INTRODUCTION

This survey is part of a requirements analysis activity in the SimCog project. The SimCog research project (Simulation of Cognitive Agents) aims to develop a generic agent-based-simulation platform. The project wider goals are: (i) to propose a reference model for the requirements specification of multi-agent-based platforms, promoting the identification of shared requirements in the scientific community, and addressing these requirements to the development of integrated projects; (ii) to analyse existing methods and assess methodological and epistemological questions in the intersection of agent-based simulation, computer science and social/natural sciences; (iii) to inform the specification and the development of the SimCog platform.

The role of the survey is to advance the prospection of technical-operational and high-level requirements for multi-agent-based simulation (MABS) platforms, and to identify general methodological principles that will lead to the development of agent-based simulations.

THE QUESTIONNAIRE

This questionnaire is directed to researchers who use multi-agent-based simulation in various scientific fields, from social and natural science to mathematical, cognitive and computer science. The final results will be available at our site, and your privacy will be preserved. 

The following sections compose the first module of a two-module questionnaire. This first module assesses a requirements analysis of multi-agent-based simulation platforms. The second module focus on general methodological and epistemological topics in agent-based simulation, and it will be available at our site soon. All those who answer the first module will be invited to answer the second. 

In the following sections, you will find six possible choices for each possible answer. We believe it will be easy for you to answer. We expect that it will require approximately 20 minutes of your time. All descriptions and questions are self-contained. Nevertheless, if you want further information please consult this paper.

Thank you for your attention!

PERSONAL INFORMATION    (*) Required Field

A1) Your interest in agent-based simulation (can be more than one):

	Engineering				Industrial
	Research				Policy
	Education				Business

A2) Intended use of simulation (can be more than one):

	To model and simulate artificial societies that do not necessarily reference a concrete target or specific theory about the real world, but only some proposed idea of abstract nature.
	To model socio-cognitive or sociological theories and implement computational animations of logical formalisms, in order to refine/extend social theories and check its consistency.
	To model and simulate concrete social systems based on direct observation and statistical data, in order to understand social and institutional processes and phenomena.
	To model and simulate multi-agent systems to explore multi-agent system requirements and intended behaviours, for use in real environments and general-purpose engineering.
Others:

MANAGE MOBILITY (F)
Mobility defines the agents' ability to navigate within electronic communication networks. To comply with this requirement a platform should provide services such as (i) transmit and receive agents through the network; (ii) persistence mechanisms to maintain the agents state when travelling.

MODEL SCALABILITY (NF)
Roughly, scalability assesses how well the system does useful work as the size and/or complexity of the system increases. Ideally, a platform should efficiently process large-scale systems in a reasonable amount of time, and according to the users' needs. Some metrics to assess and control scalability are: (i) number of agents that the system is able to control with reasonable qualitative results; (ii) an ordered increase in the number of agents, for instance, clustering agents in groups associated with logical or physical patterns. To fulfil this requirement a platform should be structured in order to maintain and/or improve its performance while increasing the size, complexity and workload of its components.

DOMAIN FACILITIES

LAUNCH AGENTS (F)
A platform should provide agent templates related with different manners to launch agents like threads, applets and objects. For instance, platforms that do not provide multi-threaded agents may difficult the modelling and simulation of distributed systems.

MANAGE INTENTIONAL FAILURES (F)
From a technical-operational point of view there are two classes of intentional failures that can be manipulated in a simulation. The first class, called operational failures, works with disturbances in the technical-operational infrastructure (corrupted messages, server failures, etc.). The second, called logical failures, manipulate patterns of behaviour that can be viewed as dysfunctional exceptions in the simulated system. Operational failures can be used to build specific scenarios and serve as the base to build more general logical failures. Logical failures are strongly domain dependent, and the user may have to engage in further implementation work in order to utilize them. The platform should offer (i) libraries to manipulate basic operational failures; (ii) mechanisms to store and search templates of logical failures created by users.

