware Systems by means of a Planning Framework based on HTN Planning

Bhargava¹,

Agrawala²

2015

Proceedings of the 12th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services

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Today's context-aware systems tend to be reactive or 'pull' based-the user requests or queries for some information and the system responds with the requested information. However, none of the systems anticipate the user's intent and behavior, or take into account his current events and activities to pro-actively 'push' relevant information to the user. On the other hand, Proactive context-aware systems can predict and anticipate user intent and behavior, and act proactively on the users' behalf without explicit requests from them. Two fundamental capabilities of such systems are: prediction and autonomy. In this paper, we address the second capability required by a context-aware system to act proactively i.e. acting autonomously without an explicit user request. To address it, we present a new paradigm for enabling proactivity in context-aware middleware systems by means of a Planning Framework based on HTN planning. We present the design of a Planning Framework within the infrastructure of our intelligent context-aware middleware called Rover II. We also implement this framework and evaluate its utility with several use cases. We also highlight the benefits of using such a framework in dynamic ubiquitous systems.

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“…De même, le projet européen ATRACO (Adaptive and Trusted Ambient Ecologies) [1] utilise un planificateur HTN pour permettre de décrire une hiérarchie de tâches pour différencier les tâches pouventêtre exécutées en parrallèle ou en séquence. En effet, ce planificateur utilise aussi des concepts de Partial-Order Planning pour produire un plan d'actions concrètes partiellement ordonnées.…”

Section: Génie Logicielunclassified

“…Les travaux portant sur la composition dynamique produisent un plan sous la forme d'une séquence d'actions. A notre connaissance, seul le plannificateur du projet ATRACO [1] produit un plan partiellement ordonné. Cependant, ce plan ne comportent pas les méthodes (actions arbtraites) fournies en entrée.…”

Section: Positionnementunclassified