Petros Kefalas

Computational models of collective foraging

In this paper the behaviour of a bee colony is modelled as society of communicating agents acting in parallel and synchronising their behaviour. Two computational approaches for defining the agents behaviour are introduced and compared. Their common features as well as the complementary aspects making them suitable for merging together into a more complex model.

Enhancing NetLogo to Simulate BDI Communicating Agents

The implementation process of complex agent and multi-agent systems (AMAS) can benefit significantly from a simulation platform that would allow rapid prototyping and testing of initial design ideas and choices. Such a platform, should ideally have a small learning curve, easy implementation and visualisation of the AMAS under development, while preserving agent oriented programming characteristics that would allow to easily port the design choices to a fully-fledged agent development environment. However, these requirements make such a simulation platform an ideal learning tool as well. We argue that NetLogo meets most of the requirements that suit our criteria. In addition, we describe two extra NetLogo libraries, one for BDI-like agents and one for ACL-like communication that allow effortless development of goal-oriented agents, that communicate using FIPA-ACL messages. We present one simulation scenario that employs these libraries to provide an implementation in which agents cooperate under a Contract Net protocol.

Communicating X-machines: a practical approach for formal and modular specification of large systems

Abstract 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 labelled 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 X-machines 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 X-machine Description Language (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 X-machines towards the incremental modelling 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 modelling 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.

Communicating X-machines: from theory to practice

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.Th is paper describes a methodology of building communicating X-machines from existing stand-alone X-machine models and presents the theory that drives this methodology. An X-machine is a formal method that resembles a finite state machine but can model non-trivial data structures.T his 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. Ho wever, 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 standalone X-machine components and (b) the description of the communication between these components.Th is approach is based on a different view of the theory of communicating X-machines and it leads towards disciplined, practical, and modular development.T he proposed methodology is accompanied by an example, which demonstrates the use of communicating X-machines towards the modeling of large-scale systems.

Modelling dynamic organization of biology-inspired multi-agent systems with communicating x-machines and population p systems

Dynamic organization of multi-agent systems can be inspired by the way biological systems adapt to the evolution of their components. In this paper, we investigate how multi-agent systems can be formally modelled as well as how their configurations can be altered, thus affecting the communication between agents. We use two different formal methods, communicating X-machines and population P systems with active membranes, in order to model the case of flocking agents. Each method possesses different appealing characteristics which are examined through the modelling process.

A multi‐agent framework to assist networked learning

Through the use of continuously evolving information technologies, the concept of Networked Learning (NL) offers an effective and low cost means for open and distance learning. However, NL is far from being a process without problems. Apart from the pedagogical model used, NL heavily relies on organisational/management and other educational aspects. It is suggested that Artificial Intelligence (AI) provides mechanisms which have the potential to support NL both at the organisational and educational level. This paper proposes a framework based on multiple AI agents which can be used to automatically or semi-automatically assist various stages of NL. This assistance is indented for both learners and course providers who need to carry out specific tasks before, during and after the delivery of a course.

Simulation and verification of P systems through communicating X-machines

The aim of this paper is to prove the suitability of a parallel distributed computational model, communicating X-machines, to simulate in a natural way a well established model of molecular computation, P systems, and to present some further benefits of the approach allowing us to check for some formal properties. A set of rules to transform any P system with symbol-objects into a communicating X-machine model is presented and a variation of temporal logic for X-machines is briefly discussed, which facilitates model checking of desired properties of the system. Finally, the benefits resulting from the transformation are discussed.

Formal Modelling of Reactive Agents as an Aggregation of Simple Behaviours

Agents, as highly dynamic systems, are concerned with three essential factors: (i) a set of appropriate environmental stimuli, (ii) a set of internal states, and (iii) a set of rules that relates the previous two and determines what the agent state will change to if a particular stimulus arrives while the agent is in a particular state. Although agent-oriented software engineering aims to manage the inherent complexity of software systems, there is still no evidence to suggest that any proposed methodology leads towards correct systems. In the last few decades, there has been a strong debate on whether formal methods can achieve this goal. In this paper, we show how a formal method, namely X-machines, can deal successfully with agent modelling. The X-machine possesses all those characteristics that can lead towards the development of correct systems. X-machines are capable of modelling both the changes that appear in an agents internal state as well as the structure of its internal data. In addition, communicating X-machines can model agents that are viewed as an aggregation of different behaviours. The approach is practical and disciplined in the sense that the designer can separately model the individual behaviours of an agent and then describe the way in which these communicate. The effectiveness of the approach is demonstrated through an example of a situated, behaviour-based agent.

Transforming communicating X-machines into P systems

Tissue P systems (tPS) 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 (XMs) 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 XMs 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 XMs. In this paper, we define a set of principles to transform communicating XMs into tPS. 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.

Evacuation Simulation through Formal Emotional Agent based Modelling

Evacuation Simulation is recognised as an important tool for assessing design choices for urban areas. Although a number of approaches have been introduced, it is widely acceptable that such simulation scenarios demand modelling of emotional aspects of evacuees, and how these affect their behaviour. The present work, proposes that formal agent modelling based on eX-machines can rigorously define but also naturally lead to realistic simulations of such scenarios. eX-machines can model agent behaviour influenced by emotions, including social aspects of emotions, such as emotion contagion. The developed formal model is refined to simulation code, that is able to visualise and simulate crowd believable behaviour.