INTEGRATE CONTROLLED AND NON- CONTROLLED ENVIRONMENTS (F)
Typically, the simulation environment must be totally controlled, every event in the simulation world must be performed under the control of the simulator. These situations characterize what we call controlled environments. There may be cases where the agents can (or must) perform actions outside the controlled environment, in real environments. To support this functionality a platform should offer agent architectures that separate the agent domain-dependent behaviour from the simulator design patterns. Also, in order to keep the simulation consistent and guarantee a good level of repeatability, when agents are running in non-controlled environments some of their events should be notified to the simulator, which should update its local view.

MODEL THE PLATFORM EXECUTION MODE (NF)
To support simulations that demand high processing and/or number of components, a platform should be executed in different processors and machines, in a distributed way. The platform should be designed with a non-centralized architecture, for which the simulation components could be distributed across different physical hosts and/or processors. Issues like network protocols and synchronization are crucial in such environments.

PROVIDE GRAPHICAL REPRESENTATION OF DOMAIN(s) (NF)
A platform should offer user-friendly graphical interfaces to facilitate the construction of graphical representations of domain(s), taking in consideration  the environment, the agents and their interactions. Moreover, it should provide help systems to guide and direct the interaction between the platform and its users.

DEVELOPMENT FACILITIES

GUARANTEE INDEPENDENCY FROM THE SIMULATOR (F)
The platform should provide agent architectures independent from the simulator design patterns. After the verification and validation processes the agents should be easily decoupled from the simulator, and be ready to be deployed in real environments.

USE ORGANISATIONAL ABSTRACTIONS (F)
Organisational abstractions are MAS components that explicitly structure an organisation. The platform should provide services to represent an organisation, taking together organisational abstractions like roles, groups and multiple societies.

USE GROUPS (F)
Groups define different aggregates of agents according to modularity, encapsulation and organisational principles. The platform should support the creation and management of agent collections, clustered around common relations that can be defined by the users.

USE ROLES (F)
A role is what an agent is expected to do in the organisation, in an autonomous way or in cooperation with other agents. A platform should provide services to define roles in terms of functions, activities or responsibilities. It should also be possible to define their interaction protocols.

USE ORGANISATIONAL RULES (F)
Organisations should take into account a set of constraints to model their global behaviours. Such constraints are called organisational rules. For instance, an organisational rule in a football team specifies that the players must respect the coach. The platform should provide services to express specific relations and/or constraints between roles, protocols and between roles and protocols.

USE MULTIPLE SOCIETIES (F)
In the real world we have the ability to create explicit organisational structures,  observe them and reason about them, like other agents, institutions or even new societies (for example, artificial agent societies). From the observer's point of view, an artificial society may be seen as an aggregate of agents and organisations, which coexist and interact with each other through social events. The concept of society in agent-based simulation is rarely specified as an explicit structural and relational entity, but usually implicitly defined in formally or informally terms of inclusiveness of agents and organisational entities. Such tendency complicates the design of artificial agents that are able to observe and reason about other societies, particularly if the environment is composed of multiple interacting social spaces and levels of abstraction. Although some approaches have used models that explicitly define multiple societies, the concept of society in those models is still reducible to a group, where agents are viewed simultaneously both as actors and non-neutral observers in a given society. Therefore, the role of opaque artificial observers is not assigned explicitly to agents, being exclusively and implicitly defined in the person of the system designer. The platform should provide primitives to instantiate topologies of multiple societies and to instantiate opaque social spaces (see requirement R26) that can be used as neutral observation windows to other societies and social spaces.

USE ONTOLOGIES (F)
Ontologies can be used with many objectives like interoperability, reuse, search of information and knowledge sharing and acquisition. When using ontologies to assist simulation modelling, the platform should provide: (i) knowledge bases containing the ontologies; (ii) a search engine to manipulate the ontologies; (iii) a browser to query the search engine and visualize the results; (iv) mechanisms to relate the ontologies with implementation components.

ADOPT ONTOLOGICAL COMMITMENT (NF)
The use of ontologies may take into account the establishment of an ontological commitment. An ontological commitment is an agreement to use the shared vocabulary in a coherent and consistent way. This commitment allows the access to heterogeneous sources of information, which could be otherwise unintelligible. To comply with this requirement a platform should be designed having in mind the sharing of ontologies between the simulation components.

