Showing posts with label Multi-agent Systems. Show all posts
Showing posts with label Multi-agent Systems. Show all posts

Friday, October 19, 2018

New Paper: Scalability in the MASON Multi-agent Simulation System

Previously we posted about our work on advancing MASON, part of which we briefly discussed making it distributed in order to  run large scale models including geographical explicit ones along for optimization and validation purposes. To this end we recently had a paper accepted and presented at the  22nd International Symposium on Distributed Simulation and Real Time Applications (DS-RT 2018),  entitled "Scalability in the MASON Multi-agent Simulation System". 

In this paper we describe a distributed version of the MASON, and use three existing MASON models: HeatBugs, Flockers, and CampusWorld, to demonstrate how Distributed MASON achieves highly scalable performance, in terms of linear performance increases as the size of the simulations grow using Amazon Web Services.  Below you can read the abstract of the paper, see  some figures relating to how we go about data management and some of the results. Finally, at the bottom of the post you can see the full reference and access the paper itself.

Abstract:
This paper describes Distributed MASON, a distributed version of the MASON agent-based simulation tool. Distributed MASON is architected to take advantage of well known principles from Parallel and Discrete Event Simulation, such as the use of Logical Processes (LP) as a method for obtaining scalable and high performing simulation systems. We first explain data management and sharing between LPs and describe our approach to load balancing. We then present both a local greedy approach and a global hierarchical approach. Finally, we present the results of our implementation of Distributed MASON on an instance in the Amazon Cloud, using several standard multi-agent models. The results indicate that our design is highly scalable and achieves our expected levels of speed-up.




Full Reference:
Wang, H., Wei, E., Simon, R., Luke, S., Crooks, A.T., Freelan, D. and Spagnuolo, C.  (2018), Scalability in the MASON multi-agent simulation system, in Besada, E., Polo, Ó.R., De Grande, R. and Risco J.L (eds.). Proceedings of the 22nd International Symposium on Distributed Simulation and Real Time Applications, Madrid, Spain, pp. 135-144. (pdf)

This research is supported by the National Science Foundation (Grant 1727303).

Wednesday, July 04, 2018

MASON Update

At the upcoming Multi-Agent-Based Simulation (MABS) workshop, we have a paper entitled "The MASON Simulation Toolkit: Past, Present, and Future" in which we discuss MASON's development history, its design and (probably more interesting) where MASON is going. This includes:
  1. Making it more robust (i.e. easier to run parameter tests), 
  2. Making it distributed in order to  run large scale models including geographical explicit ones along for optimization and validation purposes.
  3. Making it more coder-friendly by adding code templates that allow users to generate code skeletons for common MASON patterns and a way to easily record outputs and statistics.
  4. Making it more community-friendly by hopefully developing a special online repository to enable researchers to distribute models as jar files along with education aids and examples. Relating to this last point we have added a number of example models (code and data) from our own research to GitHub, see: https://github.com/eclab/mason/tree/master/contrib/geomason/sim/app/geo and the data to run the models is either there or here https://cs.gmu.edu/~eclab/projects/mason/extensions/geomason/geodemodata.zip (note this is 1.5 GB).
Below you can read the abstract from the paper along with a link to the paper itself.

Example Applications of MASON

Abstract
MASON is a widely-used open-source agent-based simulation toolkit that has been in constant development since 2002. MASON’s architecture was cutting-edge for its time, but advances in computer technology now offer new opportunities for the ABM community to scale models and apply new modeling techniques. We are extending MASON to provide these opportunities in response to community feedback. In this paper we discuss MASON, its history and design, and how we plan to improve and extend it over the next several years. Based on user feedback will add distributed simulation, distributed GIS, optimization and sensitivity analysis tools, external language and development environment support, statistics facilities, collaborative archives, and educational tools.

Keywords: Agent-Based Simulation, Open Source, Library

Full Reference:
Luke, S., Simon, R., Crooks, A.T., Wang, H., Wei, E., Freelan, D., Spagnuolo, C., Scarano, V., Cordasco, G. and Cioffi-Revilla, C. (2018), The MASON Simulation Toolkit: Past, Present, and Future, 19th International Workshop on Multi-Agent-Based Simulation (MABS2018), Stockholm, Sweden. (pdf)

Available on Github


This research is supported by the National Science Foundation (Grant 1727303).

Thursday, April 03, 2014

Multi-Agent Systems for Urban Planning

Recently we contributed a chapter to "Technologies for Urban and Spatial Planning: Virtual Cities and Territories" which aims to quote from the preference:  
"(i) to contribute to the dissemination of the recent research and development of the use of information and communication technologies (ICT) in urban and spatial planning, trying to demonstrate their usability in planning processes through the presentation of relevant case studies, framed by their underlying theory; (ii) to give additional evidence to the fact that ICT are the privileged means to produce virtual cities and territories; and (iii) to make available, from a pedagogical standpoint, a group of illustrative reviews of the scientific production made by both academics and practitioners in the field."
The book has 11 chapters which are grouped in several themes:
"first group focuses on the discussion over the use of ICT in spatial planning; the second group of contributions deals with urban modelling and simulation; the third group focuses on the use of different sensors to acquire information and model spatial processes; the fourth group focuses on the use of data to create more capable visualization tools; and the fifth group is about the use of virtual models to simulate real environments and plan and manage other aspects of the built environment such as energy."
Our chapter is entitled "Multi-agent Systems for Urban Planning" fits into the second group with respect to urban modeling and simulation. We present a detailed overview about the theory and the development of multi-agent systems (MAS) in spatial planning, focusing on how MAS can lead to insights into urban problems and aid urban planning fostering a bottom up approach to spatial planning. The abstract is as follows:
Cities provide homes for over half of the world's population, and this proportion is expected to increase throughout the next century. The growth of cities raises many questions and challenges for urban planning including which cities and regions are most likely to grow, what the pattern of urban growth will be, and how the existing infrastructure will cope with such growth. One way to explore these types of questions is through the use of multi-agent systems (MAS) that are capable of modeling how individuals interact and how structures emerge through such interactions, in terms of both the social and physical environment of cities. Within this chapter, the authors focus on how MAS can lead to insights into urban problems and aid urban planning from the bottom up. They review MAS models that explore the growth of cities and regions, models that explore land-use patterns resulting from such growth along with the rise of slums. Furthermore, the authors demonstrate how MAS models can be used to model transportation and the changing demographics of cities. Through these examples the authors also demonstrate how this style of modeling can give insights into such issues that cannot be gleamed from other modeling methodologies. The chapter concludes with challenges and future research directions of MAS models with respect to capturing the dynamics of human behavior in urban planning.

Full Reference:
Crooks, A.T., Patel, A. and Wise, S. (2014), Multi-agent Systems for Urban Planning, in Pinto, N.N., Tenedório, J. Antunes A. P. and Roca, J. (eds.), Technologies for Urban and Spatial Planning: Virtual Cities and Territories, IGI Global, Hershey, PA, pp. 29-56. (pdf)