Whereas Multi-Agent Based Simulation (MABS) is emerging as a reference approach for complex system simulation, the event-driven approach of Discrete-Event Simulation (DES) is the most used approach in the simulation mainstream. In this talk, we elaborate on two intuitions: 1) event-based systems and multi-agent systems are amenable of a coherent interpretation within a unique conceptual framework; 2) integrating MABS and DES can lead to a more expressive and powerful simulation framework. Accordingly, we propose a computational model integrating DES and MABS, based on an extension of the Gillespie's stochastic simulation algorithm. Then, we discuss a case of a simulation platform (Alchemist) specifically targeted at such a kind of complex models, and show an example of urban crowd steering simulation.

1. Extending the Gillespie’s Stochastic Simulation
Algorithm for Integrating Discrete-Event and
Multi-Agent Based Simulation
Sara Montagna, Andrea Omicini, Danilo Pianini
{sara.montagna, andrea.omicini, danilo.pianini}@unibo.it
Alma Mater Studiorum—Universit`a di Bologna a Cesena
The XVI International Workshop on Multi-Agent Based Simulation
May 5, 2015 - Istanbul, Turkey
Montagna, Omicini, Pianini (UNIBO) Gillespie’s SSA to Integrate DES and MABS 2015-05-15 MABS 1 / 22

2. Outline
1 Background
Discrete Event Simulation (DES)
Agent Based Modelling (ABM) and Simulation (MABS)
2 Motivation
3 A Unified Stochastic Computational Model
Model
Simulation algorithm
4 Tools: The ALCHEMIST Platform
5 Conclusion
Montagna, Omicini, Pianini (UNIBO) Gillespie’s SSA to Integrate DES and MABS 2015-05-15 MABS 2 / 22

3. Background Discrete Event Simulation (DES)
DES
. . . The most used approach in the simulation mainstream
Instantaneous events responsible for the changes in the system state
In between events, no change to the system is assumed to occur
All events are ordered (even if they happen at the exact same time)
It is normally very efficient since it allows to jump in time from one
relevant event
Defining a DES means to model the behaviour of a system as an ordered
sequence of non-continuous events, by specifying for each of them the
perturbations in the system state it provokes, and the exact point in time
when it has to be triggered.
Montagna, Omicini, Pianini (UNIBO) Gillespie’s SSA to Integrate DES and MABS 2015-05-15 MABS 3 / 22

4. Background Agent Based Modelling (ABM) and Simulation (MABS)
MABS I
MABS (Multi-Agent Based Simulation) was introduced as a novel and
alternative approach to Discrete Event Simulation (DES) and to Systems
Dynamic (SD)
Rather than a simulation method, it is a simulation paradigm
ABM models the system at the micro-level
active entity are autonomous and interacting agent
dynamic environment
global system-level behaviour as a result of the agent-to-agent
interactions
Montagna, Omicini, Pianini (UNIBO) Gillespie’s SSA to Integrate DES and MABS 2015-05-15 MABS 4 / 22

5. Background Agent Based Modelling (ABM) and Simulation (MABS)
MABS II
The simulation platform
Various platforms were developed for the purpose [All10],
tools for developing, editing, and executing ABM, as well as for
visualising the simulation dynamic.
How do they operate over a timeline?
How are agents and environment behaviours coupled and scheduled?
Montagna, Omicini, Pianini (UNIBO) Gillespie’s SSA to Integrate DES and MABS 2015-05-15 MABS 5 / 22

6. Background Agent Based Modelling (ABM) and Simulation (MABS)
Crucial Issue: Time
How to deal with the evolution of time?
Continuous approaches
Rare
Specifically used for modelling the endogenous dynamic of the
environment coupled with discrete agent internal processes.
Discrete approaches
Time-driven
most widely used
fixed time steps
Event-driven
Montagna, Omicini, Pianini (UNIBO) Gillespie’s SSA to Integrate DES and MABS 2015-05-15 MABS 6 / 22

7. Motivation
Motivation: Why not Time-driven?
Efficiency
Time passes fixed time steps
even if no action changes the state happen in between
Carefully choice of temporal granularity
low granularity may ruin results
high granularity may lead to a waste of computational resources
Accuracy, validity, coherency
All the events happening in the same ∆t are executed together
possible loss of order and changes on the system outcome
to be as close as possible to the MAS paradigm, actions and
interactions should be conducted concurrently
Congruence
Simultaneous updates of system entities is far from reality
Montagna, Omicini, Pianini (UNIBO) Gillespie’s SSA to Integrate DES and MABS 2015-05-15 MABS 7 / 22

