Cats and dogs and pheromones: researching the student experience

Pheromone Therapy is a unique online course pioneered by the University of Lincoln and delivered through the university's virtual learning environment. The course adopted innovative practices such as induction activities designed to embed the skills required for successful online learning, a range of interactions with content and focus on opportunities for socialisation including ‘café’ forums and a student gallery. Retention is a key issue with distance delivery (Simpson, 2003) but listening to the student voice is only a comparatively recent initiative. Past studies have focussed on non-completion (Yorke & Longden, 2008) or the student experience on campus (JISC, 2007). Pheromone Therapy was an opportunity to capture data about distance learner’s experience of learning in isolation and collect individual responses to the course design. Findings provided a rich source of information for the construction and delivery of online courses in the future. Social aspects of learning online have been suggested as prime motivators in building a sense of collegiality. Pheromone Therapy was designed to include opportunities for social interaction but students demonstrated how the learning experiences which were situated in practice, with opportunities for shared participation, created the greatest cohesion and sense of community

Stigmergy in Web 2.0: a model for site dynamics

Building Web 2.0 sites does not necessarily ensure the success of the site. We aim to better understand what improves the success of a site by drawing insight from biologically inspired design patterns. Web 2.0 sites provide a mechanism for human interaction enabling powerful intercommunication between massive volumes of users. Early Web 2.0 site providers that were previously dominant are being succeeded by newer sites providing innovative social interaction mechanisms. Understanding what site traits contribute to this success drives research into Web sites mechanics using models to describe the associated social networking behaviour. Some of these models attempt to show how the volume of users provides a self-organising and self-contextualisation of content. One model describing coordinated environments is called stigmergy, a term originally describing coordinated insect behavior. This paper explores how exploiting stigmergy can provide a valuable mechanism for identifying and analysing online user behavior specifically when considering that user freedom of choice is restricted by the provided web site functionality. This will aid our building better collaborative Web sites improving the collaborative processes

Self-organisation in ant-based peer-to-peer systems

Peer-to-peer systems are a highly decentralised form of distributed computing, which has ad¬ vantages of robustness and redundancy over more centralised systems. When the peer-to-peer system has a stable and static population of nodes, variations and bursts in traffic levels cause momentary levels of congestion in the system, which have to be dealt with by routing policies implemented within the peer-to-peer system in order to maintain efficient and effective routes.Peer-to-peer systems, however, are dynamic in nature, as they exhibit churn, i.e. nodes enter and leave the system during their use. This dynamic nature makes it difficult to identify consistent routing policies that ensure a reasonable proportion of traffic in the system is routed successfully to its destination. Studies have shown that chum in peer-to-peer systems is difficult to model and characterise, and further, is difficult to manage.The task of creating and maintaining efficient routes and network topologies in dynamic environments, such as those described above, is one of dynamic optimisation. Complex adap¬ tive systems such as ant colony optimisation and genetic algorithms have been shown to display adaptive properties in dynamic environments. Although complex adaptive systems have been applied to a small number of dynamic optimisation problems, their application to dynamic opti¬ misation problems is new in general and also application to routing in dynamic environments is new. Further, the problem characteristics and conditions under which these algorithms perform well, and the reasons for doing so, are not yet fully understood. The assessment of how good the complex adaptive systems are at creating solutions to the dynamic routing optimisation problem detailed above is dependent on the metrics used to make the measurements.A contribution of this thesis is the development of a theoretical framework within which we can analyse the behaviours and responses of any peer-to-peer system. We do this by considering a peer-to-peer system to be a graph generating algorithm, which has input parameters and has outputs which can be measured using topological metrics and statistics that characterise the traffic through the network. Specifically, we consider the behaviour of an ant-based peer-to-peer system and we have designed and implemented an ant-based peer-to-peer simulator to enable this.Recently methods for characterising graphs by their scaling properties have been developed and a small number of distinct categories of graphs have been identified (such as random graphs, lattices, small world graphs, and scale-free graphs). These graph characterisation methods have also enabled the creation of new metrics to enable measurements of properties of the graphs belonging to different categories.We use these new graph characterisation techniques mentioned above and the associated metrics to implement a systematic approach to the analysis of the behaviour of our ant peer-to-peer system. We present the results of a number of simulation runs of our system initiated with a range of values of key parameters. The resulting networks are then analysed from both the point of view of traffic statistics, and also topological metrics.Three sets of experiments have been designed and conducted using the simulator created during this project. The first set, equilibrium experiments, consider the behaviour of the system when the number of operational nodes in the system is constant and also the demand placed on the system is constant. The second set of experiments considers the changes that occur when there are bursts in traffic levels or the demand placed on the system. The final set considers the effect of churn in the system, where nodes enter and leave the system during its operation. In crafting the experiments we have been able to identify many of the major control parameters of the ant-based peer-to-peer system.A further contribution of this thesis is the results of the experiments which show that under conditions of network congestion the ant peer-to-peer system becomes very brittle. This is characterised by small average path lengths, a low proportion of ants successfully getting through to their destination node, and also a low average degree of the nodes in the network. This brittleness is made worse when nodes fail and also when the demand applied to the system changes abruptly.A further contribution of this thesis is the creation of a method of ranking the topology of a network with respect to a target topology. This method can be used as the basis for topological control (i.e. the distributed self-assembly of network topologies within a peer-to-peer system that have desired topological properties) and assessing how best to modify a topology in order to move it closer to the desired (or reference) topology. We use this method when measuring the outcome of our experiments to determine how far the resulting graph is from a random graph. In principle this method could be used to measure the distance of the graph of the peer-to-peer network from any reference topology (e.g. a lattice or a tree).A final contribution of this thesis is the definition of a distributed routing policy which uses a measure of confidence that nodes in the system are in an operational state when making calculations regarding onward routing. The method of implementing the routing algorithm within the ant peer-to-peer system has been specified, although this has not been implemented within this thesis. It is conjectured that this algorithm would improve the performance of the ant peer-to-peer system under conditions of churn.The main question this thesis is concerned with is how the behaviour of the ant-based peer-to-peer system can best be measured using a simulation-based approach, and how these measurables can be used to control and optimise the performance of the ant-based peer-to-peer system in conditions of equilibrium, and also non-equilibrium (specifically varying levels of bursts in traffic demand, and also varying rates of nodes entering and leaving the peer-to-peer system)

