Agent-based modeling of ancient societies and their organization structure

Some of the most interesting questions one can ask about early societies, are about people and their relations, and the nature and scale of their organization. In this work, we attempt to answer such questions with approaches introduced by multiagent systems. Specifically, we developed a generic agent-based model (ABM) for simulating ancient societies. Unlike most existing ABMs used in archaeology, our model includes agents that are autonomous and utility-based. Our model can (and does) also incorporate different social organization paradigms and technologies used in ancient societies. Equipped with such paradigms, our model allows us to explore the transition from a simple to a more complex society by focusing on the historical social dynamics—i.e., the flexibility and evolution of power relationships depending on social context and time. As a case study, we employ our model to evaluate the impact of the implemented social and technological paradigms on an artificial Early Bronze Age “Minoan” society located at a particular region of the island of Crete. Model parameter choices are based on archaeological evidence and studies, but are not biased towards any specific assumption. Results over a number of different simulation scenarios demonstrate an impressive sustainability for settlements consisting of and adopting a socio-economic organization model based on self-organization, and which was inspired by a recent framework for modern self-organizing agent organizations. This is the first time a self-organization approach is incorporated in an archaeology ABM system.     

Modelling Off-the-Shelf Information Systems Requirements: An Ontological Approach

Requirements for choosing off-the-shelf information systems (OISR) differ from requirements for development of new information systems in that they do not necessarily provide complete specifications, thus allowing flexibility in matching an existing IS to the stated needs. We present a framework for OISR conceptual models that consists of four essential elements: business processes, business rules, information objects and required system services. We formalise the definitions of these concepts based on an ontological model. The ontology-based OISR model provides a framework to evaluate modelling languages on how appropriate they are for OISR requirements specifications. The evaluation framework is applied to the Object-Process Methodology, and its results are compared with a similar evaluation of ARIS. This comparison demonstrates the effectiveness of the ontological framework for evaluating modelling tools on how well they can guide selection, implementation and integration of purchased software packages.     

“Virtual laboratory environment” (VLE): a software environment oriented agent and object for modeling and simulation of complex systems

Our research relates to multi-agent and oriented object modeling and simulation of the complex systems. Our research interest itself more particularly with system where the spatial and temporal component make a great part of system to model (for example, ecosystems or systems of production). Within the framework of this article, we will be interested in the flexible production systems.The simulation of complex systems requires generally the integration and the coupling of heterogeneous models (multi-agent, mathematical, and so on). This heterogeneity is a consequence of the diversity of the disciplines and abilities of designers. The approach that we develop consists in the development of “virtual laboratories ”. Our platform “virtual laboratory environment” (VLE) enables us to specify, simulate and analyze spatial complex systems. VLE is based on the concepts of reactive agents, objects and spatial and temporal multi-scale systems.     

Simulating heterogeneous behaviours in complex systems on GPUs

Agent Based Modelling (ABM) is an approach for modelling dynamic systems and studying complex and emergent behaviour. ABMs have been widely applied in diverse disciplines including biology, economics, and social sciences. The scalability of ABM simulations is typically limited due to the computationally expensive nature of simulating a large number of individuals. As such, large scale ABM simulations are excellent candidates to apply parallel computing approaches such as Graphics Processing Units (GPUs). In this paper, we present an extension to the FLAME GPU¹ [1] framework which addresses the divergence problem, i.e. the challenge of executing the behaviour of non-homogeneous individuals on vectorised GPU processors. We do this by describing a modelling methodology which exposes inherent parallelism within the model which is exploited by novel additions to the software permitting higher levels of concurrent simulation execution. Moreover, we demonstrate how this extension can be applied to realistic cellular level tissue model by benchmarking the model to demonstrate a measured speedup of over 4x.     

Proving the Asymmetry Thesis Principles for a BDI Agent-Oriented Programming Language

In this paper, we consider each of the nine principles of BDI logics as defined by Rao and Georgeff based on Bratman's asymmetry thesis, and we verify which ones are satisfied by Rao's AgentSpeak(L), a computable logic language inspired by the BDI architecture for cognitive agents. This is in line with Rao's original motivation for defining AgentSpeak(L): to bridge the gap between the theory and practice of BDI agent systems. In order to set the grounds for the proof, we first introduce a particular way in which to define the informational, motivational, and deliberative modalities of BDI logics for AgentSpeak(L) agents, according to its structural operational semantics (that we introduced in a recent paper). This provides a framework that can be used to investigate further properties of AgentSpeak(L) agents, contributing towards giving firm theoretical grounds for BDI agent programming.     