8. Motivation
From the Time-driven Drawbacks to Our Contribution
Based on two intuitions
a unified conceptual framework [Omi15]
coherent interpretation of event-driven and multi-agent systems
powerful simulation framework [Mey14]
from the integration of DES and MABS
. . . and one observation
Gillespie’s SSA [Gil77] as an event-driven algorithm
Our proposal
A computational model integrating DES and MABS based on an extension
of the Gillespie’s stochastic simulation algorithm.
Montagna, Omicini, Pianini (UNIBO) Gillespie’s SSA to Integrate DES and MABS 2015-05-15 MABS 8 / 22

9. A Unified Stochastic Computational Model
Gillespie’s SSA as an Event-driven Algorithm
Gillespie’s algorithm
Gillespie first proposed an event driven stochastic simulation algorithm
(SSA) for the exact stochastic simulation of chemical systems
Gibson and Bruck [GB00] improved its performance
Next reaction selection not by propensity (function of concentration of
reagents and a markovian rate) but by generated putative times
Dependency graph meant to update only the events whose scheduling
time might have changed because of other events
Building on their work, we extended the algorithm in order to be able
to shift from the world of chemistry to the richer MABS world
Montagna, Omicini, Pianini (UNIBO) Gillespie’s SSA to Integrate DES and MABS 2015-05-15 MABS 9 / 22

10. A Unified Stochastic Computational Model Model
Generalised Chemistry
Pure chemistry vs. agent-based systems
Single, static compartment versus multiple, possibly mobile, and
interconnected agents whose ability to communicate may depend on
environmental and technological factors
Molecules are described by concentrations (an integer), agents may
carry and process any kind of data
Reactions “scheduling” in nature follows a Poisson distribution whose
rate equation depends on reagents’ concentration [Gil77]. Events in
an agent-based simulation may be influenced by any of the
environment components and follow any probability distribution
(triggers, timers, events with memory)
Agents live in an environment, such abstraction is absent in chemistry
A huge leap, indeed
Montagna, Omicini, Pianini (UNIBO) Gillespie’s SSA to Integrate DES and MABS 2015-05-15 MABS 10 / 22

11. A Unified Stochastic Computational Model Model
Close the Gap: Environment
ReactionA proactive behaviour
Linking Rule
A function of the environment
that decides wether or not
two nodes are connected
Moleculetoken representing a
chunk of data
(think of it as a pointer)
ConcentrationActual data associated
with a "molecule"
Environment
Riemannian manifold
where nodes live
NodeA container of reactions
and molecules situated
in the environment
Montagna, Omicini, Pianini (UNIBO) Gillespie’s SSA to Integrate DES and MABS 2015-05-15 MABS 11 / 22

12. A Unified Stochastic Computational Model Model
Close the Gap: Reactions
Number of
neighbors<3
Node
contains
something
Any other
condition
about this
environment
Rate equation: how conditions
influence the execution speed
Conditions Probability distribution Actions
Any other
action
on this
environment
Move a node
towards...
Change
concentration
of something
Reaction
Montagna, Omicini, Pianini (UNIBO) Gillespie’s SSA to Integrate DES and MABS 2015-05-15 MABS 12 / 22

13. A Unified Stochastic Computational Model Model
Flexibility and Data Types
Abstract Concentration
Concentration can be any data type
Pick integers, the result is a simulator for (bio)chemistry, with multiple
intercommunicating compartments situated in an environment
[MPV12]
Pick “set of tuples matching a tuple template”, the result is a
simulator of network of programmable tuple spaces
Pick “any object”, the result is flexibile enough to simulate a network
of devices running their own program [PVB15]
For each type of concentration, a specific set of legal conditions and
actions can (must) be defined
All the other entities can be defined in a generic fashion, and reused
Montagna, Omicini, Pianini (UNIBO) Gillespie’s SSA to Integrate DES and MABS 2015-05-15 MABS 13 / 22

14. A Unified Stochastic Computational Model Simulation algorithm
Extended SSA—Phase 1: Pick the Next Event
How to select the next event?
Most high-performance SSAs presume an underlying model that only
includes memoryless events [STP08]
Gibson/Bruck’s “next reaction” uses putative times instead
We extended it adding support for addition and removal of events at
runtime
Agents may join and leave the system at runtime, new agents may be
equipped with novel behaviours
Montagna, Omicini, Pianini (UNIBO) Gillespie’s SSA to Integrate DES and MABS 2015-05-15 MABS 14 / 22

