automated generation of various and consistent populations in multi-agent simulations

Automated generation of various and

consistent populations in multi-agent

simulations

Benoit Lacroix

[email protected]

University of Lille

Computer Science Dept.

LIFL (UMR CNRS 8022)

Practical Applications of Agents and Multiagent Systems 2012 (PAAMS’12)

Philippe Mathieu

[email protected]

Benoit Lacroix and Philippe Mathieu

University of Lille

Automated generation of various and consistent

populations in multi-agent simulations PAAMS 2012

Context and motivations

Context

Design realistic scenarios in simulations

Introduce both various and consistent agents behaviors

Motivation

Assist the designer in the configuration tasks

Proposed approach

Based on a behavioral differentiation model

Automated configuration of the model from sample data

Automated generation of agents populations

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Outline

1. Behavioral differentiation model

2. Automated configuration of the model

3. Generation of agents populations

4. Experimental evaluation and results

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populations in multi-agent simulations PAAMS 2012 4

Behavioral differentiation model

Based on a social norm metaphor

Provide “behavioral patterns” during agents creation

Conformity control at runtime

Introduced in previous works (PAAMS’09)


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Parameters

Parameter

Finite definition domain

Default value

Probability distribution over the definition domain

Reference parameter

Distance function

Example : « normal maximal speed » of a vehicle

normal maximal speed

maximal speed


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Norms

Norm

Set of parameters

Properties

Violation rate

Maximal gap to the norm

Example : « normal » norm

« normal maximal speed » and « normal safety time »

France, highway

5%

3% normal maximal speed normal safety time


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Model Agents

Encapsulate the simulation agents

Technical constraints, allows for different processes

Model agents

Instantiate a norm

Reference norm

Set of parameters values

Example : model agent « Bob »

Belongs to the « normal » norm

Two parameters

Maximal speed: 126 km/h

Safety time: 1,8 seconds

“normal” norm

safety time:

[1.5,2.5] seconds

maximal speed:

[110,130] km/h


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Automated configuration of the model

Using sample data

Objectives

Ease the designer works

Facilitate the use of the model

Choices

Unsupervised learning

Limit configuration and user supervision

Kohonen neural networks

Distribution function estimation

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Principle of the algorithm

A neuron = a norm

Neuron weights values = norms parameters default values

For all the examples matching a neuron (i.e. in the same norm)

Maximal / minimal values

= bounds of the definition domain of the corresponding parameter

Distribution estimation

= probability distribution of the corresponding parameter

Result

Automated creation of a set of norms representing the sample data

Easy parameterization of the model

Reproduction of experimental settings based on recorded values

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Description of the algorithm

1. Train the neural network K

Rectangular topology, (d+1)² neurons, with d the size of the inputs

2. For each neuron k of K, create a norm n

n holds a parameter per dimension of the input vectors

Associate the weights of the neuron to the parameters default values

3. Classify the examples with K

For each example e, let k be the triggered neuron (norm n)

If needed, update the corresponding bounds of the domain

Add e to the distribution estimator for the corresponding parameter

Could be used with other clustering techniques

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Generation of agents populations

Objectives

Easily populate a database with agents

Specify precisely the composition of the population

Combination of profiles, time slices and generators

Profile

Reference norm

Set of characteristics

Examples

p1 of norm “normal”

p2 of norm “aggressive

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Time slice

Time slice

Set of profiles and their relative percentage in the population

Duration

Generation frequency (s-1)

Example: a time slice t1 “rush hour”

80% of profiles p1 (“normal”) and 20% of profiles p2 (“aggressive”)

Active from 7 a.m. to 9 a.m.

Generation frequency 1.0 (one agent created per second)

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Generator

Generator

Set of time slices

Function associating a position in space to an agent

Example: generator “morning traffic”

The time slice t1

A time slice t2

Active from 9 to 11 a.m. with 100% of profiles p1 and frequency 0.2

Creation at the position (0,0,0)

The “morning traffic”

A rush hour with aggressive drivers and a dense flow of 3600 veh/h

Followed by a quieter period with only normal driver and only 720 veh/h

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Generation mechanism properties

Flexible mechanism to introduce behaviors

High level definition, with low level specification

Based on the behavioral differentiation model

Automated configuration of the generators

Based on the inference mechanism

A profile per norm

Relative proportion = the proportion of matching examples

User only has to specify the position where to create the agents

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Application

Context

Commercial driving simulation software

SCANeR™ (http://scanersimulation.com)

Design studies, driving aid systems development…

Traffic simulation in SCANeR™

Based on a multi-agent architecture

Complex configuration steps

Involves manual configuration of each vehicle / parameter

Objective

Automate the simulation configuration

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Evaluation

Highway database

Recording of vehicles data

Speed, safety time

Experimental protocol

Generation and recording of a population of vehicles

Pre-configured generators: 10% cautious and 10% aggressive drivers,

80% normal ones

Norm inference and construction of new generators

Generation and recording of a second population

Comparison of the two populations

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Results (1/2)

Norm inference

From the initial population

9 norms

Generator construction

1 time slice

9 profiles (one per norm)

Proportion = relative occurrence of the norm

Generation and recording of a new population

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Comparison the clusters for each population

At most 2.3% difference on the default value,

8.3% on the domain bounds,

and 10.2% on the repartition

Similar populations

Same behavioral

characteristics

But resulting population more “careful”

Results (2/2)

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Conclusion

Automated generation of populations

Description of agents using a social norm metaphor

Inference of the behavioral model parameters

Clustering and parameters distribution estimation

Agents generators

Flexible mechanism to introduce various and consistent behaviors

Application to traffic simulation

Creation of a population statistically close to the reference

Future works

Real world data

Norms representation improvement

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Thank you for your attention

automated generation of various and consistent populations in multi-agent simulations

Technology