Parallel Agent-Based Simulation on a Cluster of Workstations

Popov, Konstantin; Vlassov, Vladimir; Rafea, Mahmoud; Holmgren, Fredrik; Brand, Per; Haridi, Seif

doi:10.1007/978-3-540-45209-6_69

Cited by 12 publications

(3 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…13. Architecture and measured speedup of the iCities parallel agent simulator written in Mozart using the network-transparent distribution model (from [Popov et al 2003])…”

Section: Projects and Applicationsmentioning

confidence: 99%

“…The iCities European project ran from 2000 to 2003 and studied the evolution of inhabitants of virtual worlds (on-line communities), to understand emergent organizational patterns for the information society. This project used Mozart to develop a large-scale agent simulation platform (supporting millions of concurrent agents) running on workstation clusters [Popov et al 2003]. This work has shown empirically that the distribution of users on sites follows a universal power law [Lelis et al 2001].…”

Section: Projects and Applicationsmentioning

confidence: 99%

See 1 more Smart Citation

A history of the Oz multiparadigm language

Roy

Haridi

Schulte

et al. 2020

Proc. ACM Program. Lang.

Self Cite

View full text Add to dashboard Cite

Oz is a programming language designed to support multiple programming paradigms in a clean factored way that is easy to program despite its broad coverage. It started in 1991 as a collaborative effort by the DFKI (Germany) and SICS (Sweden) and led to an influential system, Mozart, that was released in 1999 and widely used in the 2000s for practical applications and education. We give the history of Oz as it developed from its origins in logic programming, starting with Prolog, followed by concurrent logic programming and constraint logic programming, and leading to its two direct precursors, the concurrent constraint model and the Andorra Kernel Language (AKL). We give the lessons learned from the Oz effort including successes and failures and we explain the principles underlying the Oz design. Oz is defined through a kernel language, which is a formal model similar to a foundational calculus, but that is designed to be directly useful to the programmer. The kernel language is organized in a layered structure, which makes it straightforward to write programs that use different paradigms in different parts. Oz is a key enabler for the book Concepts, Techniques, and Models of Computer Programming (MIT Press, 2004). Based on the book and the implementation, Oz has been used successfully in university-level programming courses starting from 2001 to the present day. CCS Concepts: • Social and professional topics → Computing education; • Theory of computation → Constraint and logic programming; Process calculi; Operational semantics; • Software and its engineering → Distributed programming languages; Object oriented languages; Functional languages; Constraint and logic languages; Data flow languages; Multiparadigm languages; Semantics; Graphical user interface languages; • Human-centered computing → User interface programming; User interface toolkits.

show abstract

“…13. Architecture and measured speedup of the iCities parallel agent simulator written in Mozart using the network-transparent distribution model (from [Popov et al 2003])…”

Section: Projects and Applicationsmentioning

confidence: 99%

Section: Projects and Applicationsmentioning

confidence: 99%

A history of the Oz multiparadigm language

Roy

Haridi

Schulte

et al. 2020

Proc. ACM Program. Lang.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Again, there are no concrete statements concerning scalability of this system. Popov et. al (2003) describe a parallel sequential simulation approach to simulate 10 6 agents to capture the behavior of web users.…”

Section: Related Workmentioning

confidence: 99%

Scalability in distributed simulations of agent-based models

Pawlaszczyk¹,

Strassburger²

2009

Proceedings of the 2009 Winter Simulation Conference (WSC)

View full text Add to dashboard Cite

Research on systems of autonomous agents, called multiagent systems (MAS), has received much interest in the domain of (distributed) artificial intelligence in recent years. MAS are most suitable for the development of distributed applications within an uncertain and dynamically changing environment (Logan 2005). For validation of such systems agent based simulation is a new modeling paradigm not limited to systems which qualify as MAS by default. The focus of the work presented here is on scalability aspects of simulation environments for agent based simulations. Scalable solutions are required, as complex models require the capability to simulate hundreds or more complex deliberative agents. This is a capability which is often lacking in existing simulation environments for agents. We investigate different aspects which influence scalability and present a solution for enabling a scalable and efficient distributed simulation of agent-based models based on an adapted optimistic synchronization protocol which limits the level of optimism by using knowledge about agent interaction patterns.

show abstract

Agent Based Modeling, Large Scale Simulations

Parry¹

2009

Encyclopedia of Complexity and Systems Science

View full text Add to dashboard Cite

Parallel Agent-Based Simulation on a Cluster of Workstations

Cited by 12 publications

References 20 publications

A history of the Oz multiparadigm language

A history of the Oz multiparadigm language

Scalability in distributed simulations of agent-based models

Agent Based Modeling, Large Scale Simulations

Contact Info

Product

Resources

About