information (Jennings et al., 1998, cited in Klügl, 2001, p. 13), computational emergence (see Section 2.1.1), and (self-)organisation (e.g. Holland, 1998); see also the brief discussion in (Page and Kreutzer, 2005, p. 342).
in MABS models do not exist outside the model [see also the discussion by Klügl (2000, pp. 62)].
Finally, due to the inherent complexity of data analysis in agent-based models (Sanchez and Lucas, 2002, p. 117), simulation tools built on the agent metaphor may occasionally even be helpful during an agent-based model's design and analysis (Drogoul et al., 2002, pp. 10).
This thesis is focused on the process-oriented analysis and validation of MABS, but the pre-sented concepts and techniques might also be applied in agent simulation. The main distinction is the analysis objective with a scientic focus in MABS and a software-technical focus in agent simulation. The integration of the presented analysis techniques into automated assistants might as well be regarded as agent-supported simulation.
3.2.2. Components of Agent-Based Models
A MABS is a MAS in a simulated (spatial and temporal) environment that serves to represent a real system (Klügl, 2000, p. 60). Thus the main components of a MABS include (see Klügl, 2000, p. 60 and the review in Page and Kreutzer, 2005, pp. 353):
• a simulation scheduler,
• a set of simulated agents,
• an infrastructure for communication and organization,
• a (possibly spatial) environment.
These components are briey described below with one exception: It seems not sensible to elaborate on specic properties of simulated agents since these do not signicantly dier from other types of software agents described in Section 3.1. The main dierence is that simulated agents exist in simulated time and space (Meyer, 2008), which normally allows to keep their sensors and eectors simple (Klügl, 2000, p. 64). The following description is based on Klügl (2000, pp. 63) and our review in (Page and Kreutzer, 2005, pp. 354).
3.2.2.1. Scheduling in MABS
As reviewed in (Page and Kreutzer, 2005, p. 354):
Scheduling in MABS can be both time- or event-driven. For models with few complex agents, which communicate via messages, event-driven scheduling is often the better choice. Conversely, time-driven control may be preferable where models consist of large numbers of agents with similar behaviour, and where every agent is activated in every simulation cycle and similar actions are executed in a regular[...]
fashion.
Execution order of agents is an important aspect in time-driven, and to a lesser extent in event-driven scheduling strategies. While conceptually agents will act in parallel, the serialization of actions required to execute on a single processor may
introduce so-called artifacts into the model.7 The execution order of agents in time-driven models is therefore often randomized at each simulation step (Klügl, 2001, p. 157).
Davidsson (2000, p. 100) argues that event-driven scheduling contradicts the autonomy of agents, because the scheduler imposes a central control by ordering the individual actions on a global event list. Meyer (2008) rightly disagrees with this in two respects: On the one hand, a time-driven simulation scheduler must also impose a global execution order to ensure repeatable simulation results. On the other hand, MABS deals with autonomy mainly on the conceptual level and not in (distributed) implementations (see also Section 3.1.1.1).
As indicated in Section 2.2.1, the event-driven approach is more general because time-driven scheduling can be emulated and integrated by means of equidistant clock pulse events. Similarly, the analysis of event-driven models might be regarded as more general, since non-equidistant inter-event durations must be coped with (e.g. in time-weighted statistics over event-traces).
This thesis is concerned with trace-based analysis techniques for event-driven models, which are straightforwardly applied to time-driven models as well.
3.2.2.2. Communication and Organization
Two dierent modes of communication are found in MABS: Agents either communicate ex-plicitly via messages or imex-plicitly by placing objects in a common environment (Ferber, 1995, p. 13).8 An appropriate communication model should be chosen with respect to the repre-sented system, e.g. implicit communication via 'pheromones' in anthill simulations (Ferber, 1995, pp. 389). Message-based communication requires a communication infrastructure that might exhibit an own dynamic, e.g. to simulate delayed or unreliable forwarding of messages (Page and Kreutzer, 2005, p. 355).
The analysis of models with explicit communication seems less demanding than the implicit case, because message passing events can be clearly identied in the simulation trace. Therefore, we will focus on the analysis of MABS with explicit (message-based) communication in this thesis.
