MAS 2011 Lecture 1

7/28/2019 MAS 2011 Lecture 1

1/42

Agent-Based Simulation


Principles of simulation and the Multi-Agent approach

Federico Pecora

School of Science and Technologyrebro [email protected]

Federico Pecora Agent-Based Simulation Part 1 1 / 37
http://localhost/var/www/apps/conversion/tmp/scratch_8/[email protected]://localhost/var/www/apps/conversion/tmp/scratch_8/[email protected]://find/http://goback/

7/28/2019 MAS 2011 Lecture 1

2/42


Outline

1 Goals and structure of this course

2 What is simulation?

3 The Multi-Agent approach

4 Principles of simulation design and implementation

http://find/

7/28/2019 MAS 2011 Lecture 1

3/42


Goals and structure of this course

Outline





http://find/

7/28/2019 MAS 2011 Lecture 1

4/42



Goals of this course

Learn about Multi-Agent simulation and its advantages

Understand concepts of simulation engineering

Learn how to perform a Multi-Agent simulation study

Experience practical development in Multi-Agent simulation

environments

http://find/

7/28/2019 MAS 2011 Lecture 1

5/42



Structure of the course


Cellular Automata

Lab assignment 1 (Golly)

Behavioral Models for Coordinated Motion

Lab assignment 2 (Crossroads)

Lab assignment 3 (WildLife)

Swarm Intelligence and Ant Colony OptimizationAdvanced topics, Review, Q&A

http://find/

7/28/2019 MAS 2011 Lecture 1

6/42



Evaluation criteria and exam

This course is composed of labs and theory

For labs, you get either G (passed) or U (failed)

For theory, you are graded in the system you are registered with

(U,3,4,5), (U,G,VG) or (F,E,D,C,B,A)Theory is assessed through an exam

80 points in total

40 points required to pass

the 80 points are mapped to grades as follows:

(U,3,4,5) ([0,39], [40,54], [55,70], [71,80])(U,G,VG) ([0,39], [40,69], [70,80])(F,E,D,C,B,A)

([0,39], [40,47], [48,55], [56,63], [64,71], [72,80])


A t B d Si l ti
http://find/

7/28/2019 MAS 2011 Lecture 1

7/42



Labs

Labs are semi-supervised by Federico Pecora and Jonas Ullberg

They all require programming

in Python or Perl for Cellular Automata

in C++ for Behavioral Models for Coordinated Motion

All your programs will be developed within a pre-existing

framework or API

Labs are individual

you must submit all labs by email to teacher and TA within two

weeks from the completion of your exam

you are strongly advised to submit every assignment before the

next one starts, and the last assignment before you take the exam


Agent Based Sim lation
http://find/

7/28/2019 MAS 2011 Lecture 1

8/42



Theoretical exam

The theoretical exam is divided into four sections

the first gives a maximum of 20 points: you must reply true/false to

theoretical questions spanning the entire course

the other three are relevant to selcted topics and require a morein-depth answer

e.g., Swarm intelligence (20 points), Behavioral models (10

points), Cellular automata (20 points)

questions can be composed of sub-questions

You have 5 hours to complete the examduring which I will come by to answer questions approximately

one hour after start


Agent Based Simulation
http://find/

7/28/2019 MAS 2011 Lecture 1

9/42



Theoretical exam: whats important?

The theoretical exam is based on the lectures

The material is the lecture slides, your notes, and any papers that

the are handed out and/or referenced in the slides. . . plus the experience you have gained from the labs!

Everything in the course is important for the exam

this course has a relatively limited scope, but in-depth knowledge

is expected to pass the exam

you are always absolutely free to come to my office if anything isunclear!


http://find/

7/28/2019 MAS 2011 Lecture 1

10/42


What is simulation?

Outline






http://find/

7/28/2019 MAS 2011 Lecture 1

11/42


What is simulation?

What is simulation?

We want to examine a part of the real world which

is not accessible

is difficult to experiment with

evolves over loo long or too short time scales

does not exist anymore/yet

Modeling as a tool for understanding, verifying hypotheses,

predicting, . . .


http://find/

7/28/2019 MAS 2011 Lecture 1

12/42


What is simulation?

