Agent-based Simulation Platform Evaluation in the Context of Human Behavior Modeling

1. Agent-based Simulation Platform Evaluation in the Context of Human Behavior Modeling Michal Laclavík, Štefan Dlugolinský, Martin Šeleng, Marcel Kvassay, Bernhard Schneider, Holger Bracker, Michał Wrzeszcz, Jacek Kitowski, Ladislav Hluchý

2. IKT Group - Institute of Informatics SAS URL: http://ikt.ui.sav.sk Dept. of Parallel and Distributed Computing Research and Development Areas: • Large-scale HPCN and Grid applications • Intelligent and Knowledge oriented Technologies Experience from European projects: • 6 project in FP6: EGEE II, K-Wf Grid, DEGREE (coordinator), EGEE, int.eu.grid, MEDIGRID • 4 projects in FP7: Commius, Admire, EGEE III, Secricom • 1 EDA project:EUSAS Several National Projects (SPVV, VEGA, APVT) IKT Group Focus: • Multi-Agent Systems • Information Processing • Semantic Web • Knowledge oriented Technologies • Parallel and Distributed Information Processing Solutions: • AgentOWL: semantic web and FIPA agents • Ontea: Pattern-based Semantic Annotation • ACoMA: KM tool in Email • EMBET: Recommendation System Director & leader of PDC: Dr. Dipl. Ing. Ladislav Hluchý ITMAS 2011

3. EDA R&T JIP FP project(for R&T JointInvestment Programme on Force Protection) EuropeanUrbanSimulation forAsymmetricScenarios ITMAS 2011

4. Learning EUSAS Objectives results qualitative evaluation Serious Game updates interactions Training Modelling Rules Of Engagement updates results models Data Farming updates interactions quantitative evaluation Analysis results ITMAS 2011

5. ABS Evaluation Approach • Survey Literature • List of available simulation platforms is in Deliverable Appendix • Existing ABS evaluations were considered • Evaluation Criteria/Features • 12 features selected – list on the next slide • Principle: Evaluation through implementation • Exemplary Human Behavior scenario defined • Civilians getting angry (throwing stones) or afraid (running to safety). • Soldiers arresting Civilians if hit by stone • Implemented in MASON, NetLogo and VBS2 ITMAS 2011

6. Survey Literature • Stupid Agent Model • 16 features • S.F. Railsback, S.L. Lytinen and S.K. Jackson Agent Based Simulation Platforms: Review and Development Recommendations Simulation 8:9 (2005) • NetLogo, MASON, Repast, Swarm and Java Swarm • Later also others, like EcoLab • Human Behavior Modeling • We have chose 12 features • Generic, but evaluated on Human Behavior Scenario ITMAS 2011

7. 12 Features selected as evaluation criteria • Loading and Representing the Environment and the Scenario • Creating and Representing Agents • Behavior Implementation • Movement Implementation • Visualization • Parameterization • Model check-pointing • Analytical Tools • Logging • Performance • Standards • Development Environment ITMAS 2011

8. Exemplary Human Behavior Scenario • Soldiers • Catching civilians if hit twice by stone • If civilian is caught (arrested), civilian will disappear • Soldiers are robotic (no emotions) • Civilians • Driven by 2 emotions (fear and anger) • When angry, trying to find stone, going to soldier and throws the stone • When afraid, flying to safety area (yellow) ITMAS 2011

9. NETLOGO 4.1 • Advantages • NetLogo can be invoked and controlled by another program running on the JVM by Controlling facility API (e.g. app which automates series of model runs, embed NetLogo models in a larger app) • Simulation state (i.e. world) can be saved to a CSV file and later loaded • Java API for creating custom extensions to NetLogo (commands, reporters) • Models can be run without visualization • Ability to load vector GIS data (points, lines, and polygons - ESRI shapefiles), and raster GIS data (grids) into NetLogo by GIS extension • Easy to draw graphs, create simulation parameter controllers (sliders, buttons, etc.) • Many useful tools like BehaviorSpace, System Dynamics Modeler, HubNet, Logging ITMAS 2011

10. NETLOGO 4.1 • Disadvantages • Slower than Mason, some parts of user code are interpreted at runtime • The Controlling facility API is considered experimental. It is likely to continue to change and grow. • Support for creating 3D worlds is still in an experimental state. Only 2D world is fully supported. ITMAS 2011

11. MASON • Advantages • Fast, overhead of simulation environment is minimal • Java • Models are completely independent from visualization • Models may be checkpointed and recovered • Agents are not forced to have a physical location, which is good if we want to create agents representing groups (meso and macro levels) • Physical environment – any number of 2D or 3D layers • Multiple Displays of simulation • Time series Graphs, variable inspectors • GIS data can be loaded • Disadvantages • NetLogo has better support for movement and analysis of distances, objects etc. in physical environment – this impacts development speed, but gives flexibility ITMAS 2011

12. VBS2 • Integration Challenges • VBS is thread- and event-based. Our candidate ABS systems (MASON and NetLogo) are step-based. Integration is not straightforward but feasible. • Changing the action in the middle of its execution may cause a jerkinganimation. For example: while throwing a stone – the agent decides torun to the safety area • Movement in VBS may be executed a bit differently from what was planned and simulated in ABS: we need to use waypoints to minimize the discrepancy. ITMAS 2011

13. Evaluated Features • Loading and Representing the Environment and the Scenario • MASON • 2D, 3D, layered: IntGrid2D, Continuos2D • GIS support: tested • NetLogo • 2D: two-dimensional grid of “patches”, 3D experimental • easy import from bitmap • GIS support: tested • Creating and Representing Agents • Soldier, Civilian, Stone • MASON • Represented by Java class (Steppable interface), step(SimState state) method • access to environment : SimState state • NetLogo • Turtles (dynamic), patches, links and the observer ITMAS 2011

14. Evaluated Features • Behavior Implementation • NetLogo • turtle variable and the RUN command • MASON • step(SimState state) • Movement Implementation • NetLogo • Direction and step • MASON • Go to X, Y • Flocking, steering: implemented in demo • Visualization • MASON: strong separation of Model and Visualization • NetLogo: possibility to switch off visualization, speed does not change. ITMAS 2011

15. Evaluated Features • Parameterization • supported • Model check-pointing • Supported in both • MASON: platform independent • Analytical Tools • MASON: improvement over the years • Property inspectors • Video, snapshot, streaming, charts • NetLogo: • Property inspectors • snapshot, streaming, charts • Logging • MASON: log4j can be used • NetLogo: using log4j integration ITMAS 2011

16. Evaluated Features: Performance NetLogo Mason ITMAS 2011

17. Evaluated Features • Standards • Agent Standards: FIPA – not relevant for simulation agents • DIS and HLA standards • Relevant but we did not test • VBS2 will be integrated for training • we plan to use the plug-in functionality in VBS2 and CORBA technology • need to create a FOM - Federation Object Model • Java based HLA: • poRTIco • Java port of CERTI • Development Environment • Both step based, easy debug, better then thread based MAS • NetLogo: • NetLogo IDE, debugging using variable inspectors • MASON: • Any Java IDE, standard Java debbuging • We have used Eclipse ITMAS 2011

18. Conclusion • Both are almost equal in many features • NetLogo: better in physical movement support and some analytical tools. • MASON: much faster, supports strong separation of visualization and behavior models, better support for 3D environment, Java based - easier to integrate with other systems. ITMAS 2011