1. Techniques for Analysis and Calibration of Multi- Agent Simulations Manuel Fehler Franziska Klügl Frank Puppe Universität Würzburg Lehrstuhl für Künstliche Intelligenz und Angewandte Informatik

2. Outline Motivation and Problem Statement Society Calibration Problem Standard Solutions White Box Calibration Techniques Discussion

3. Motivation and Problem Statement

4. Multi-agent society simulation General starting point We want to understand and/or design societies by simulating them using multi agent simulation Multi-Agent Simulation Allows explicit representation of agents and societies from original system in micro model To learn from a society simulation model it must be valid in terms of: model structure input parameter configuration controlling the structure Calibration of society MASim Model calibration means tuning the parameters so that some desired (global) society goal(s) or behavior(s) are achieved Validity of the current parameter configuration for a society behavior must be evaluated by a goal function Value of goal function for one parameter configuration is computed by running the simulation

5. Society Calibration Problem Calibrating multi agent society models is especially hard because: Multi-agent society models often use much more extensive sets of input parameters than other more restricted forms of modeling Model parameters influencing the simulation on a local or agent level have to be set in a way that a certain global society goal or behavior is reached  multi-level calibration problem  society calibration problem

6. Standard Black Box Calibration Overview: Society simulation model as black box Simulation computes some function, not explicitly written down Try to obtain approximate relationship between input parameters and simulation output to determine „optimal“ input setting Examples: Simulated Annealing, Genetic Algorithms Advantages: Generally applicable to any simulation model Disadvantages: Calibration of larger society simulation models possibly too complex to tackle: Large parameter configuration search spaces Complex parameter relationships on different society levels Multi-agent simulations very computationally expensive

7. White Box Calibration Approach Explicitly use model knowledge to enhance calibration process Knowledge about structural model properties Knowledge about inter-parameter dependencies Goal of white box approach: Reduce parameter configuration search space Reduce complexity of parameter relationships  Faster convergence of black box search methods Reduce computational cost of running multi-agent society simulation  Allow to test more parameter configuration search space points in same amount of time

8. Simple bee hive example used for illustration Simulation of major activities in a bee hive a)Foster bee brood: Keep the brood warm Keep the brood fed up b)Forage a patch environment around the hive for nectar Find good nectar sources Communicate to exchange information about source positions Nectar New bees

9. Starting Points for White Box Calibration Model decompositon techniques Model abstraction techniques Proposed techniques generally applicable to any multi- agent simulation fulfilling requirements for a technique

10. Model Decomposition Techniques General Idea: Break MASim into smaller submodels, that can be treated as individual models for calibration Merge calibrated submodels afterwards Dimensions of model decomposition: Decomposition based on: functional model units distributed problem solving in societies behavioral agent models temporal phases and situations

11. Model Decomposition Techniques 2 Decomposition based on functional model units Identify mostly independent functional units and calibrate them separately Techniques: Independent macro model parts as functional unit

12. Model Decomposition Techniques 2 Functional decomposition Identify mostly independent functional units and calibrate them separately Techniques: Independent macro model parts as functional unit Non-agent environment as functional unit Example

13. Model Decomposition Techniques 2 Functional decomposition Identify mostly independent functional units and calibrate them separately Techniques: Independent macro model parts as functional unit Non-agent environment as functional unit Groups of agents as functional units Example

14. Model Decomposition Techniques 2 Functional decomposition Identify mostly independent functional units and calibrate them separately Techniques: Independent macro model parts as functional unit Non-agent environment as functional unit Groups of agents as functional units Individual agents as functional units Example

15. Model Decomposition Techniques 3 Decomposition based on distributed problem solving property Decompose global problem solved by society into subproblem hierarchy Calibrate mostly independent subproblems individually Merge submodels and refine calibration Simple Example from Biology

16. Model Decomposition Techniques 4 Decomposition based on behavioral agent models Classify parameters based on their relevance to different possible agent behaviors Identify individual goal functions for each behavior class Apply optimization with fewer parameters for each goal Simple Example

17. Model Decomposition Techniques 5 Decomposition based on situations and temporal phases Identify independent temporal phases or situations of mostly independent behavior during one simulation run  not all behavior occurs at the same time Separate calibration of the simulation during those phases Reduce inner simulation time for one simulation run Reduce parameter space for one temporal phase If one phase creates precondition for another phase  Submodel calibration ordered by phases Simple Example

18. Model Abstraction Techniques General Idea: Enable faster computation and easier model analysis by abstracting model aspects Techniques: Abstraction by aggregation of functional groups Calibrate individual group agents  replace individual agents on group by one agent representing whole group Calibrate group behavior  constraints for individual group agents