Reputation-based decisions for logic-based cognitive agents

Computational trust and reputation models have been recognized as one of the key technologies required to design and implement agent systems. These models manage and aggregate the information needed by agents to efficiently perform partner selection in uncertain situations. For simple applications, a game theoretical approach similar to that used in most models can suffice. However, if we want to undertake problems found in socially complex virtual societies, we need more sophisticated trust and reputation systems. In this context, reputation-based decisions that agents make take on special relevance and can be as important as the reputation model itself. In this paper, we propose a possible integration of a cognitive reputation model, Repage, into a cognitive BDI agent. First, we specify a belief logic capable to capture the semantics of Repage information, which encodes probabilities. This logic is defined by means of a two first-order languages hierarchy, allowing the specification of axioms as first-order theories. The belief logic integrates the information coming from Repage in terms if image and reputation, and combines them, defining a typology of agents depending of such combination. We use this logic to build a complete graded BDI model specified as a multi-context system where beliefs, desires, intentions and plans interact among each other to perform a BDI reasoning. We conclude the paper with an example and a related work section that compares our approach with current state-of-the-art models.     

A hybrid hierarchical agent-based simulation approach for buildings indoor layout evaluation based on the post-earthquake evacuation

In the aftermath of severe earthquakes, building occupants evacuation behaviour is a vital indicator of the performance of an indoor building design. However, earthquake evacuation has been systematically neglected in the current building design practice. Arguably, one of the primary reasons for this is that post-earthquake evacuation behaviour is complex and distinct from all other types of evacuation behaviours such as fire. Thus, a comprehensive approach to considering the integration of human evacuation behaviour and a building's indoor layout design, mainly focused on non-structural damage, has been consistently neglected in the literature. In this paper, a hierarchical hybrid Agent-Based Model (ABM) framework integrated with a Cellular Automata (CA) and a 2D Building Information Model (BIM) damage visualisation to consider an approximation of non-structural damage has been developed. The proposed ABM incorporates learning mechanisms and human psychological aspects influencing evacuees' utility during the navigation process. The proposed approach was verified by comparing the results to previous real-life post-earthquake evacuation data and a “model to model” comparison of results from the existing relevant studies. The model prototype was successfully tested to simulate the pedestrian evacuation process from one floor of the new engineering building at The University of Auckland, New Zealand. The proposed simulation approach has been carried out for two different internal layout design alternatives where five population sizes are evacuated through different scenarios. The outputs from this study can be used to improve the design's compatibility of the building's indoor layout with the occupants' post-earthquake evacuation behaviour.     

Qualitative modelling with imprecise ecological knowledge: a framework for simulation

Ecological understanding is often imprecise and heterogeneous; relationships between different quantities and objects may only be expressed in roughly quantitative or even non-quantitative terms. We argue that there is a need for general time-driven simulation modelling systems capable of utilising these types of understanding, using vegetation dynamics as an example. Although work has gone into developing qualitative models in the past, it has not focussed on the needs of time-driven simulation and there is currently no off-the-shelf solution available. This paper presents a categorisation of the types of knowledge that comprise formal models, then uses the categorisation as the basis for exploring and developing a framework for time-driven simulation modelling with imprecise, heterogeneous knowledge. One of the key concepts presented is the explicit separation of all non-quantitative state variable values from their direction and rate of change. From this concept a general computational method is developed for updating non-quantitative state variables in time-driven simulation. First-order logic is advocated as a suitable representational vehicle and a modelling system that implements the proposed framework is briefly presented. We believe that the framework provides a useful step towards increasing the practical utility of available knowledge.     

