The use of broadcast based, service discovery protocols puts a significant strain on the delivery reliability of messages, particularly when a large number of devices simultaneously issue discovery requests. This usually results in undelivered discovery requests either requiring the requests to be issued again or the services remaining undiscovered. One of the principle courses of such message loss is the queuing mechanism used by the intermediate routers and/or any control points required to manage devices. This paper presents a significant analysis of the relationships between the different network devices to try to understand the causes of such message loss. In particular it will examine the different causes and how they influence each other. We present a formula which shows the relationship between the different causes of undelivered messages. This formula has been applied to the Universal Plug and Play (UPnP) protocol using simulated client and service applications written in C++. This paper provides researchers and network designers a mechanism for analyzing the potential performance of a network. It allows the impact of the relationships between the different causes for then on-delivery of messages to be taken into consideration when designing a network and applying protocols to that network.

In this short conceptual paper we explore the need for demonstrations of intelligent environments research that can convey what we as researchers think to potential users that have limited exposure to such ideas. This is especially important where physical and virtual worlds meet in the smart home context. We present several exemplars that are intended to promote user understanding through the use of mixed reality technologies.

In this paper, we will introduce a novel system where identical miniaturized robotic agents with limited capabilities will collaborate to form a team that is capable of localizing and repairing scale formations in tanks and pipes within inaccessible fluidic environments. Each robotic agent is an autonomous entity that is based on the biologically inspired spiking neural networks (SNNs) that communicate using pulses or spikes. The weights of the SNN are evolved using adaptive genetic algorithm (GA) that uses adaptive crossover and mutation to converge relatively fast to solutions that allow the robots to complete the desired tasks. The robotic agents communicate using indirect communication to move towards the site of scale formation and collaborate to repair damages.

This work contains the results of an initial study into the FPGA implementation of a spiking neural network. This work was undertaken as a task in a project that aims to design and develop a new kind of tangible collaborative autonomous agent. The project intends to exploit/investigate methods for engineering emergent collective behaviour in large societies of actual miniature agents that can learn and evolve. Such multi-agent systems could be used to detect and collectively repair faults in a variety of applications where it is difficult for humans to gain access, such as fluidic environments found in critical components of material/industrial systems. The initial achievement of implementation of a spiking neural network on a FPGA hardware platform and results of a robotic wall following task are discussed by comparison with software driven robots and simulations.

In this paper we introduce a novel mechanism for controlling autonomous mobile robots that is based on using spiking neural networks (SNNs). The SNNs are inspired by biological neurons that communicate using pulses or spikes. As SNNs have shown to be excellent control systems for biological organisms, they have the potential to produce good control systems for autonomous robots. In this paper we present the use and benefits of SNNs for mobile robot control. We also present an adaptive genetic algorithm (GA) to evolve the weights of the SNNs online using real robots. The adaptive GA using adaptive crossover and mutation converge in a small number of generations to solutions that allow the robots to complete the desired tasks. We have performed many experiments using real mobile robots to test the evolved SNNs in which the SNNs provided a good response.

The paper describes research that has been carried out in "extrovert-Gadgets", a research project funded in the context of EU IST/FET proactive initiative "Disappearing Computer". It presents a set of architectures for the composition of ubiquitous computing applications. The proposed architectures are part of GAS (Gadgetware Architectural Style), a generic architectural style, which can be used to describe everyday environments populated with computational artifacts. The overall innovation of the GAS approach lies in viewing the process where people configure and use complex collections of interacting eGadgets, as having much in common with the process where system builders design software systems out of components. This approach regards the everyday environment as being populated with tens even hundreds of artifacts, which people (who are always in control) associate in ad-hoc and dynamic ways.

In this paper we introduce a novel learning and adaptation system for embedded-agents embodied and situated in intelligent inhabited environments. The fuzzy incremental synchronous learning (ISL) techniques we describe seek to provide an online, life-long, non-intrusive method for learning personalised behaviour and anticipatory adaptive control for physical environments.

Addresses the development of a system for online learning of fuzzy behaviour co-ordination for autonomous agents in the form of robots based on genetic algorithms (GAs) and real-time interaction with the environment. The proposed system organises the behaviours hierarchically and uses fuzzy engines to implement both the behaviours and their co-ordination mechanism. In previous work (1999) we reported on our success in the online learning of individual behaviours (rules and membership functions). In this paper we report on a system that allows the fuzzy membership function (MF) for behaviour co-ordination to be learnt online in a manner that satisfies some high level mission or plan. The GAs use adaptive learning parameters and guided constrained optimisation to speed the GAs search and enable it to be performed via real-world interaction rather than off-line simulation. The results of this work are compared with results reported elsewhere and reveals this approach to have a superior learning performance while learning using real outdoor robots in changing environments. The ability to learn co-ordination skills in a short time interval without human intervention makes this approach particularly useful for applications where access is difficult such as nuclear reactors, underwater vehicles and space robots and fast changing and dynamic environments such as the agricultural environments.

We describe a technique which enables a fuzzy-logic based robot control system to automatically determine the membership functions (MF) of the input sensors online and in a short time interval. There is a necessity for such online self-calibration for fast changing and dynamic environments such as agricultural environments and difficult or inaccessible environments, such as nuclear reactors, underwater and space environments. In these media the robot has to learn the appropriate MF with no human intervention taking into account the difference in sensor characteristics in the different environments and changes in production requirements and repairing or otherwise upgrading robots. So there is a necessity to find a fast converging algorithm that can calibrate the MF online in real time with no need for human intervention or simulation. our work reports on an approach based on the use of a modified genetic algorithm to evolve the fuzzy MF of the individual behaviours. The MF of four behaviours were learnt online in an average time of 4 minutes for each behaviour in an outdoor environment. These learnt behaviours were then co-ordinated and tested in complex and dynamic environments in which the robot gave a very good response.

This paper describes the design of a fuzzy controlled autonomous robot, incorporating genetic algorithms (GA) based rule learning, for use in an outdoor agricultural environment for path and edge following activities which involve spraying insecticide, distributing fertilisers, ploughing, harvesting, etc. The robot has to navigate under different ground and weather conditions. This paper addresses the development of an online self-learning system based on modified version of the fuzzy classifier system. The proposed technique has resulted in rapid convergence suitable for learning individual behaviours online without the need for simulation. The controller was tested on both an in-door and out-door mobile robot operating with different types of sensors, propulsion and steering. Experiments include operating the vehicle following irregular crop edges under different weather and ground conditions within a tolerance in the order of 2 inches.