PARALLEL AGENT-BASED SIMULATION ON A CLUSTER OF WORKSTATIONS

2003 ◽  
Vol 13 (04) ◽  
pp. 629-641 ◽  
Author(s):  
Konstantin Popov ◽  
Mahmoud Rafea ◽  
Fredrik Holmgren ◽  
Per Brand ◽  
Vladimir Vlassov ◽  
...  
Keyword(s):  
Web Sites ◽  
Large Scale ◽  
Parallel Implementation ◽  
Simulation Models ◽  
Agent Based Simulation ◽  
Time Step ◽  
Agent Based ◽  
Simulation Step ◽  
Cluster Of Workstations ◽  
Concurrent Simulation

We discuss a parallel implementation of an agent-based simulation. Our approach allows to adapt a sequential simulator for large-scale simulation on a cluster of workstations. We target discrete-time simulation models that capture the behavior of Web users and Web sites. Web users are connected with each other in a graph resembling the social network. Web sites are also connected in a similar graph. Users are stateful entities. At each time step, they exhibit certain behaviour such as visiting bookmarked sites, exchanging information about Web sites in the "word-of-mouth" style, and updating bookmarks. The real-world phenomena of emerged aggregated behavior of the Internet population is studied. The system distributes data among workstations, which allows large-scale simulations infeasible on a stand-alone computer. The model properties cause traffic between workstations proportional to partition sizes. Network latency is hidden by concurrent simulation of multiple users. The system is implemented in Mozart that provides multithreading, dataflow variables, component-based software development, and network-transparency. Currently we can simulate up to 106 Web users on 104 Web sites using a cluster of 16 computers, which takes few seconds per simulation step, and for a problem of the same size, parallel simulation offers speedups between 11 and 14.

Agents in bioinformatics

2005 ◽  
Vol 20 (2) ◽  
pp. 117-125 ◽  
Author(s):  
MICHAEL LUCK ◽  
EMANUELA MERELLI
Keyword(s):  
Data Analysis ◽  
Simulation Models ◽  
Biological Data ◽  
System Modelling ◽  
Agent Based Simulation ◽  
Huge Amount ◽  
Agent Based ◽  
Information Agents ◽  
New Knowledge ◽  
Definition Of

The scope of the Technical Forum Group (TFG) on Agents in Bioinformatics (BIOAGENTS) was to inspire collaboration between the agent and bioinformatics communities with the aim of creating an opportunity to propose a different (agent-based) approach to the development of computational frameworks both for data analysis in bioinformatics and for system modelling in computational biology. During the day, the participants examined the future of research on agents in bioinformatics primarily through 12 invited talks selected to cover the most relevant topics. From the discussions, it became clear that there are many perspectives to the field, ranging from bio-conceptual languages for agent-based simulation, to the definition of bio-ontology-based declarative languages for use by information agents, and to the use of Grid agents, each of which requires further exploration. The interactions between participants encouraged the development of applications that describe a way of creating agent-based simulation models of biological systems, starting from an hypothesis and inferring new knowledge (or relations) by mining and analysing the huge amount of public biological data. In this report we summarize and reflect on the presentations and discussions.

Scoping the Future with Theory-Driven Models—Where’s the Uncertainty?: Commentary on Lustick and Tetlock 2021

2021 ◽  
Author(s):  
David R. Mandel
Keyword(s):  
National Security ◽  
Policy Decision ◽  
Simulation Models ◽  
Insufficient Data ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Strategic Intelligence ◽  
The Future ◽  
Policy Decision Making ◽  
Rich Data

Lustick and Tetlock outline an intellectually ambitious approach to scoping the future. They are particularly interested in sectors of national security and foreign policy decision-making that require anticipatory strategic intelligence that is difficult to produce because there is insufficient data, even if relevant theories are available. They propose that in these theory-rich/data-impoverished cases, there can be great value in developing agent-based simulation models that incorporate probabilistic rules that cohere with postulates of the theory or theories that are brought to bear on the intelligence challenge. This is the gist of the “simulation manifesto.” The aim of this commentary is to focus on the assessment and representation of key uncertainties in such models and I outline several ways in which uncertainty may arise in the process of simulation model construction.