A hierarchical composite framework of parallel discrete event simulation for modelling complex adaptive systems

As complex adaptive systems(CAS) continue to grow in scale and complexity, and the need for system adaptability increases, systems modelling has become an essential concern. Parallel discrete event simulation became a preferred choice as logical process world view, which bridges complex system modelling and high-performance computing. To resolve the shortcoming of this world view identified with respect to modularity and scalability. A hierarchical composite modelling framework was proposed, which is a three-level architecture intended to support the composition and integration of sub-models. The bottom layer is simulation model component(SMC), which is not a model but implement some simulation-specific support functionality. The middle layer is logical process model(LP), which describes an agent which can react to the current situation by executing a sequence of SMCs. The top layer is CAS system model, which defines a CAS model consist of several LPs and also the interactions between these LPs. The hierarchical composite modelling process and parallel simulation execution strategy are discussed to support the modelling and simulation of a CAS. In order to verify its effectiveness, a complex social opinion system model is proposed based on this hierarchical composite modelling framework. The experimental results confirms the viability of utilizing multi-level architecture for simulating large scale complex adaptive systems.     

Modelling real world industrial systems: an inter-disciplinary approach based on petri nets

A modelling approach that will facilitate an in-depth understanding of the inter-relations of the different phenomena, human interactions and environmental factors constituting “real world” industrial processes is presented. Important industrial systems such as aluminium smelters, nuclear plants and batch chemical reactors have inter-related internal process activities coexisting with external events and requires an inter-disciplinary approach to model them. This modelling framework is based on identifying as modules, processes prevalent in industrial systems which to some degree are homogeneous in their actions. The selected initial set of modules are structured as petri net models and made to interact iteratively to provide process states of the system. The goal is accomplished by identifying the evolution of the process states and interpreting this in a markov chain as a means of effective representation of the “actual running” of the industrial process. The paper discusses the function and the implementation of the modelling method as applicable to the industrial reduction (smelting) of aluminium.     

Multiagent simulation of protection of information resources in internet

An approach to investigation of mechanisms of protection of information resources on the Internet based on multiagent simulation is proposed. According to this approach, protection systems are considered as interacting teams of intellectual agents. The architecture and program implementation of the simulation environment providing a possibility of integration of simulation on the basis of discrete events, multiagent approach, and simulation of network packet exchange using different Internet protocols are presented. The developed environment provides a possibility of analysis of complex attack scenarios and defense mechanisms. Experimental results on investigation of cooperative mechanisms of defense from “Distributed Denial of Service” attacks are presented. The promising character of realization of these mechanisms for defense from distributed attacks on the Internet is demonstrated.     

Modelling and simulation of complex manufacturing systems using statechart-based actors

This paper describes a multi-agent architecture based on the actors computational model, for the distributed simulation of discrete event systems whose entities have a complex dynamic behaviour. Complexity is dealt with by exploiting statechart-based actors which constitute the basic building blocks of a model. Actors are lightweight reactive autonomous agents that communicate to one another by asynchronous message passing. The thread-less character of actors saves memory space and fosters efficient execution. The behaviour of actors is specified through “distilled statecharts” that enable hierarchical and modular specifications. Distributed simulation is achieved by partitioning a system model among a set of logical processes (theatres). Timing management and inter-theatre communications rest in a case on the High Level Architecture services. The paper illustrates the practical application of the proposed modelling and simulation methodology by specifying and analysing a complex manufacturing system.     

Lessons learned on development and application of agent-based models of complex dynamical systems

The field of agent-based modelling (ABM) has gained a significant following in recent years, and it is often marketed as an excellent introduction to modelling for the novice modeller or non-programmer. The typical objective of developing an agent-based model is to either increase our mechanistic understanding of a real-world system, or to predict how the dynamics of the real-world system are likely to be affected by changes to internal or external factors. Although there are some excellent ABMs that have been used in a predictive capacity across a number of domains, we believe that the promotion of ABM as an ‘accessible to all’ approach, could potentially lead to models being published that are flawed and therefore generate inaccurate predictions of real-world systems. The purpose of this article is to use our experiences in modelling complex dynamical systems, to reinforce the view that agent-based models can be useful for answering questions of the real-world domain through predictive modelling, but also to emphasise that all modellers, expert and novice alike, must make a concerted effort to adopt robust methods and techniques for constructing, validating and analysing their models, if the result is to be meaningful and grounded in the system of interest.     