An important objective in MABS is to investigate the mutual inuences between individual behaviour and organizational structures, which requires an appropriate representation of these structures in the model. In some cases, organizational structures are represented implicitly in terms of the spatial model, where spatial proximity of two agents might e.g. be interpreted as 'sharing a similar culture' (e.g. Axelrod, 1995).
Agent-Group-Role Model A well-known framework for the explicit representation of organi-zational structures is the agent-group-role (AGR) model by Ferber and Gutknecht (1998). It describes an infrastructure that allows agents to dynamically found, disband, join, and leave groups in a virtual environment. Within groups, agents can play roles that represent their organizational positions, specic abilities, or responsibilities. As an example, several agents
7In particular such model-artifacts are articial causal dependencies due to the serialization of originally concurrent actions.
8see also Page and Kreutzer (2005, p. 354)
might enact the role 'professor' in the group 'University of Hamburg'. Groups and roles allow agents to reference others in an indirect or deictic (Klügl, 2000, p. 64) way, e.g. 'the professor who teaches my computer science course at the University of Hamburg'. Extensions of the AGR model towards spatial constructs (places, locations, and paths) are described by Rupert et al. (2007).
FIPA Standard Another common (but more technical) model is the communication and plat-form infrastructure dened in the FIPA9standard. Following our review in (Page and Kreutzer, 2005, p. 360):
This standard denes an agent communication language (ACL), as well as a platform architecture consisting of an agent communication channel (ACC) and two special agents called AMS (Agent Management System) and DF (Directory Facilitator) (see e.g. Rölke, 2004, pp. 87). By registering and de-registering agents with a unique identier, the AMS provides so-called white page services. The DF manages the agents' service descriptions [which are roughly comparable to roles in the AGR model] (yellow page services). The internal agent architecture is not part of the FIPA standard.
The ACL is a standardized message format for agent communication specied in (FIPA, 2002b):
A FIPA ACL message contains a number of attributes including message type (performative), sender, receiver, and content. The performative indicates the intention pursued by sending the message. It can be chosen from a set of standardized communicative acts such as request or propose (FIPA, 2002a).10 The content can be specied in an arbitrary format, but the FIPA advocates the use of certain knowledge representation languages including SL (semantic language), RDF (resource description framework), and KIF (knowledge interchange format, see FIPA, 2005).
An ACL message can include further optional attributes for self-description and communica-tion control (FIPA, 2002b): The former comprises informacommunica-tion on the language (e.g. SL) and ontology (i.e. the domain-specic terminology) of the content. The latter includes the at-tributes reply-with and in-reply-to to identify threads of related messages that were sent in reply to each other as well as conversation-id and protocol-id to identify the conversation and the protocol that a message belongs to. The FIPA species a number of standardized protocols for common interaction types (mainly auctions or negotiations, see FIPA, 2005). Due to their representation in AgentUML, a detailed description is deferred to Section 3.3.2.1.
Implicit versus Explicit Organization Organizational structures (e.g. groups and roles) and processes (e.g. interaction protocols) in MABS are either pre-dened by the modeler or emerge from local interactions during the simulation (see Ferber, 1995, p. 114 cited in Page and Kreutzer, 2005, p. 355). As in reality, a combination of both approaches is found most often:
We might e.g. pre-dene a set of basic interaction protocol classes. However, the agents' actual execution and combination of these protocols into cooperative tasks might not be predictable from the (static) specication but can only be observed at runtime.
9Foundation for Physical Intelligent Agents (FIPA, 2005)
10The idea of communicative acts is based on the speech act theory by Searle (1974), in which communication is understood as a specic form of action.
The analysis of implicit organizational patterns is challenging because (a) the patterns are often hidden in the data observed during simulation, and (b) an automated analysis is complicated by the fact that many organizational concepts cannot be straightforwardly reduced to simple quantitative measures. This topic is further discussed in Section 3.5.
3.2.2.3. Spatial Environment
A spatial environment is a central component of many MABS. In most cases, it represents a 'real' spatial topology, e.g. a landscape in an ecological model or a trac network in logistic simulation. As mentioned above, some social simulations also visualize more abstract concepts like group formation by means of the agents' spatial distribution. The presentation in this section is in particular inuenced by the view on spatial modeling described by Meyer, 2008 and implemented in our MABS framework FAMOS (Knaak, 2002; Knaak et al., 2002; Meyer, 2008; see also Section 3.4.4).