19. Model Abstraction Techniques General Idea: Enable faster computation and easier model analysis by abstracting model aspects Techniques: Abstraction by aggregation of functional groups Abstraction reducing heterogeneity Homogeneous environments or agent abilities may allow to learn initial parameter configurations for full model calibration

20. Model Abstraction Techniques General Idea: Enable faster computation and easier model analysis by abstracting model aspects Techniques: Abstraction by aggregation of functional groups Abstraction reducing heterogeneity Abstraction at model scale Calibrate model of reduced scale (environment, agent numbers) Scaling relationships must be known

21. Model Abstraction Techniques General Idea: Enable faster computation and easier model analysis by abstracting model aspects Techniques: Abstraction by aggregation of functional groups Abstraction reducing heterogeneity Abstraction at model scale Reduction of mass agent systems for easier analysis Mass agent systems solve problems by solving similar smaller subproblems very often Calibration of individual agent for smaller subproblem can solve mass system problem

22. Model Abstraction Techniques General Idea: Enable faster computation and easier model analysis by abstracting model aspects Techniques: Abstraction by aggregation of functional groups Abstraction reducing heterogeneity Abstraction at model scale Reduction of mass agent systems for easier analysis Implementation optimization techniques Abstraction of deterministic agent actions Meta models for calibrated macro model parts

23. Sketch of White Box Calibration Method

24. Sketch of a White Box Calibration Method 1.Calibrate problem setting (e. g. calibrate non agent environment) 2.Temporal and situation based decomposition May be repeated recursively 3.Goal and behavior based decomposition of submodels After successful decomposition Goto 1 4.Model analysis Reduce mass agent systems in submodels for easier analysis Generate constraints by calibration of abstracted models 5.Identify goal functions for each submodel and calibrate 6.Merge submodels and calibrate linking parameters Use abstracted models of calibrated submodels

25. Sketch of a White Box Calibration Method 1.Calibrate problem setting (e. g. calibrate non agent environment) 2.Temporal and situation based decomposition May be repeated recursively 3.Goal and behavior based decomposition of submodels After successful decomposition Goto 1 4.Model analysis Reduce mass agent systems in submodels for easier analysis Generate constraints by calibration of abstracted models 5.Identify goal functions for each submodel and calibrate 6.Merge submodels and calibrate linking parameters Use abstracted models of calibrated submodels

26. Sketch of a White Box Calibration Method 1.Calibrate problem setting (e. g. calibrate non agent environment) 2.Temporal and situation based decomposition May be repeated recursively 3.Goal and behavior based decomposition of submodels After successful decomposition Goto 1 4.Model analysis Reduce mass agent systems in submodels for easier analysis Generate constraints by calibration of abstracted models 5.Identify goal functions for each submodel and calibrate 6.Merge submodels and calibrate linking parameters Use abstracted models of calibrated submodels

27. Sketch of a White Box Calibration Method 1.Calibrate problem setting (e. g. calibrate non agent environment) 2.Temporal and situation based decomposition May be repeated recursively 3.Goal and behavior based decomposition of submodels After successful decomposition Goto 1 4.Model analysis Reduce mass agent systems in submodels for easier analysis Generate constraints by calibration of abstracted models 5.Identify goal functions for each submodel and calibrate 6.Merge submodels and calibrate linking parameters Use abstracted models of calibrated submodels

28. Sketch of a White Box Calibration Method 1.Calibrate problem setting (e. g. calibrate non agent environment) 2.Temporal and situation based decomposition May be repeated recursively 3.Goal and behavior based decomposition of submodels After successful decomposition Goto 1 4.Model analysis Reduce mass agent systems in submodels for easier analysis Generate constraints by calibration of abstracted models 5.Identify goal functions for each submodel and calibrate 6.Merge submodels and calibrate linking parameters Use abstracted models of calibrated submodels

29. Sketch of a White Box Calibration Method 1.Calibrate problem setting (e. g. calibrate non agent environment) 2.Temporal and situation based decomposition May be repeated recursively 3.Goal and behavior based decomposition of submodels After successful decomposition Goto 1 4.Model analysis Reduce mass agent systems in submodels for easier analysis Generate constraints by calibration of abstracted models 5.Identify goal functions for each submodel and calibrate 6.Merge submodels and calibrate linking parameters Use abstracted models of calibrated submodels

30. Discussion Summary Calibration of multi agent societies is a very hard problem in terms of Complexity of inter parameter dependencies Size of parameter configuration search space Computational complexity of running a simulation model Black box calibration methods fail in calibration of complex multi-agent society simulation models White box calibration methods can exploit structural modularity inherent to society MASims to simplify the calibration problem White box techniques need to be applied with great care in order to produce valid and usable results Further Work Bottom up calibration vs. top down calibration Calibration for design