Sociomateriality Implications of Multi-Agent Supported Collaborative Work Systems

Abstract

Recent technological advancements significantly redefined the context in which organizations acquire, process, and share information. The transformations that emerged across the organizational and institutional landscapes have led to the emergence of new forms of organizational design and business models. Within this context, the new business patterns, platforms and architectures have been developed to enable for the maximization of benefits from data through the adoption of collaborative work practices. The main focus of such practices is oriented towards the improvement of responsiveness, building of alliances and enhancing organizational reach. The use of global networks and web based systems for the implementation of collaborative work has been accompanied with a wide range of computer supported collaborative systems. This paper examines the context of collaboration, collaborative work and the development of agent-supported collaborative work system and examines the implications of the ontological positions of sociomateriality on agent-supported collaborative work domains in terms of multi-agent architecture and agent-oriented evaluation.

FuzzyOrganization of Self-Adaptive Agents Based On Software Components

“…The analysis of approaches in which Components and agents are combined allows revealing that all existing approaches have failed to attain the limit of exploiting the full power of both paradigms. The organization of MAS and the concept of role are not exploited despite their important contributions mentioned in the introduction and the existence of interesting results of researchs in the domain of MAS organization such as Kolp, Faulkner and Wautelet, (2008); Afsaneh, Kamran and Naser, (2011);Tagelsir, (2012) and Moamin et al (2013). The MAS community has a body of knowledge on organization.…”

Managing Stakeholder Concerns in Large-Scale Multi-Agent Information Systems

“…Before you Emergency response is defined as all the activities for identifying, detecting, planning, staff training, analyzing vulnerability and responding to unanticipated events that may result in injury and/or loss of human lives and damage and/or destruction of critical infrastructure elements (Tagelsir, 2012a). The phases of emergency response management (i.e., planning, mitigation, preparedness, response, and recovery) aim at ensuring the availability and efficient use of resources, unity of command, and coordination of response and organized handling of patients.…”

“…The phases of emergency response management (i.e., planning, mitigation, preparedness, response, and recovery) aim at ensuring the availability and efficient use of resources, unity of command, and coordination of response and organized handling of patients. While the emphasis on "resource allocation" and 'service coverage" (Tagelsir, 2012b) assumes high priority in emergency response operations, the focus on the acquisition, sharing of emergency response management information is the key success factor. The provision of emergency services in short response times (in urban areas), for example, requires real time and updated information about the different aspects of calls and the emergency management systems such as the type and location of the request, availability and location resources (such as ambulances and emergency staff), traffic conditions and system-related considerations such as congestion (Wikipedia).…”

“…Therefore, they have been used in sectors such as healthcare, education, banking, insurance, airlines and environment, among others. They assist in task handling, users' training, event monitoring, information search, matchmaking and filtering (Tagelsir, 2012a). Despite the existence of many studies that focus on identifying agent qualities, the list of most widely cited qualities include autonomy, social ability, reactivity and pro-activeness, learning, mobility, benevolence, rationality, independence, cooperation, reasoning, intelligence and adaptivity, among others.…”

A knowledge-based framework for a mobile OLAP system

“…Such a knowledge (contextual profile) take into account both the behaviours shown by a user while querying OLAP server (profile) and the devices used during these navigations (context).The proposed framework (called MASCARS) is based on multi-agents system. In complex situations, multi agent configurations facilitate collaboration, interaction and the production of user-created content (Gasmelseid, 2012).…”