A Generic Agent Organisation Framework For Autonomic Systems

Autonomic computing is being advocated as a tool for managing large, complex computing systems. Specifically, self-organisation provides a suitable approach for developing such autonomic systems by incorporating self-management and adaptation properties into large-scale distributed systems. To aid in this development, this paper details a generic problem-solving agent organisation framework that can act as a modelling and simulation platform for autonomic systems. Our framework describes a set of service-providing agents accomplishing tasks through social interactions in dynamically changing organisations. We particularly focus on the organisational structure as it can be used as the basis for the design, development and evaluation of generic algorithms for self-organisation and other approaches towards autonomic systems

Message from the IEEE/ACM ASONAM 2012 general chairs

[No abstract available

Exploring leadership in multi-sectoral partnerships

This article explores some critical aspects of leadership in the context of multi-sectoral partnerships. It focuses on leadership in practice and asks the question, `How do managers experience and perceive leadership in such partnerships?' The study contributes to the debate on whether leadership in a multi-sectoral partnership context differs from that within a single organization. It is based on the accounts of practising managers working in complex partnerships. The article highlights a number of leadership challenges faced by those working in multi-sectoral partnerships. Partnership practitioners were clear that leadership in partnerships was more complex than in single organizations. However, it was more difficult for them to agree a consensus on the essential nature of leadership in partnership. We suggest that a first-, second- and third-person approach might be a way of better interpreting leadership in the context of partnerships

The journal of Islamic law : adressing issues of law, religion and culture

Knowledge intensive business services such as IT Services, rely on the expertise of the knowledge workers for performing the activities involved in the delivery of services. The activities performed could range from performing simple, repetitive tasks to resolving more complex situations. The expertise of the task force can also vary from novices who cost less to advanced skill workers and experts who are more expensive. Staffing of service systems relies largely on the assumptions underlying the operational productivity of the workers. Research independently points to the impact of factors such as complexity of work and expertise of the worker on worker productivity. In this paper, we examine the impact of complexity of work, priority or importance of work and expertise of the worker together, on the operational productivity of the worker. For our empirical analysis, we use the data from real-life engagement in the IT service management domain. Our finding, on the basis of the data indicates, not surprisingly, that experts are more suitable for complex or high priority work with strict service levels. In the same setting, when experts are given simpler tasks of lower priority, they tend to not perform better than their less experienced counterparts. The operational productivity measure of experts and novices is further used as an input to a discrete event simulation based optimization framework that model real-life service system to arrive at an optimal staffing. Our work demonstrates that data driven techniques, similar to the one presented here is useful for making more accurate staffing decisions by understanding worker efficiency derived from the analysis of operational data.</p

Representation of Developments in Labour Market Research

knowledge graphs, knowledge representation,