What is simulation?

We want to examine a part of the real world which

is not accessible

is difficult to experiment with

evolves over loo long or too short time scales

does not exist anymore/yet

Modeling as a tool for understanding, verifying hypotheses,

predicting, . . .


http://find/

7/28/2019 MAS 2011 Lecture 1

13/42

g

What is simulation?

What is simulation?

System: an actual or theoretical system in which distinct entities

interact

Simulation: designing a model of a system, executing the model on

a computer, and analyzing the execution output

Model: a reproduction of the system at an appropriate level of

granularity


http://find/

7/28/2019 MAS 2011 Lecture 1

14/42

What is simulation?

Model design, execution and analysis

Three sub-fields in simulation:

Model

design

Model

execution

Execution

analysis

Model design

Model execution

Execution analysis


http://find/

7/28/2019 MAS 2011 Lecture 1

15/42

What is simulation?

Types of simulation (1)

Simulation approaches can be categorized according to various

criteria, e.g., dicrete vs. continuous time:

Discrete event simulation

Model: finite state machines, queues, petri nets, . . . ; Execution:

read the queue of events and trigger new events as each event is

processed; Applications: logic-test and fault-tree simulations.

Continuous dynamic simulation

Model: partial or ordinary differential equations; Execution: periodic

numerical resolution of equations; Applications: flight simulators,

electrical circuits.


http://find/

7/28/2019 MAS 2011 Lecture 1

16/42

What is simulation?



criteria, e.g., stochastic vs. deterministic:

Monte-Carlo simulation

Model: can be almost anything; Execution: generate inputsrandomly from the domain, and perform a deterministic computation

on them; Applications: largely used in computational physics and

engineering (e.g., designing heat shields and aerodynamic forms).

Cellular automataModel: designing heat shields and aerodynamic for; Execution:

periodic application of rules which determine state of a cell as a

function of neighboring cells; Applications: see next lecture!


http://goforward/http://find/http://goback/

7/28/2019 MAS 2011 Lecture 1

17/42

What is simulation?



criteria, e.g., macrosimulation vs. microsimulation:

Macrosimulation

Model: complete system is modeled as one monolithic entity

populations are averaged togetherand the model attempts tosimulate changes in these averaged characteristics for the whole

population; Execution: periodically update the state variables

describing the system; Applications: naturally applies to systemsthat can be modeled centrally and and in which the dynamics are

dominated by physical laws.


http://find/

7/28/2019 MAS 2011 Lecture 1

18/42

What is simulation?




MicrosimulationModel: explicitly attempts to model specific behaviors of specificindividuals; Execution: periodic interaction/communication betweenindividuals; Applications: most appropriate for domains

characterized by a high degree of localization and distribution and

dominated by discrete decision.

Multi-Agent simulation is a special form of Microsimulation


http://find/

7/28/2019 MAS 2011 Lecture 1

19/42

What is simulation?




MicrosimulationModel: explicitly attempts to model specific behaviors of specificindividuals; Execution: periodic interaction/communication betweenindividuals; Applications: most appropriate for domains

characterized by a high degree of localization and distribution and

dominated by discrete decision.

Multi-Agent simulation is a special form of Microsimulation


http://find/

7/28/2019 MAS 2011 Lecture 1

20/42

What is simulation?

Time advance function

Whatever the model, execution consists in steping through time while

updating the variables in the model

There are many ways to step through time

In continuous-time models (e.g., differential equations) time steps

can be reduced idefinitely

In discrete-time models time is quantized somehow

leap through time using event scheduling

employ small time increments using time slicingsimulate the program on a massively parallel computer


http://find/

7/28/2019 MAS 2011 Lecture 1

21/42

What is simulation?

Goal of simulation

Why use simulation?

