177,999 research outputs found
Modelling of Multi-Agent Systems: Experiences with Membrane Computing and Future Challenges
Formal modelling of Multi-Agent Systems (MAS) is a challenging task due to
high complexity, interaction, parallelism and continuous change of roles and
organisation between agents. In this paper we record our research experience on
formal modelling of MAS. We review our research throughout the last decade, by
describing the problems we have encountered and the decisions we have made
towards resolving them and providing solutions. Much of this work involved
membrane computing and classes of P Systems, such as Tissue and Population P
Systems, targeted to the modelling of MAS whose dynamic structure is a
prominent characteristic. More particularly, social insects (such as colonies
of ants, bees, etc.), biology inspired swarms and systems with emergent
behaviour are indicative examples for which we developed formal MAS models.
Here, we aim to review our work and disseminate our findings to fellow
researchers who might face similar challenges and, furthermore, to discuss
important issues for advancing research on the application of membrane
computing in MAS modelling.Comment: In Proceedings AMCA-POP 2010, arXiv:1008.314
Overview on agent-based social modelling and the use of formal languages
Transdisciplinary Models and Applications investigates a variety of programming languages used in validating and verifying models in order to assist in their eventual implementation. This book will explore different methods of evaluating and formalizing simulation models, enabling computer and industrial engineers, mathematicians, and students working with computer simulations to thoroughly understand the progression from simulation to product, improving the overall effectiveness of modeling systems.Postprint (author's final draft
Shingle 2.0: generalising self-consistent and automated domain discretisation for multi-scale geophysical models
The approaches taken to describe and develop spatial discretisations of the
domains required for geophysical simulation models are commonly ad hoc, model
or application specific and under-documented. This is particularly acute for
simulation models that are flexible in their use of multi-scale, anisotropic,
fully unstructured meshes where a relatively large number of heterogeneous
parameters are required to constrain their full description. As a consequence,
it can be difficult to reproduce simulations, ensure a provenance in model data
handling and initialisation, and a challenge to conduct model intercomparisons
rigorously. This paper takes a novel approach to spatial discretisation,
considering it much like a numerical simulation model problem of its own. It
introduces a generalised, extensible, self-documenting approach to carefully
describe, and necessarily fully, the constraints over the heterogeneous
parameter space that determine how a domain is spatially discretised. This
additionally provides a method to accurately record these constraints, using
high-level natural language based abstractions, that enables full accounts of
provenance, sharing and distribution. Together with this description, a
generalised consistent approach to unstructured mesh generation for geophysical
models is developed, that is automated, robust and repeatable, quick-to-draft,
rigorously verified and consistent to the source data throughout. This
interprets the description above to execute a self-consistent spatial
discretisation process, which is automatically validated to expected discrete
characteristics and metrics.Comment: 18 pages, 10 figures, 1 table. Submitted for publication and under
revie
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