Holonic Multiagent Multilevel Simulation: Application to Real-Time Pedestrian Simulation in Urban Environment

Holonic Multiagent Multilevel Simulation Application to Real-time Pedestrians Simulation in Urban Environment Nicolas Gaud, Franck Gechter, Stephane´ Galland, Abderrafiaaˆ Koukam Multiagent Systems and Applications Group, Systems and Transport Laboratory, University of Technology of Belfort-Montbeliard,´ France http://set.utbm.fr/info Abstract self-similar structures composed of holons as substructures. They are neither parts nor wholes in an absolute sense. The Holonic Multi-Agent Systems (HMAS) are a con- organizational structure defined by holons, called holarchy, venient and relevant way to analyze, model and allows the modeling at several granularity levels. Each level simulate complex and open systems. Accurately corresponds to a group of interacting holons. simulate in real-time complex systems, where a Since we consider several individuals and their relation- great number of entities interact, requires extensive ships, the complexity of the system is increased. One issue in computational resources and often distribution of the real-time simulation of complex systems is to allow mul- the simulation over various computers. A possi- tilevel simulation. This type of simulation aims at dynami- ble solution to these issues is multilevel simulation. cally adapting the level of the entity behaviors (microscopic, This kind of simulation aims at dynamically adapt- macroscopic) while being as faithful as possible to the sim- ing the level of entities’ behaviors (microscopic, ulated model. We propose a holonic organizational multi- macroscopic) while being as faithful as possible to level model for real-time simulation of complex systems by the simulated model. We propose a holonic organi- exploiting the hierarchical and distributed properties of the zational multilevel model for real-time simulation holarchies. To fully exploit this model, we estimate the de- of complex systems by exploiting the hierarchical viation of simulation accuracy between two adjacent levels and distributed properties of the holarchies. To through physics-based indicators. These indicators will then fully exploit this model, we estimate the deviation allow us to dynamically determine the most adapted level for of simulation accuracy between two adjacent lev- each entity of the application to maintain the best compromise els through physics-based indicators. These indi- between simulation accuracy and available resources. cators will then allow us to dynamically determine After a short review of related works, this paper will intro- the most suitable level for each entity in the applica- duce our holonic organizational model. Then we will detail tion to maintain the best compromise between sim- our physics-based indicators and how to integrate them into ulation accuracy and available resources. Finally a a multilevel model. Finally a 3D real-time multilevel simu- 3D real-time multilevel simulation of pedestrians is lation of pedestrians is presented as well as a discussion of presented as well as a discussion of experimental experimental results. results. 2 Related works 1 Introduction A great number of works dedicated to multilevel simu- A large complex system contains a great number of inter- lation have already been proposed in many scientific do- acting entities. Their components are themselves complex mains : social simulation [Troitzsch, 1996], virtual ur- (nested relationship) and the boundaries of the system are ban simulation [Donikian, 1997], robotics [Pettinaro et difficult to determine. Some behaviors and patterns emerge al., 2003], real-time multilevel simulation platforms [Kim, as a result of interactions between elements [Simon, 1996; 2001]. However, multilevel modeling works are mainly con- Holland, 1995; Rodriguez, 2005]. Exhibiting all these char- centrated on Computer-Aided Design (CAD) optimizations acteristics, the urban environments can thus be considered as [Schwabacher, 1998]. Besides the major part of existent mul- complex systems. In recent years, scientific community has tilevel models work with a fixed number of levels, usually seen an great number of research works dedicated to com- two : microscopic and macroscopic. Moreover the level at plex systems. Multiagent systems are emerging as one of the which an entity is simulated is usually fixed, determined a most adapted tools for analysis, modeling and simulation of priori by the designer according to its experience and exper- this kind of systems. The holonic paradigm [Koestler, 1967] imental results of previous simulations. This point of view and its application to multiagent systems have proven to be is shared by [Ghosh, 1986] who proposes the first dynamical an effective solution to model complex systems [Tecchia et multilevel simulation. His