Structures in systems 2: viewpoint cohomology of general systems

This paper introduces new homology and cohomology functors designed to analyze a broad range of logical systems including representations of enterprise information systems. Such systems contain nested relations and are a class of higher order logic. The homology and cohomology augment simplex constructions with ideas of type extensions to capture the logic of the system. Here logical systems, including large-scale information systems, have a class of derived objects called “scenarios” that give the connective logic of the system. Scenarios have a “secondary” logic derived from the underlying business rules of the enterprise. A “viewpoint” is a flexible classification logic which creates “proxy” variables so that the (co)homology functors can be applied to the secondary (and higher) scenario logics but with different (but functionally related) variables. These viewpoint (co)homologies can then analyze structures in emergent phenomena that reflect structures in the logic of the original system.     

Proving BDI Properties of Agent-Oriented Programming Languages: The asymmetry thesis principles in AgentSpeak(L)

In this paper, we consider each of the nine BDI principles defined by Rao and Georgeff based on Bratman's asymmetry thesis, and we verify which ones are satisfied by Rao's AgentSpeak(L), a logic programming language inspired by the BDI architecture for cognitive agents. In order to set the grounds for the proofs, we first introduce a rigorous way in which to define the informational, motivational, and deliberative modalities of BDI logics for AgentSpeak(L) agents, according to its structural operational semantics that we introduced in a recent paper. This computationally grounded semantics for the BDI modalities forms the basis of a framework that can be used to further investigate BDI properties of AgentSpeak(L) agents, and contributes towards establishing firm theoretical grounds for a BDI approach to agent-oriented programming.     

Modelling socio-ecological systems with MAIA: A biogas infrastructure simulation

Similar to other renewable energy technologies, the development of a biogas infrastructure in the Netherlands is going through social, institutional and ecological evolution. To study this complex evolutionary process, we built a comprehensive agent-based model of this infrastructure. We used an agent-based modelling framework called MAIA to build this model with the initial motivation that it facilitates modelling complex institutional structures. The modelling experience however proved that MAIA can also act as an integrated solution to address other major modelling challenges identified in the literature for modelling evolving socio-ecological systems. Building on comprehensive reviews, we reflect on our modelling experience and address four key challenges of modelling evolving socio-ecological systems using agents: (1) design and parameterization of models of agent behaviour and decision-making, (2) system representation in the social and spatial dimension, (3) integration of socio-demographic, ecological, and biophysical models, (4) verification, validation and sensitivity analysis of such ABMs.     

Visual perception modelling for intelligent virtual driver agents in synthetic driving simulation

This paper introduces a vision model used within a synthetic (or purely software-based) driving simulation framework. This framework represents driver decision-making, individual vehicle movement and emergent traffic flow, and is intended to aid the integration of driver psychology, traffic management and vehicle engineering. The aims of developing the vision model discussed here are twofold: Firstly, to remove the unrealistic availability of ‘perfect knowledge’ concerning the positions and velocities of vehicles in a simulation and secondly, to provide a means of introducing deeper cognitive models of driver reasoning and behaviour. The paper presents the essential mechanisms of the vision model, along with the results of initial validation experiments conducted with Leicestershire Constabulary, Traffic Division in the UK. In these experiments, subjects' visual perception of positions and speeds of moving vehicles were measured and compared with estimations of the agent based driving simulator. The results have demonstrated the feasibility of modelling driver vision within an agent based traffic simulation using concepts derived from AI and ALife systems. The paper is completed by a short discussion on the future development of cognitive models enabled through more detailed vision modelling.     

Cyber coordinated simulation for distributed multi-stage additive manufacturing systems

Additive Manufacturing (AM) processes have been increasingly used to manufacture energy storage products with dedicated material preparation and post-processing stages to enhance product properties. Most researchers focus on selecting materials and improving processes, yet the system modeling and management has not been investigated so far. This paper extends the conventional single-stage AM processes to multi-STage distRibutEd AM (STREAM) systems. In STREAM, a batch of material produced at the pre-processing stage is jointly consumed by distributed AM printers, and then the printed parts are collected for the post-processing stage. Modeling and managing such complex systems have been challenging. We propose a novel framework for “cyber-coordinated simulation” to manage the hierarchical information in STREAM. This is important because simulation can be used to infuse data into predictive analytics, thus providing guidance for the optimization and control of STREAM operations. The proposed framework is hierarchical in nature, where the single-stage, multi-stage, and distributed productions are modeled through the integration of different simulators. We demonstrate the proposed framework with simulation data from Freeze Nano Printing (FNP) AM for the fabrication of energy storage products.