Visualization in Biologically Inspired computation (Agent Based Model)

1. Visualization in Biologically Inspired computation(Agent Based Model) UKAUMUNNA CHIEMEZIE Department of Computer Science University Of Texas Pan American

2. Overview • Simulation: a definition and motivations • Agent Based Simulation • Differences from other approaches • Peculiarities, advantages and risks • Some reflections • From reality, to models, to a simulation • The role of the environment and indirect interaction models • Frameworks and tools supporting Agent-Based modeling and simulation • Demonstration of some sample models with NETLOGO • Conclusions

3. Complex Systems? • Definitions • A complex system is a highly structured system, which shows structure with variations • A complex system is one whose evolution is very sensitive to initial conditions or to small perturbations, one in which the number of independent interacting componentsis large, or one in which there are multiple pathwaysby which the system can evolve • A complex system is one that by design or function or both is difficult to understand and verify • A complex system is one in which there are multiple interactions between many different components • Complex systems are systems in process that constantly evolveand unfold over time

4. Features of complex systems • Composed by several interacting elements • Nonlinearity • Networked structure • Hierarchical structure • Positive and negative feedbacks • Possibility to evolve and adapt • Robustness and plasticity • One of their main features is their internal structure and the interaction among their composing parts… that very often is studied by means of simulations

5. Simulation: definition • Computer) Simulation represents a way to exploit a computational model • to evaluate designs and planswithout actually bringing them into existence in the real world • to evaluate theories and modelsof complex systems by envisioning the effect of the modeling choices, with the aim of gaining insight of their functioning

6. Simulation: motivations • The use of “synthetic environments”is sometimes necessary, because the simulated system cannot actually be observed • Because it is actually being designed • For ethical or practical reasons

7. Simulation life-cycle Simulation execution • From the target systemto its computational model and a simulator • Execution of a simulation campaign • Evaluation/validationof the model (and simulator) against collected data • Possible usage for explaination and/or prediction Data generated by thesimulation(s) Model and simulator Analysis of results + interpretation(model evaluation leading to explanation orprediction) Modelingand designof a simulator Collected Data Target System Dynamics of Target System

8. A reflection: from reality,to models, to a simulation • The overall simulation project involves several phase, roles, types of knowledgeand competences • The frequent passages (translation, encoding, decoding, interpretation...) between different levels of abstraction can lead to several problems • Non documented assumptions • Unrealistic/unfeasible simplifications • Simulation projects are difficult Simulator Computationalmodel Abstractmodel Subsystem Reality

9. Agent Based Models for simulation: peculiarities, advantages, risks • The analytical unit is the individualagent, not aggregate variables • This means, on one hand, that additional insight on the modeled system is required • On the other hand such a model should be able to • Generate the same aggregate dynamics as traditional ones • Be able to represent, manage, analyze additional aspects, such as for instance spatial ones

10. The role of the environment Agent Agent Agent • Most agent definitions include the term environment, but in some cases it is conceived as a mere communication infrastructure • In most simulation scenarios the environment plays a more relevant role, since • Spatial features of the environment influence agents behaviour • Relevant simulation results are often spatially related • The environment is used as a ‘channel’ for (indirect) agent interaction • Some laws and properties must be enacted and preserved: the environment is the best ‘place’ to do so Communication infrastructure Comprehensive environment Agent Agent Agent

11. Agent Based Modeling Tools andSimulation Frameworks • They provide tools facilitating the development of agent-based simulations • in terms of abstractions and mechanisms • in terms of generic functionalities (e.g. monitoring and visualization, scheduling and control of the simulation, data export and analysis) • Three main categories • General purpose frameworks based on ‘ad-hoc languages’ (e.g. Logo dialects) • General purpose frameworks based on general purpose languages (e.g. Java) • Model specific frameworks (SimSesam)

12. Software used for implementing ABS Different agent based software have been used for implementing ABSS (Tobias & Hofmann 2004) such as • Repast • Multi Agent Simulation Suite (MASS). Fables is a component of MASS, generating Repast J models • Swarm (simulation) • Janus: Multiagent, Organizational and Holonic Platform. • Ascape(an implementation of the agent based model Sugarscape) • IngeniasSeSAm Multiagent simulator and graphical modelling environment. (Free Software) • NetLogo(Free Software) • GlobalSimulateMultiparadigmsimulation and modelling environment. (Open Source Software)

13. Demonstration of sample models with NETLOGO • Ant lines: In these models the behavior of ants following a leader towards a food source. The leader ant moves towards the food along a random path; after a small delay, the second ant in the line follows the leader by heading directly towards where the leader is located. Each subsequent ant follows the ant ahead of it in the same manner.

14. NETLOGO • Flocking: This model is an attempt to mimic the flocking of birds. (The resulting motion also resembles schools of fish.) The flocks that appear in this model are not created or led in any way by special leader birds. Rather, each bird is following exactly the same set of rules, from which flocks emerge. .

15. NETLOGO • Ants: In this model, a colony of ants forages for food. Though each ant follows a set of simple rules, the colony as a whole acts in a sophisticated way

16. Conclusions • Agent Based models represent a useful kind of instrument • for the study of complex systems • to realize simulators support decision making activities (system design, configuration, operation) • These models have different requirementsand ‘performances’ from traditional approaches, but there is no silver bullet... • Simulation is a complex activity, with several possible pitfalls • Benefits of being aware of the approaches, techniques and advances of different disciplinesin this topic

17. References • General resources • Growing Artificial Societies: Social Science From the Bottom Up, Joshua M. Epstein and Robert L. Axtell, MIT Press 1996 • MABS workshops serie (Lecture Notes in Computer Science vol. 1534, 1979, 2581, 2927, 3415, 3891, Springer-Verlag) (http://www.pcs.usp.br/~mabs/) • Agent Based Modeling and Simulation Symposium, at EMCSR (www.lintar.disco.unimib.it/ABModSim) • SWARM Development Group Wiki (http://www.swarm.org): “A resource for agent- and individual-based modelers and the home of Swarm” • Individual-Based Models (http://www.red3d.com/cwr/ibm.html): an annotated list of links by Craig Reynolds (the author of the Boids model) • Pedestrian modeling • Environment and planning B, vol. 28 no. 3, Theme issue: Pedestrian modeling, Editor: Michael Batty • In general Environment and planning is a good source of papers adopting agent based models in the architecture and urban planning area • Social simulation • Simulation for the Social Scientist, Nigel Gilbert, Klaus G. Troitzsch, Open University Press • Journal of Artificial Societies and Social Simulation (http://jasss.soc.surrey.ac.uk/JASSS.html) • Biomedical context MAS*BIOMED workshop series

18. Questions