approach is based on hierachical al., 2001; Ulieru and Geras, 2002]. Holons are defined as models from an abstract level to a more concrete one. IJCAI-07 1275 As soon as we consider a highly detailed simulation of So as to maintain this framework as generic as possible, several individuals and their relationships, the complexity of [Rodriguez, 2005] distinguishes two aspects that overlap in the system and the associated computational costs increase. a holon. The first is directly related to the holonic character The multilevel simulation appears as one of the best solu- of the entity, i.e. a holon (super-holon) is composed of other tions. [Brogan and Hodgins, 2002] and [Musse and Thal- holons (sub-holons or members). This aspect is common to mann, 2001] propose multilevel behavioral models for mo- every holon, thus called holonic aspect. And the second is bile entities in virtual environments or situated environments, related to the problem(s) the members are trying to solve, and where various behaviors are attached to virtual characters. thus specific to the application or domain of application. In the multiagent domain, works dealing with multilevel A super-holon is an entity in its own right, but it is com- problems mainly focus on the study of emergent phenom- posed by its members. Then, we need to consider how mem- ena with various approaches : mathematical, biological, or bers organize and manage the super-holon. This constitutes a purely multiagent way. MAS models for emergence usually the first aspect of the holonic framework. To describe this deal with multilevel emergent structures (also called multi- aspect, [Rodriguez, 2005] define a particular organization ple emergence) [Heylighen, 1989] and focus on the detec- called Holonic Organization. This organization represents a tion and the recognition of behavioral patterns [Beurier et al., moderated group [Gerber et al., 1999] in terms of roles and 2003]. Multiagent-based simulations (MABS) often lead to their interactions. It defines three main roles corresponding to the emergence of local groups of entities [Servat et al., 1998], the status of a member inside a super-holon. The Head role but provide no means of manipulating them. Giving a full players are the representatives or moderators of the group, sense to multiagent simulations would certainly imply the dy- and a part of the visible interface. For the represented mem- namic creation of agent’s groups, but also their agentification bers two different roles have been defined. The Part role to deal with specific behaviors at each level. [Van Aeken, represents members belonging to only one super-holon. The 1999] introduces the notion of minimal multiagent system to Multi-Part role is played by sub-holons shared by more than study the dynamics of multiagent systems. This approach is one super-holon. In this approach, every super-holon must based on the creation of a composed agent for each couple of contain at least one instance of the Holonic Organization. Ev- atomic agent that could be merged. Even if its model remain ery sub-holon must play at least one role of this organization relatevily abstract and difficult to apply to real applications, to define its status in the super-holon composition. we can consider that with HMAS we adopt an equivalent ap- Super-holons are created with an objective and to perform proach. But the modularity and the reusability of the holonic certain tasks. To achieve these goals/tasks, members must organizational model (cf. section 3.1) allow us to overcome interact and coordinate their actions. The framework also of- Van Aeken’s model drawbacks. Our approach consists in dy- fers means to model this second aspect of the super-holons. namically grouping agent and creating new level, and also These goal-dependent interactions are modeled using organi- determining the deviation of simulation accuracy between ad- zations, called Internal Organizations, since they are specific jacent levels through physic-inspired indicators. The aims of to each holon and its goals/tasks. The behaviors and interac- these indicators is to dynamically detect when it’s necessary tions of the members can thus be described independently of to switch between simulation levels. their roles as a component of the super-holon. The set of internal organizations can be dynamically updated to describe 3 An holonic multiagent approach for additional behaviors. The only strictly required organization multilevel simulation is the Holonic organization that describes member’s status in the super-holon. At the holon level, organizations are instan- 3.1 An holonic organizational model tiated into groups. Figure 5 depicts an example of such an A holon can be seen, depending on the level of observation, instanciation. Notation g1: Scheduling denotes

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