There are at least the following two reasons

Prediction: model should produce quantitatively correct

predictions depending on its input values

Explanation: qualitatively significant results are sufficient for

understanding the reaction of the system to input

valuesThe goal of virtually all forms of simulation falls within this

spectrum (including Multi-Agent simulation)



Th M lti A t h
http://find/

7/28/2019 MAS 2011 Lecture 1

22/42

The Multi-Agent approach

Outline







The Multi Agent approach
http://find/

7/28/2019 MAS 2011 Lecture 1

23/42


Some examples

Ant colony optimization

Physics simulation

Biological system simulation

Flocking/Herding

Cellular automata

. . . the real thing



http://videos/Starlings_Flocking.avihttp://videos/computational-CA.avihttp://videos/the_Hilton_turns_into_birds.avihttp://videos/Artificial_life_and_multiagent_system.avihttp://videos/temperature-CA.avihttp://videos/Ant_Colony_Optimization_of_truss.avihttp://find/

7/28/2019 MAS 2011 Lecture 1

24/42


Multi-Agent simulation

Model concept based on Multi-Agent Systems

Fundamental entities can sense the environment and act in the

environment (environment = other agents + simulated world)

Modeling a real Multi-Agent System as a Multi-Agent System

in the model, i.e., using the concept of multi-agent systems in con-

ceptualization, specification and implementation of the model

Interaction among agents is the central point

Simulation data: emerging behavior of the agents



http://find/

7/28/2019 MAS 2011 Lecture 1

25/42


Why Multi-Agent simulation?

A paradigm that allows appropriate modeling capabilities in a

number of imporant disciplines

Social science, Biology

Modeling socio-technical systems and test environments for

agent-based software

Allows to simulate systems that are particularly difficult to treat

with traditional approaches

Emergent phenomena, models with variable structure

Can afford more detail in models

more realism and micro-validity

Provides an intuitive way of modeling

Facilitates communication with other fields and enables more

researchers to use simulation



http://find/

7/28/2019 MAS 2011 Lecture 1

26/42


Charateristics of Multi-Agent simulation

Agents are autonomous entities that react to environmental

change and proactively change their behavior

Typically one agent

Perception and action is localVariable structure models

models that entail in their description the possibility to change

their own structure, i.e., their constitutive components as well as

the relations that exist among them

Flexible interactions between agents

Non-linear feedback loops

Adaptive agents



http://find/

7/28/2019 MAS 2011 Lecture 1

27/42


Simulating Multi-Agent Systems

Multi-Agent Models are used as substitutes for another system,

the original multi-agent system

Multi-Agent Simulations mostly use virtual time

Simulated Multi-Agent Systems live a in a simulated

environment

social space

virtual 2D/3D space

= time and environment are controllable by the modeler



http://find/

7/28/2019 MAS 2011 Lecture 1

28/42

g pp

Applications of Multi-Agent simulation

Biological systems: active and heterogeneous entities exert influenceson their local environment reacting on local stimuli

Traffic simulation: microscopic models represent sophisticated

intelligent entities (simulated drivers)

Social science simulation: artificial societies are created to testscientific hypotheses (e.g., gossip)

Industrial simulation: there are non-artificial (e.g., human) agents

involved, or where parts of the system contribute to an

overall difficult to model emerging behavior

Simulation for entertainment: large multitudes of agents (e.g.,

animals, armies, . . . ) move in 3D space (see Lord of the

Rings)

Testbeds: for complex control systems



http://find/

7/28/2019 MAS 2011 Lecture 1

29/42

Multi-Agent simulation vs. Macrosimulation

Pitfalls of Macrosimulation

Assumes homogeneous space and population

No representation of the individual and its locality, i.e., no

conditional behavior, no adaptive behavior, no flexible interaction

Can only observe the system as a whole, not its parts

Advantages of Macrosimulation

Differential equations: a well understood, established

mathematical framework

Easy to document

Low number of parameters, global input-output behaviour

Simulation experiments can be very fast



http://find/

7/28/2019 MAS 2011 Lecture 1

30/42


Pitfalls of Multi-Agent simulation

Development of complex models can be very costly

Difficult to determine minimal model

Established formalism is missing, difficult to document

Calibration problem

finding the best parameter setting for a model (given a

structurally valid model)

