G-networks with multiple classes of negative and positive customers
Theoretical Computer Science
A methodology and modelling technique for systems of BDI agents
MAAMAW '96 Proceedings of the 7th European workshop on Modelling autonomous agents in a multi-agent world : agents breaking away: agents breaking away
Dynamic agent population in agent-based distance vector routing
Second international workshop on Intelligent systems design and application
Requirements engineering for large-scale multi-agent systems
Software engineering for large-scale multi-agent systems
A multi-level graded-precision model of large scale power systems for fast parallel computation
Mathematical and Computer Modelling: An International Journal
Multiclass G-Networks of Processor Sharing Queues with Resets
ASMTA '08 Proceedings of the 15th international conference on Analytical and Stochastic Modeling Techniques and Applications
An initiative for a classified bibliography on G-networks
Performance Evaluation
Bibliography on G-networks, negative customers and applications
Mathematical and Computer Modelling: An International Journal
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As a result of the structure and content transformation of an evolving society, many large scale autonomous systems emerged in diverse areas such as biology, ecology or finance. Inspired by the desire to better understand and make the best out of these systems, we propose an approach which builds stochastic mathematical models, in particular G-networks models, that allow the efficient representation of systems of agents and offer the possibility to analyze their behavior using mathematics. This approach is capable of modelling the system at different abstraction levels, both in terms of the number of agents and the size of the geographical location. We demonstrate our approach with some urban military planning scenarios and the results suggest that this approach has tackled the problem in modelling autonomous systems at low computational cost. Apart from offering the numerical estimates of the outcome, the approach helps us identify the characteristics that impact the system most and allows us to compare alternative strategies.