Efficient algorithms for agent-based semantic resource discovery

A semantic overlay network is a powerful mechanism for collaborative environments where multiple agents, managing several resources, can cooperate in pursuing common and individual goals while achieving good overall performance. However, building such a social structure dynamically from an unstructured peer-to-peer network is a lengthy process if appropriate algorithms and techniques are not used. In this paper, we analyse a set of network evolution techniques that improve the performance of classic approaches, such as the flooding search algorithm. We compare the efficiency of these enhanced classic algorithms with our previously proposed search algorithm, which has also been improved through the referred techniques. Evaluation tests show that the improved version of our algorithm outperforms the improved version of the classic search algorithm and efficiently creates a semantic overlay network for agent-based resource coordination.info:eu-repo/semantics/acceptedVersio

A Neural Model for Insect Steering Applied to Olfaction and Path Integration

Many animal behaviors require orientation and steering with respect to the environment. For insects, a key brain area involved in spatial orientation and navigation is the central complex. Activity in this neural circuit has been shown to track the insect’s current heading relative to its environment and has also been proposed to be the substrate of path inte-gration. However, it remains unclear how the output of the central complex is integrated into motor commands. Central complex output neurons project to the lateral accessory lobes (LAL), from which descending neurons project to thoracic motor centers. Here, we present a computational model of a simple neural network that has been described anatomically and physiologically in the LALs of male silkworm moths, in the context of odor-mediated steering. We present and analyze two versions of this network, one rate based and one based on spiking neurons. The mod-eled network consists of an inhibitory local interneuron and a bistable descending neuron (flip-flop) that both receive input in the LAL. The flip-flop neuron projects onto neck motor neurons to induce steering. We show that this simple computational model not only replicates the basic parameters of male silkworm moth behavior in a simulated odor plume but can also take input from a computational model of path integration in the central complex and use it to steer back to a point of origin. Fur-thermore, we find that increasing the level of detail within the model im-proves the realism of the model’s behavior, leading to the emergence of looping behavior as an orientation strategy. Our results suggest that descending neurons originating in the LALs, such as flip-flop neurons, are sufficient to mediate multiple steering behaviors. This study is therefore a first step to close the gap between orientation circuits in the central complex and downstream motor centers