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

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In multi-agent based simulations, providing various and consistent behaviors for the agents is an important issue to produce realistic and valid results. However, it is difficult for the simulations users to manage simultaneously these two elements, especially when the exact influence of each behaviorial parameter remains unknown. We propose in this paper a generic model designed to deal with this issue: easily generate various and consistent behaviors for the agents. The behaviors are described using a normative approach, which allows increasing the variety by introducing violations. The generation engine controls the determinism of the creation process, and a mechanism based on unsupervised learning allows managing the behaviors consistency. The model has been applied to traffic simulation with the driving simulation software used at Renault, SCANeR 2, and experimental results are presented to demonstrate its validity.

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

  1. 1. Automated generation of various andconsistent populations in multi-agent simulations Benoit Lacroix Philippe Mathieu lacroix.benoit@gmail.com philippe.mathieu@univ-lille1.fr University of Lille Computer Science Dept. LIFL (UMR CNRS 8022) Practical Applications of Agents and Multiagent Systems 2012 (PAAMS’12)
  2. 2. 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 populationsBenoit Lacroix and Philippe Mathieu Automated generation of various and consistentUniversity of Lille populations in multi-agent simulations PAAMS 2012 2
  3. 3. Outline1. Behavioral differentiation model2. Automated configuration of the model3. Generation of agents populations4. Experimental evaluation and resultsBenoit Lacroix and Philippe Mathieu Automated generation of various and consistentUniversity of Lille populations in multi-agent simulations PAAMS 2012 3
  4. 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)Benoit Lacroix and Philippe Mathieu Automated generation of various and consistentUniversity of Lille populations in multi-agent simulations PAAMS 2012 4
  5. 5. Parameters Parameter  Finite definition domain  Default value  Probability distribution over the definition domain  Reference parameter  Distance function Example : « normal maximal speed » of a vehicle maximal speed normal maximal speedBenoit Lacroix and Philippe Mathieu Automated generation of various and consistentUniversity of Lille populations in multi-agent simulations PAAMS 2012 5
  6. 6. 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 timeBenoit Lacroix and Philippe Mathieu Automated generation of various and consistentUniversity of Lille populations in multi-agent simulations PAAMS 2012 6
  7. 7. 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 » “normal” norm  Belongs to the « normal » norm safety time:  Two parameters [1.5,2.5] seconds  Maximal speed: 126 km/h maximal speed:  Safety time: 1,8 seconds [110,130] km/hBenoit Lacroix and Philippe Mathieu Automated generation of various and consistentUniversity of Lille populations in multi-agent simulations PAAMS 2012 7
  8. 8. 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 estimationBenoit Lacroix and Philippe Mathieu Automated generation of various and consistentUniversity of Lille populations in multi-agent simulations PAAMS 2012 8
  9. 9. 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 valuesBenoit Lacroix and Philippe Mathieu Automated generation of various and consistentUniversity of Lille populations in multi-agent simulations PAAMS 2012 9
  10. 10. Description of the algorithm1. Train the neural network K  Rectangular topology, (d+1)² neurons, with d the size of the inputs2. 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 values3. 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 techniquesBenoit Lacroix and Philippe Mathieu Automated generation of various and consistentUniversity of Lille populations in multi-agent simulations PAAMS 2012 10
  11. 11. 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 “aggressiveBenoit Lacroix and Philippe Mathieu Automated generation of various and consistentUniversity of Lille populations in multi-agent simulations PAAMS 2012 11
  12. 12. 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)Benoit Lacroix and Philippe Mathieu Automated generation of various and consistentUniversity of Lille populations in multi-agent simulations PAAMS 2012 12
  13. 13. 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/hBenoit Lacroix and Philippe Mathieu Automated generation of various and consistentUniversity of Lille populations in multi-agent simulations PAAMS 2012 13
  14. 14. 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 agentsBenoit Lacroix and Philippe Mathieu Automated generation of various and consistentUniversity of Lille populations in multi-agent simulations PAAMS 2012 14
  15. 15. 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 configurationBenoit Lacroix and Philippe Mathieu Automated generation of various and consistentUniversity of Lille populations in multi-agent simulations PAAMS 2012 15
  16. 16. Benoit Lacroix and Philippe Mathieu Automated generation of various and consistentUniversity of Lille populations in multi-agent simulations PAAMS 2012 16
  17. 17. 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 populationsBenoit Lacroix and Philippe Mathieu Automated generation of various and consistentUniversity of Lille populations in multi-agent simulations PAAMS 2012 17
  18. 18. 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 populationBenoit Lacroix and Philippe Mathieu Automated generation of various and consistentUniversity of Lille populations in multi-agent simulations PAAMS 2012 18
  19. 19. Results (2/2) 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”Benoit Lacroix and Philippe Mathieu Automated generation of various and consistentUniversity of Lille populations in multi-agent simulations PAAMS 2012 19
  20. 20. 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 improvementBenoit Lacroix and Philippe Mathieu Automated generation of various and consistentUniversity of Lille populations in multi-agent simulations PAAMS 2012 20
  21. 21. Thank you for your attentionBenoit Lacroix and Philippe Mathieu Automated generation of various and consistentUniversity of Lille populations in multi-agent simulations PAAMS 2012 21

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