Brandywine

An X-machine is a general computational machine that can model: (a) non-trivial data structures as a typed memory tuple and (b) the dynamic part of a system by employing transitions, which are not labeled with simple inputs but with functions that operate on inputs and memory values. The X-machine formal method is valuable to software engineers since it is rather intuitive, while at the same time formal descriptions of data types and functions can be written in any known mathematical notation. These differences allow the X-machines to be more expressive and flexible than a Finite State Machine. In addition, a set of Xmachines can be viewed as components, which communicate with each other in order to specify larger systems. This paper describes a methodology as well as an appropriate notation, namely XMDL, for building communicating X-machines from existing stand-alone X-machine models. The proposed methodology is accompanied by an example model of a traffic light junction, which demonstrates the use of communicating Xmachines towards the incremental modeling of large-scale systems. It is suggested that through XMDL, the practical development of such complex systems can be split into two separate activities: (a) the modeling of stand-alone X-machine components and (b) the description of the communication between these components. The approach is disciplined, practical, modular and general in the sense that it subsumes the existing methods for communicating X-machines.

Formal modeling of complex systems is a non-trivial task, especially if a formal method does not facilitate separate development of the components of a system. This paper describes a methodology of building communicating Xmachines from existing stand-alone X-machine models and presents the theory that drives this methodology. A X-machine is a formal method that resembles a finite state machine but can model non-trivial data structures. This is accomplished by incorporating a typed memory tuple into the model as well as transitions labeled with functions that operate on inputs and memory values. A set of X-machines can exchange messages with each other, thus building a communicating system model. However, existing communicating X-machines theories imply that the components of a communicating system should be built from scratch. We suggest that modeling of complex systems can be split into two separate and distinct activities: (a) the modeling of stand-alone X-machine components and (b) the description of the communication between these components. This approach is based on a different view of the theory of communicating X-machines and it leads towards disciplined, practical, and modular development. The proposed methodology is accompanied by an example, which demonstrates the use of communicating X-machines towards the modeling of large-scale systems.

A X-machine is a general computational machine that can model non-trivial data structures as a typed memory tuple as well as the dynamic part of a system by employing transitions labeled with functions that operate on inputs and memory values. The X-machine formal method is rather intuitive, while at the same time formal descriptions of data types and functions can be written in any known mathematical notation. A set of X-machines can be viewed as components, which communicate with each other. This paper describes a methodology of building communicating X-machines from existing stand-alone X-machine models. It is suggested that the development of complex systems can be split into two separate activities: (a) the modeling of standalone X-machine components and (b) the description of the communication between these components. The approach is disciplined, practical, modular and general in the sense that it subsumes the existing methodologies. The proposed methodology is accompanied by an example, which demonstrates the use of communicating X-machines towards the modeling of large-scale systems.

Recent advances in both the testing and verification of software based on formal specifications of the system to be built have reached a point where the ideas can be applied in a powerful way in the design of agent-based systems. The software engineering research has highlighted a number of important issues: the importance of the type of modeling technique used; the careful design of the model to enable powerful testing techniques to be used; the automated verification of the behavioural properties of the system; the need to provide a mechanism for translating the formal models into executable software in a simple and transparent way. This paper introduces the use of the X-machine formalism as a tool for modeling biology inspired agents proposing the use of the techniques built around X-machine models for the construction of effective, and reliable agent-based software systems.

Tissue P systems represent a class of P systems in which cells are arranged in a graph rather than a hierarchical structure. On the other hand, communicating X-machines are state-based machines, extended with a memory structure and transition functions instead of simple inputs, which communicate via message passing. One could use communicating X-machines to create models built out of components in a rather intuitive way. There are investigations showing how various classes of P systems can be modelled as communicating X-machines. In this paper, we define a set of principles to transform communicating Xmachines into Tissue P systems. We describe the rules that govern such transformations, present an example to demonstrate the feasibility of this approach and discuss ways to extend it to more general models, such as population P systems, which involve dynamic structures.

Social insect colonies present an interesting problem for formal modelling due to their outstanding characteristics, such as self-organisation and emergence. In this paper, we experiment with two different formal methods, Communicating X-machines and Population P Systems, which can be used separately to model biologically inspired. systems. The case of Pharaoh's ants is used as a vehicle of study. We discuss the advantages of each method and we present a framework that leads to a rapid implementation and simulation of such multiagent systems.

Formal development of agent systems with inherent high complexity is not a trivial task, especially if a formal method used is not accompanied by an appropriate methodology. Xmachines is a formal method that resembles Finite State Machines but has two important extensions, namely internal memory structure and functions. In this paper, we present a disciplined methodology for developing agent systems using communicating X-machine agents and we demonstrate its applicability through an example. In practice, the development of a communicating system model can be based on a number of well-defined distinct steps, i.e. development of types of X-machine models, agents as instances of those types, communication between agents, and testing as well as model checking each of these agents individually. To each of the steps a set of appropriate tools is employed. Therefore the proposed methodology utilises a priori techniques to avoid any flaws in the early stages of the development together with a posteriori techniques to discover any undiscovered flaws in later stages. This way it makes the best use of the development effort to achieve highest confidence in the quality of the developed agents. We use this methodology for modelling naturally distributed systems, such as multi-agent systems. We use a generalized example in order to demonstrate the methodology and explain in detail how each activity is carried out. We briefly present the theory behind communicating Xmachine agents and then we describe in detail the practical issues related using the same example throughout.

In recent years multi-agent modelling and simulation has been suggested as an approach to exploring emergent phenomena exhibited by a variety of systems. A vital part of such an effort is the ability to detect and quantify the particular emergent manifestation under study. As part of a larger study in exploring causal relations in emergent formations, this paper investigates the issue of automated detection of herds in a multi-agent model of large bodied animal dynamics. The proposed solution is based on a fuzzy reasoning approach which incorporates bottom-up and top-down phases as part of the reasoning process. The evaluation of the proposed reasoning method showed that it can be successfully applied for automated detection of herds in multi-agent simulation.

Abstract:-Simulation is a powerful technique for the study of real-life complex problems. Simulation requires the development of a program, which mimics the problem under consideration. The simulation program development follows closely the software engineering process. Although, many techniques for modelling problems have been proposed, the testing phase of the developed programs has been under-discussed.