Sensitivity problemeven small changes may have a large effect



http://find/

7/28/2019 MAS 2011 Lecture 1

31/42


Advantages of Multi-Agent simulation

Can deal with multi-agent systems directly:

real agent simulated agent

Facilitates structural validation

Elegant treatment of variable structures

Allows to model adaptation and evolution

Easy to model heterogeneous space and population

Provides different levels of observation



http://find/

7/28/2019 MAS 2011 Lecture 1

32/42

Bevaior- vs. Goal-oriented agent models

Agents in the study can be modeled in two ways

Behavior-oriented modeling: agents are described by modeling their

behaviors

Goal-oriented modeling: agents are capable of planning and the

modeler described their goal

Choice of modeling startegy strongly depends on application

context



http://goforward/http://find/http://goback/

7/28/2019 MAS 2011 Lecture 1

33/42

Bevaior- vs. Goal-oriented agent models

Agents in the study can be modeled in two ways

Behavior-oriented modeling: agents are described by modeling their

behaviors

Goal-oriented modeling: agents are capable of planning and the

modeler described their goal

Choice of modeling startegy strongly depends on application

context



http://find/

7/28/2019 MAS 2011 Lecture 1

34/42

Behavior-oriented models

The modeler describes agent status and dynamics

Example formalisms: activity graphs, crisp/fuzzy rules,

constraints, . . .

Reactions to perceptions/status changes are defined by themodeler

Can easily accommodate Reinforcement Learning and

Evolutionary Concepts

The agents goal(s) are are treated implicitlyVery intuitive mapping with simple biological systems (e.g.,

insects)



http://find/http://goback/

7/28/2019 MAS 2011 Lecture 1

35/42

Goal-oriented models

The modeler identifies goals of the agents

Agents select a goal and execute actions as a consequence

Reactions are not predefined, but goal dependent

Explicit treatment of goals in the agent behaviour, but

execution of goal-dependant actions can be error-prone

leads to significantly more complex model (see

Belief-Desire-Intention agent simulations)

We do not deal with goal-oriented models in this course



Principles of simulation design and implementation
http://find/

7/28/2019 MAS 2011 Lecture 1

36/42

Outline







http://find/

7/28/2019 MAS 2011 Lecture 1

37/42

Main steps in a simulation study [Klgl, 2004]

1 Defining the question addressed by the model

2 Conceptualizing the model

3 Specifying the model

4 Implementing the model [and the simulation environment]

5 Calibrating, bug-fixing

6 Experimentation

7 Analysis of the results



http://find/

7/28/2019 MAS 2011 Lecture 1

38/42

Model validation and verification

Validation: crucial for guaranteeing that output of simulation will say

something meaningful!

Verification: making sure the model is correctly translated from one

representation to another (e.g., from concept to

specification)

Concept

model

Specified

model

Implemented

model

Simulation

output data

Original system

Validation Validation Validation Validation

Verification

Verification

Verificatio

n

Validation and verification between all steps!



http://find/

7/28/2019 MAS 2011 Lecture 1

39/42

Model validation and verification

Validation: crucial for guaranteeing that output of simulation will say

something meaningful!

Verification: making sure the model is correctly translated from one

representation to another (e.g., from concept to

specification)

Concept

model

Specified

model

Implemented

model

Simulation

output data

Original system

Validation Validation Validation Validation

Verification

Verification

Verificatio

n

Validation and verification between all steps!



http://find/

7/28/2019 MAS 2011 Lecture 1

40/42

Potential pitfalls

Failure to have a well-defined set of objectives at the beginning

of the simulation study

Inappropriate level of model detail

need to identify a minimal model given the objective

Treat simulation simply as programming exercise

Failure to collect appropriate data from the original system

Inappropriate simulation software

this can account for 80% of development time!

Failure to account correctly for sources of randomness

Inappropriate output data analysis



http://find/

7/28/2019 MAS 2011 Lecture 1

41/42


Thank you!



References
http://find/

7/28/2019 MAS 2011 Lecture 1

42/42

References

Klgl, F. (2004).

Multi-Agent Simulation.

Lecture at the Sixth European Agents Systems Summer School, Liverpool.

http://find/

Documents

MAS 2011 Lecture 1