Spatial Structures Several spatial models are employed in MABS (e.g. Gilbert and Troitzsch, 1999; Meyer, 2008): A common representation is a two-dimensional grid consisting of rectan-gular cells. Other rerectan-gular (e.g. hexagons) and irrerectan-gular cell shapes (e.g. Voronoi tesselations), or higher dimensional grids are less frequently used. Grid-based models include a neighborhood relation that determines which neighboring cells an agent can reach from a certain position.
As in cellular automata, this relation is often dened homogeneously on the whole grid.
A more exible alternative are graph-based models that consist of nodes representing locations and edges representing (un)directed connections between locations (Meyer, 2001). Graphs are well suited to model heterogeneous topologies in logistics (road networks, see Page and Kreutzer, 2005, p. 357 and Meyer, 2001), telecommunications (communication networks), and abstract sociological models (social networks). Arbitrary grid-based models can be mapped to graphs by associating nodes with centers of grid cells and edges with (possibly heterogeneous) neighborhood relations (Meyer, 2001).
A less common alternative are continuous spatial models, that are e.g. used in pedestrian simulation. An example is the simulation of aircraft boarding and deplaning processes described by Matzen and Czogalla (2003).
Dynamics of the Environment The most obvious environmental dynamics result from the agents' movements. Depending on the modeled domain, dierent movement strategies are employed (see Meyer, 2008; Page and Kreutzer, 2005, p. 357): The most common are random walk as a simple exploration strategy, following gradient elds (e.g. simulated pheromone trails in ant foraging), and movement along previously planned routes.
Agents must be able to sense and modify other agents or objects in the environment. This is often constrained by an (individual) perception and action range to represent behavioral locality. Depending on the model's purpose, restrictions on spatial resources (e.g. the number of agents that 't' on a grid cell) are considered as well. The environment can exhibit an additional dynamic that is caused by environmental processes modeled in a more abstract fashion (e.g. as cellular automata). An example stated in (Page and Kreutzer, 2005, p. 357) is
the re-growth of 'sugar' resources in the well-known Sugar Scape model by Epstein and Axtell (1996)
As indicated in Section 3.1.1.1 interactions between an agent and its environment are often modeled similar to interactions between agents (e.g. using message passing). This leads to a view on the environment as a particular agent (Klügl, 2000, pp. 103), which is a common 'workaround' if agents are the only available modeling construct. It should, however, be avoided in favour of more specic means to model objects and environments (Page and Kreutzer, 2005, p. 355).
3.2.3. Comparison with other Simulation World Views
To complete the introduction to MABS, we briey compare it to related simulation world views.
The structure and content of this Section is largely adopted from (Page and Kreutzer, 2005, Ch. 11.4.4) co-written by and based on the diploma thesis (Knaak, 2002, Sec. 3.2.1) of the author. The presentation complements the treatment in (Klügl, 2001, pp. 27,45,61,84) with a stronger focus on discrete event simulation.
3.2.3.1. Event-Oriented Simulation versus MABS
The event-oriented world view mirrors the implementation of event-driven scheduling. Though this is an appropriate technical basis for MABS (see Section 3.2.2.1), the concepts of event-oriented modeling as described in Section 2.2.2 contradict the agent metaphor in two respects (see Page and Kreutzer, 2005, p. 351 and Knaak, 2002, p. 29): Firstly, events are often dened on a level above individual agents, which contradicts the microscopic modeling perspective.
Secondly, entities are regarded as passive elements which state is modied by events 'from the outside'. This obviously contradicts the concept of autonomy.
However, Page and Kreutzer (2005, p. 351) note that:11
Some authors like Spaniol and Ho (1995) [...] view event-orientation dierently, and attach no event routines to events. Instead, events are processed by active entities, which contain the event's relevant actions. Each entity groups state changing actions for all events in which it participates and performs these on demand; i.e. whenever relevant events occur.