15. A Unified Stochastic Computational Model Simulation algorithm
Extended SSA—Phase 2: Dependency Management
How to select the next event?
The dependency graph is key for the high performance of SSAs
[STP08]
In general, it is very hard to build a dependency graph in an open
environment composed of multiple entities
We extended the original concept of (static) dependency graph with:
Events can be added and removed at runtime, the graph is dynamically
updated
Execution contexts: local, neighborhood, global
Separation of influencing context and context of influence (input and
output)
Overall, the dependency graph is greatly pruned, with positive impact
on performance
Montagna, Omicini, Pianini (UNIBO) Gillespie’s SSA to Integrate DES and MABS 2015-05-15 MABS 15 / 22

16. Tools: The ALCHEMIST Platform
The ALCHEMIST Platform as a Tool 1
ALCHEMIST: A chemistry-inspired meta-simulator
Based on the machinery already described
Java written
Provides out of the box support for simulating distributed
programmable tuple spaces and Protelis [PVB15] devices
Supports mobility and complex environments, both indoor and
outdoor (with data from OpenStreetMap)
Available as Maven artifact (it.unibo.alchemist:alchemist)
1
http://alchemist.apice.unibo.it
Montagna, Omicini, Pianini (UNIBO) Gillespie’s SSA to Integrate DES and MABS 2015-05-15 MABS 16 / 22

17. Tools: The ALCHEMIST Platform
Architecture of ALCHEMIST
Dynamic Dependency Graph
Dynamic Reaction Manager
Discrete Event Engine
Core
Environment
(model)
Reporting
Graphical Output
Logging System
Interactive UI
XML Bytecode
Environment Builder
Language
Chain
DSL to XML Compiler
Domain Specific Language
Montagna, Omicini, Pianini (UNIBO) Gillespie’s SSA to Integrate DES and MABS 2015-05-15 MABS 17 / 22

18. Tools: The ALCHEMIST Platform
Case Study: Crowd-sensitive User Steering in London
(a) Initial (b) Ongoing (c) Near-end
Figure: Three snapshots of the simulation. Our altered distance metric (the
gradient) goes from green (most favourable and close to destination) to red
(unpleasant or far from destination). Agents (in black) climb this structure
towards greener values.
Montagna, Omicini, Pianini (UNIBO) Gillespie’s SSA to Integrate DES and MABS 2015-05-15 MABS 18 / 22

19. Tools: The ALCHEMIST Platform
Case Study: Crowd-sensitive User Steering in London
Montagna, Omicini, Pianini (UNIBO) Gillespie’s SSA to Integrate DES and MABS 2015-05-15 MABS 19 / 22

20. Conclusion
Conclusion
Integration of DES and MABS
We adopted an extension of Gillespie’s SSA as stochastic event-driven
algorithm
We extended it to support the inherent complexity of multiagent
systems, still retaining the performance optimisations
We extended the chemical model towards higher flexibility,
introducing the environment, generalising the concept of reaction and
allowing arbitrary data to be a “concentration”
We implemented those concepts inside the ALCHEMIST framework
A non-trivial example was provided: crowd steering in London
Montagna, Omicini, Pianini (UNIBO) Gillespie’s SSA to Integrate DES and MABS 2015-05-15 MABS 20 / 22

21. References
References I
Rob Allan.
Survey of agent based modelling and simulation tools.
Technical Report DL-TR-2010-007, Computational Science and Engineering Department,
October 2010.
Michael A. Gibson and Jehoshua Bruck.
Efficient exact stochastic simulation of chemical systems with many species and many
channels.
J. Phys. Chem. A, 104:1876–1889, 2000.
Daniel T. Gillespie.
Exact stochastic simulation of coupled crfreactions.
The Journal of Physical Chemistry, 81(25):2340–2361, December 1977.
Ruth Meyer.
Event-driven multi-agent simulation.
In Electronic Proceedings of the Fifteenth International Workshop on Multi-Agent-Based
Simulation, 2014.
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22. References
References II
Sara Montagna, Danilo Pianini, and Mirko Viroli.
A model for drosophila melanogaster development from a single cell to stripe pattern
formation.
In Proceedings of the 27th Annual ACM Symposium on Applied Computing, SAC ’12,
pages 1406–1412, New York, NY, USA, 2012. ACM.
Andrea Omicini.
Event-based vs. multi-agent systems: Towards a unified conceptual framework.
In 2015 19th IEEE International Conference on Computer Supported Cooperative Work in
Design (CSCWD2015). IEEE Computer Society, May 2015.
Danilo Pianini, Mirko Viroli, and Jacob Beal.
Protelis: Practical aggregate programming.
In 30th Symposium On Applied Computing (SAC 2015). ACM, 2015.
Alexander Slepoy, Aidan P. Thompson, and Steven J. Plimpton.
A constant-time kinetic monte carlo algorithm for simulation of large biochemical reaction
networks.
The Journal of Chemical Physics, 128(20):205101, 2008.
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