Supporting adaptiveness of cyber-physical processes through action-based formalisms

Cyber Physical Processes (CPPs) refer to a new generation of business processes enacted in many application environments (e.g., emergency management, smart manufacturing, etc.), in which the presence of Internet-of-Things devices and embedded ICT systems (e.g., smartphones, sensors, actuators) strongly influences the coordination of the real-world entities (e.g., humans, robots, etc.) inhabitating such environments. A Process Management System (PMS) employed for executing CPPs is required to automatically adapt its running processes to anomalous situations and exogenous events by minimising any human intervention. In this paper, we tackle this issue by introducing an approach and an adaptive Cognitive PMS, called SmartPM, which combines process execution monitoring, unanticipated exception detection and automated resolution strategies leveraging on three well-established action-based formalisms developed for reasoning about actions in Artificial Intelligence (AI), including the situation calculus, IndiGolog and automated planning. Interestingly, the use of SmartPM does not require any expertise of the internal working of the AI tools involved in the system

Adaptive Process Management in Cyber-Physical Domains

The increasing application of process-oriented approaches in new challenging cyber-physical domains beyond business computing (e.g., personalized healthcare, emergency management, factories of the future, home automation, etc.) has led to reconsider the level of flexibility and support required to manage complex processes in such domains. A cyber-physical domain is characterized by the presence of a cyber-physical system coordinating heterogeneous ICT components (PCs, smartphones, sensors, actuators) and involving real world entities (humans, machines, agents, robots, etc.) that perform complex tasks in the “physical” real world to achieve a common goal. The physical world, however, is not entirely predictable, and processes enacted in cyber-physical domains must be robust to unexpected conditions and adaptable to unanticipated exceptions. This demands a more flexible approach in process design and enactment, recognizing that in real-world environments it is not adequate to assume that all possible recovery activities can be predefined for dealing with the exceptions that can ensue. In this chapter, we tackle the above issue and we propose a general approach, a concrete framework and a process management system implementation, called SmartPM, for automatically adapting processes enacted in cyber-physical domains in case of unanticipated exceptions and exogenous events. The adaptation mechanism provided by SmartPM is based on declarative task specifications, execution monitoring for detecting failures and context changes at run-time, and automated planning techniques to self-repair the running process, without requiring to predefine any specific adaptation policy or exception handler at design-time

Designing a goal-oriented smart-home environment

The final publication is available at Springer via http://dx.doi.org/10.1007/s10796-016-9670-x[EN] Nowadays, systems are growing in power and in access to more resources and services. This situation makes it necessary to provide user-centered systems that act as intelligent assistants. These systems should be able to interact in a natural way with human users and the environment and also be able to take into account user goals and environment information and changes. In this paper, we present an architecture for the design and development of a goal-oriented, self-adaptive, smart-home environment. With this architecture, users are able to interact with the system by expressing their goals which are translated into a set of agent actions in a way that is transparent to the user. This is especially appropriate for environments where ambient intelligence and automatic control are integrated for the user’s welfare. In order to validate this proposal, we designed a prototype based on the proposed architecture for smart-home scenarios. We also performed a set of experiments that shows how the proposed architecture for human-agent interaction increases the number and quality of user goals achieved.This work is partially supported by the Spanish Government through the MINECO/FEDER project TIN2015-65515-C4-1-R.Palanca Cámara, J.; Del Val Noguera, E.; García-Fornes, A.; Billhard, H.; Corchado, JM.; Julian Inglada, VJ. (2016). Designing a goal-oriented smart-home environment. Information Systems Frontiers. 1-18. https://doi.org/10.1007/s10796-016-9670-xS118Alam, M. R., Reaz, M. B. I., & Ali, M. A. M. (2012). A review of smart homes: Past, present, and future. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 42(6), 1190–1203.Andrushevich, A., Staub, M., Kistler, R., & Klapproth, A. (2010). Towards semantic buildings: Goal-driven approach for building automation service allocation and control. In 2010 IEEE conference on emerging technologies and factory automation (ETFA) (pp. 1–6) IEEE.Ayala, I., Amor, M., & Fuentes, L. (2013). Self-configuring agents for ambient assisted living applications. Personal and Ubiquitous Computing, 17(6), 1159–1169.Cetina, C., Giner, P., Fons, J., & Pelechano, V. (2009). Autonomic computing through reuse of variability models at runtime: The case of smart homes. Computer, 42(10), 37–43.Cook, D. J. (2009). Multi-agent smart environments. Journal of Ambient Intelligence and Smart Environments, 1(1), 51–55.Dalpiaz, F., Giorgini, P., & Mylopoulos, J. (2009). An architecture for requirements-driven self-reconfiguration. In Advanced information systems engineering (pp. pp 246–260). Springer.De Silva, L. C., Morikawa, C., & Petra, I. M. (2012). State of the art of smart homes. 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In 13th Workshop on objects and Agents (WOA 2012) (Vol. 892, pp. 49–55).Martin, D., Burstein, M., Hobbs, J., Lassila, O., McDermott, D., McIlraith, S., Narayanan, S., Paolucci, M., Parsia, B., Payne, T., & et al (2004). OWL-S: Semantic markup for web services. W3C Member Submission, 22, 2007–2004.Matthews, R. B., Gilbert, N. G., Roach, A., Polhill, J. G, & Gotts, N. M. (2007). Agent-based land-use models: a review of applications. Landscape Ecology, 22(10), 1447–1459.Molina, J. M., Corchado, J. M., & Bajo, J. (2008). Ubiquitous computing for mobile environments. In Issues in multi-agent systems (pp 33–57). Birkhäuser, Basel.Palanca, J., Navarro, M., Julian, V., & García-Fornes, A. (2012). Distributed goal-oriented computing. Journal of Systems and Software, 85(7), 1540–1557. doi: 10.1016/j.jss.2012.01.045 .Rao, A., & Georgeff, M. (1995). BDI agents: From theory to practice. In Proceedings of the first international conference on multi-agent systems (ICMAS95) (pp. 312–319).Reddy, Y. 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Collaboration in the Semantic Grid: a Basis for e-Learning

The CoAKTinG project aims to advance the state of the art in collaborative mediated spaces for the Semantic Grid. This paper presents an overview of the hypertext and knowledge based tools which have been deployed to augment existing collaborative environments, and the ontology which is used to exchange structure, promote enhanced process tracking, and aid navigation of resources before, after, and while a collaboration occurs. While the primary focus of the project has been supporting e-Science, this paper also explores the similarities and application of CoAKTinG technologies as part of a human-centred design approach to e-Learning

Advances in Production Management Systems: Issues, Trends, and Vision Towards 2030

Since its inception in 1978, the IFIP Working Group (WG) 5.7 on Advances in Production Management Systems (APMS) has played an active role in the fields of production and production management. The Working Group has focused on the conception, development, strategies, frameworks, architectures, processes, methods, and tools needed for the advancement of both fields. The associated standards created by the IFIP WG5.7 have always been impacted by the latest developments of scientific rigour, academic research, and industrial practices. The most recent of those developments involves the Fourth Industrial Revolution, which is having remarkable (r)evolutionary and disruptive changes in both the fields and the standards. These changes are triggered by the fusion of advanced operational and informational technologies, innovative operating and business models, as well as social and environmental pressures for more sustainable production systems. This chapter reviews past, current, and future issues and trends to establish a coherent vision and research agenda for the IFIP WG5.7 and its international community. The chapter covers a wide range of production aspects and resources required to design, engineer, and manage the next generation of sustainable and smart production systems.acceptedVersio