PROVIDE TRANSLATION MECHANISMS (F)
Translation mechanisms are necessary when agents use different protocols and/or languages, and need to communicate with each other. Translation may be implemented through different ontologies related to common blocks of information shared by agents. To provide this functionality the platform should offer services such as: (i) knowledge bases containing the ontologies; (ii) tools that translate ontologies to operational data.

ANALYSIS FACILITIES

OBSERVE BEHAVIOURAL EVENTS (F)
Behavioural events are the agents' social events that can be observed by an external observer (e.g., message passing, creation/destruction of agents, data base access). The platform should offer mechanisms to select specific points (observation windows) to observe behavioural events.

OBSERVE COGNITIVE EVENTS (F)
Cognitive events concern events in the agents' internal architectures. The observation of these events may be counter-intuitive according to the usual agent paradigm, but indispensable to analyse structured simulations. For example, one may want to analyse the effect of some social event on the recipient agent's mental states. The platform should offer mechanisms to control the agents' internal mechanisms, in order to trigger specific observation methods. In order to provide structured observation of cognitive events the platform should comply with another requirement, called Cognitive Reflectivity (see R27).

DEFINE SCENARIOS (F)
The platform should provide services to configure/assemble different scenarios considering characteristics like type and quantity of agents.

CONTROL TRACKING (F)
The platform should provide services to follow up the execution of simulations, in both graphical and batch modes (storing data to later analysis and manipulation). It also should provide services to terminate, initialise, interrupt and temporarily suspend simulations. While suspended the platform should permit the observation (see R17 and R18) and intervention (see R24 and R25) in behavioural and cognitive events.

PROVIDE SENSITIVITY ANALISYS (F)
The platform should offer services to implement controlled variations of simulation parameters, and provide graphical and statistical packages to assess relationship between parameter variation and changes in the output data.

PROVIDE GRAPHICAL INTERFACE (NF)
The platforms should provide friendly graphical interfaces to assist debugging processes and observation activities, helping the researcher to understand the dynamic behaviour of a simulation.

EXPLORATION FACILITIES

INTERVENE IN BEHAVIOURAL EVENTS (F)
Behavioural events are the agents' social events that can be observed by an external observer. The platform should offer mechanisms to select specific points to intervene in behavioural events. The intervention should permit the suppression, modification or creation of behavioural events. For instance, one may want to modify the content or the intended recipient of behavioural events (e.g. messages), or even to inhibit its arrival to the intended recipients. These experiments give the means to analyse functional effects in the simulation, independing from the agents' internal representations that originate other or those same events.

INTERVENE IN COGNITIVE EVENTS (F)
The platform should offer mechanisms to intervene in the agents' internal mechanisms, for instance, program variables, order of method invocation and the agents beliefs. This may alter the order of invocation and nature of behavioural events. In order to provide structured interventions the platform should comply with another requirement, called Cognitive Reflectivity (see R27).

MANAGE SOCIAL OPACITY (F)
The problematic of social opacity analyses the organisational conditions under which the control of cognitive information transfer between agents in different social spaces is possible, for instance between different multiple societies (see R14.D). Social opacity is therefore related to organisational borders. The platform should provide the means to instantiate different topologies of opaque social spaces in a dynamic way. This is useful to simulate agents that have the ability to instantiate and observe given models of other artificial agents and societies, allowing the simulation of agents that reason autonomously about the heterogeneity of different models of societies at various levels of observation. However, while the observed agents and societies must be visible to the observer agent, the observer agent and societies must be opaque to the observed agents. The platform should provide organisational ingredients and services to instantiate multiple societies (see R14.D) and opaque social spaces.

PROVIDE MODELS OF COGNITIVE REFLECTIVITY (F)
Cognitive reflectivity refers to the identification of cognitive structures and internal procedures of agents at run time. The agent architectures templates (see R12) should provide adequate models of cognitive reflectivity, allowing the user or other system agents to observe and intervene in the simulated agents' cognitive events at run time. Thus, different models of cognitive reflectivity should be provided together with generic agent architectures (see R12).