This viewpoint matches agent-based modeling frameworks much better. It oers an ecient base for controlling a set of simulated agents' behaviour and is instantiated in some software systems, such as [the well-known MABS framework] Swarm [Minar et al., 1996 ...] It should be noted that in this context agents act only if an external event occurs, or if a relevant event has been triggered by the agent itself. Between events the agents' states remain constant.
3.2.3.2. Process-Oriented Simulation versus MABS
Further following Page and Kreutzer (2005, p. 352)12, we nd that
11based on (Knaak, 2002, p. 29)
12again based on (Knaak, 2002, pp. 29)
A simulation process is an active and persistent entity, whose behaviour is described from a local perspective [...] Although the agent concept is somewhat more general, it ts a process-based simulation's world view quite well (Klügl, 2001, p. 94). Inter-process communications occur either through direct or indirect synchronizations; i.e. processes are delayed in their lifecycles and must wait until reactivated, or they must queue for a resource. Patterns of communication in agent-based models can be richer. Some MABS models may even require negotiations according to complex protocols.
The behavioural exibility of simulation processes, in whose lifecyles a linear sequence of actions unfolds in a synchronous fashion, also falls short of some MABS models' require-ments. Agents may be placed in highly dynamic environments and must react quickly to asynchronous events. Dierent from the process interaction world view, spatial location of-ten also plays an important role in MABS. Simulation processes should therefore be viewed as particularly simple, pro-active agents, with limited capabilities for communication and movement.
On two occasions, the author was pointed to the fact that the original process-oriented simula-tion language Simula with its extension library DEMOS (Birtwistle, 1979) can be regarded as a predecessor of MABS due to its innovative concepts of co-routines and object-orientation.13
3.2.3.3. Individual-Based Simulation versus MABS
This umbrella term subsumes simulation world views that take up the microscopic modeling perspective of individual entities.14 According to Klügl (2000, Sec. 3.2) this includes some process- and object-oriented models as well as cellular automata and so-called microanalytical models15. Though most agent-based models can be regarded as indvidual-based, the following dierences must be mentioned as summarized by Klügl (2000, pp. 61):
• Agent-based modeling is more general in that the agent metaphor is not restricted to individuals (Klügl, 2000, p. 61). Depending on the modeling level, groups or organizations can be modeled as agents as well (Klügl, 2000, p. 61).
• Agent-based models are often more complex and heterogeneous than individual-based models with respect to behavioral and spatial modeling (Klügl, 2000, p. 62). AI methods for learning and planning are usually not found in individual-based models either.
Nevertheless the distinction between individual and agent-based models is not clear-cut, and both modeling styles apply to similar domains, such as sociology and biology.
3.2.3.4. Activity- and Transaction-Oriented Simulation versus MABS
As mentioned in Section 2.2.2 an activity-oriented model is stated as a set of rules that describe pre- and post-conditions of time-consuming activities, which is also common in MABS. Klügl
13This relation was pointed out by Prof. Dr. Horst Oberquelle at the University of Hamburg as well as a reviewer of the author's contribution (Knaak, 2004) to the Fujaba Days 2004.
14A comparison of agent- and individual-based modeling is also found in (Klügl, 2000; Knaak, 2002; Meyer, 2008).
15This model type will not be treated here. For a summary see Klügl (2000, pp. 45)
(2000, pp. 112-113) explicitly relates her (time-driven) activity-based MABS modeling approach to activity-oriented modeling. Besides the dierent scheduling approach (Klügl, 2000, p. 113), a main distinction between both world views is that rules in activity-based models are specied at the system level, while rules in MABS are assigned to specic agents. This provides an additional object-oriented structure to the rule set (Klügl, 2000, p. 109).
A comparison of MABS and transaction-oriented models is not reasonable in the rst place.
Both world views dier strongly with respect to the modeling perspective and target systems.
However, some application domains imply a combination of both approaches. A prominent example are so-called 'holonic factories', i.e. production systems without central control, where each machine (or even workpiece) is regarded as an autonomous agent responsible for its own processing (see e.g. Giret and Botti, 2009). In this scenario, the factory layout and the process-ing of workpieces can be modeled in a transaction-oriented fashion, while a controller agent is assigned to each machine. The transaction-oriented model can thus be regarded as part of the MABS's environment.