The Agent Object Relationship Simulation as a Business Process

2010 ◽  
pp. 348-370
Author(s):  
Emilian Pascalau ◽  
Adrian Giuca ◽  
Gerd Wagner
Keyword(s):  
Business Process ◽  
Process Modeling ◽  
Simulation Models ◽  
Use Case ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Simulation Process ◽  
Simulation Paradigm ◽  
Business Process Modeling Notation ◽  
Object Relationship

The use of agent-based simulation models is growing and attracted a lot of attention recently both for researchers and business management. Agent-Object Relationship (AOR) is an agent-based simulation paradigm that uses reaction rules to model agents’ behavior. The goal of this chapter, besides exemplifying the AOR concepts by means of a use case, is to investigate the use of business process modeling notation (BPMN) to model the AOR simulation process. Moreover it discusses aspects of a distributed architecture for an AOR simulation system. The chapter concludes with the fact that BPMN is well suited to model the AOR simulation process.

Fairness in Agent Based Simulation Frameworks

2008 ◽  
Author(s):  
Lawrence Welch ◽  
Stephen Ekwaro-Osire
Keyword(s):  
Initial Conditions ◽  
Scheduling Algorithm ◽  
Random Order ◽  
Multiple Agents ◽  
Agent Based Simulation ◽  
Time Step ◽  
Thread Scheduling ◽  
Agent Based ◽  
Simulation Engine ◽  
Over Time

An agent based simulation engine should provide a fair playing field for all of its agents. A fundamental design axiom of agent based simulation frameworks is that the simulation engine should not arbitrarily bias its execution towards one agent or another. This fairness is basic to giving the agent modeler confidence that differences in behavior and performance between agents derive legitimately from the simulation modeling, initial conditions or specific agent characteristics, rather than the capriciousness of the underlying framework. One aspect of fairness in a simulation is the relative order of execution of agents over time. This order of execution is affected by techniques employed by frameworks to simulate the concurrent activities of multiple agents. One such technique is multi-threading. Multi-threaded operating systems, or programming languages and environments, such as Java, allow multiple agents, represented by software threads, to share the computer’s execution time by taking turns, thus appearing to act simultaneously. The precise order of execution of peer threads in multi-threaded applications is often out of the hands of the programmer, and may be determined exclusively by the operating system or program execution environment. However, if overlooked by the framework developer, the idiosyncrasies of a particular thread ordering mechanism can pass on to the modeler inherent random behavior that is neither intuitive, nor in line with the modeler’s expectations. To be considered fair, the engine should aim to provide all agents with equal probability of executing first within a time step, or last, or in any position in between. This paper analyzes the sequencing of agent thread execution within a Java framework that implements a multi-threaded, time-stepping, agent based simulation engine. The natural ordering of Java thread execution is demonstrated to be unfair (that is, not uniform) in its treatment of agents. This research shows that the standard mechanism of Java thread scheduling, while appropriate for most applications, is inappropriate on its own for the agent based framework. It is demonstrated that with Java’s standard thread scheduling algorithm, over time certain agents tend to execute ahead of others within each time step, while others tend to execute in the middle or at the back of the pack. This paper then introduces and demonstrates the “Uniform Specific Notification” pattern, a technique that produces a fairer, uniformly distributed random order for the initial execution of Java agent threads at each simulation time step.

The Agent Visualization System: A Graphical and Textual Representation for Multi-Agent Systems

2005 ◽  
Vol 4 (2) ◽  
pp. 83-94 ◽  
Author(s):  
Hala Mostafa ◽  
Reem Bahgat
Keyword(s):  
Information Visualization ◽  
Simulation Environment ◽  
Multi Agent Systems ◽  
Agent Based Simulation ◽  
Time Step ◽  
Visualization System ◽  
Agent Based ◽  
Agent Systems ◽  
Simulation Scenario ◽  
Multi Agent

As scientists from various domains increasingly resort to agent-based simulation for a more thorough understanding of real-world phenomena, the need for a simulation environment that facilitates rapid development of multi-agent systems is growing. Such a platform should provide means of visualizing the simulated scenario. In this paper we present the agent visualization system, the first system of its kind to specifically focus on catering to the visualization needs of agent-based simulation. The proposed system is a generic add-on that equips a simulation environment with a rich set of visualization facilities offering a variety of textual and graphical browsers that allow the modeler to detect trends and relationships in the simulation scenario. Some techniques from the field of information visualization were adapted and added to the system, while others were devised especially to be used in it. Regardless of their origin, all visualization techniques were thoroughly revised to make them generic enough to fit in our generic system. Agent visualization is more challenging than traditional information visualization in more than one respect. One of them is that the data to be visualized is not static; the simulation system is constantly producing data with every time step. Moreover, the sheer amount of data, together with its diversity, call for special adaptations to ensure that the system remains responsive and generic. To illustrate the various features of the proposed agent visualization system, we present a visualization of MicroTerra; a simulation scenario involving a group of beings trying to maximize their food intake.

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