Bihomogeneity of solenoids

Solenoids are inverse limit spaces over regular covering maps of closed manifolds. M.C. McCord has shown that solenoids are topologically homogeneous and that they are principal bundles with a profinite structure group. We show that if a solenoid is bihomogeneous, then its structure group contains an open abelian subgroup. This leads to new examples of homogeneous continua that are not bihomogeneous.Comment: Published by Algebraic and Geometric Topology at http://www.maths.warwick.ac.uk/agt/AGTVol2/agt-2-1.abs.htm

Multi-Objective Calibration For Agent-Based Models

Agent-based modelling is already proving to be an immensely useful tool for scientific and industrial modelling applications. Whilst the building of such models will always be something between an art and a science, once a detailed model has been built, the process of parameter calibration should be performed as precisely as possible. This task is often made difficult by the proliferation of model parameters with non-linear interactions. In addition to this, these models generate a large number of outputs, and their ‘accuracy’ can be measured by many different, often conflicting, criteria. In this paper we demonstrate the use of multi-objective optimisation tools to calibrate just such an agent-based model. We use an agent-based model of a financial market as an exemplar and calibrate the model using a multi-objective genetic algorithm. The technique is automated and requires no explicit weighting of criteria prior to calibration. The final choice of parameter set can be made after calibration with the additional input of the domain expert

Modelling Driver Interdependent Behaviour in Agent-Based Traffic Simulations for Disaster Management

Accurate modelling of driver behaviour in evacuations is vitally important in creating realistic training environments for disaster management. However, few current models have satisfactorily incorporated the variety of factors that affect driver behaviour. In particular, the interdependence of driver behaviours is often seen in real-world evacuations, but is not represented in current state-of-the art traffic simulators. To address this shortcoming, we present an agent-based behaviour model based on the social forces model of crowds. Our model uses utility-based path trees to represent the forces which affect a driver's decisions. We demonstrate, by using a metric of route similarity, that our model is able to reproduce the real-life evacuation behaviour whereby drivers follow the routes taken by others. The model is compared to the two most commonly used route choice algorithms, that of quickest route and real-time re-routing, on three road networks: an artificial "ladder" network, and those of Lousiana, USA and Southampton, UK. When our route choice forces model is used our measure of route similarity increases by 21%-93%. Furthermore, a qualitative comparison demonstrates that the model can reproduce patterns of behaviour observed in the 2005 evacuation of the New Orleans area during Hurricane Katrina

Decentralised Control of Adaptive Sampling in Wireless Sensor Networks

The efficient allocation of the limited energy resources of a wireless sensor network in a way that maximises the information value of the data collected is a significant research challenge. Within this context, this paper concentrates on adaptive sampling as a means of focusing a sensor’s energy consumption on obtaining the most important data. Specifically, we develop a principled information metric based upon Fisher information and Gaussian process regression that allows the information content of a sensor’s observations to be expressed. We then use this metric to derive three novel decentralised control algorithms for information-based adaptive sampling which represent a trade-off in computational cost and optimality. These algorithms are evaluated in the context of a deployed sensor network in the domain of flood monitoring. The most computationally efficient of the three is shown to increase the value of information gathered by approximately 83%, 27%, and 8% per day compared to benchmarks that sample in a naive non-adaptive manner, in a uniform non-adaptive manner, and using a state-of-the-art adaptive sampling heuristic (USAC) correspondingly. Moreover, our algorithm collects information whose total value is approximately 75% of the optimal solution (which requires an exponential, and thus impractical, amount of time to compute)

A Parameterisation of Algorithms for Distributed Constraint Optimisation via Potential Games

This paper introduces a parameterisation of learning algorithms for distributed constraint optimisation problems (DCOPs). This parameterisation encompasses many algorithms developed in both the computer science and game theory literatures. It is built on our insight that when formulated as noncooperative games, DCOPs form a subset of the class of potential games. This result allows us to prove convergence properties of algorithms developed in the computer science literature using game theoretic methods. Furthermore, our parameterisation can assist system designers by making the pros and cons of, and the synergies between, the various DCOP algorithm components clear

Efficient Opinion Sharing in Large Decentralised Teams

In this paper we present an approach for improving the accuracy of shared opinions in a large decentralised team. Specifically, our solution optimises the opinion sharing process in order to help the majority of agents to form the correct opinion about a state of a common subject of interest, given only few agents with noisy sensors in the large team. We build on existing research that has examined models of this opinion sharing problem and shown the existence of optimal parameters where incorrect opinions are filtered out during the sharing process. In order to exploit this collective behaviour in complex networks, we present a new decentralised algorithm that allows each agent to gradually regulate the importance of its neighbours' opinions (their social influence). This leads the system to the optimised state in which agents are most likely to filter incorrect opinions, and form a correct opinion regarding the subject of interest. Crucially, our algorithm is the first that does not introduce additional communication over the opinion sharing itself. Using it 80-90% of the agents form the correct opinion, in contrast to 60-75% with the existing message-passing algorithm DACOR proposed for this setting. Moreover, our solution is adaptive to the network topology and scales to thousands of agents. Finally, the use of our algorithm allows agents to significantly improve their accuracy even when deployed by only half of the team

Self-Organized Routing For Wireless Micro-Sensor Networks

In this paper we develop an energy-aware self-organized routing algorithm for the networking of simple battery-powered wireless micro-sensors (as found, for example, in security or environmental monitoring applications). In these networks, the battery life of individual sensors is typically limited by the power required to transmit their data to a receiver or sink. Thus effective network routing algorithms allow us to reduce this power and extend both the lifetime and the coverage of the sensor network as a whole. However, implementing such routing algorithms with a centralized controller is undesirable due to the physical distribution of the sensors, their limited localization ability and the dynamic nature of such networks (given that sensors may fail, move or be added at any time and the communication links between sensors are subject to noise and interference). Against this background, we present a distributed mechanism that enables individual sensors to follow locally selfish strategies, which, in turn, result in the self-organization of a routing network with desirable global properties. We show that our mechanism performs close to the optimal solution (as computed by a centralized optimizer), it deals adaptively with changing sensor numbers and topology, and it extends the useful life of the network by a factor of three over the traditional approach

Learn While You Earn: Two Approaches to Learning Auction Parameters in Take-it-or-leave-it Auctions

Much of the research in auction theory assumes that the auctioneer knows the distribution of participants ’ valuations with complete certainty. However, this is unrealistic. Thus, we analyse cases in which the auctioneer is uncertain about the valuation distributions; specifically, we consider a repeated auction setting in which the auctioneer can learn these distributions. Using take-it-or-leave-it auctions (Sandholm and Gilpin, 2006) as an exemplar auction format, we consider two auction design criteria. Firstly, an auctioneer could maximise expected revenue each time the auction is held. Secondly, an auctioneer could maximise the information gained in earlier auctions (as measured by the Kullback-Liebler divergence between its posterior and prior) to develop good estimates of the unknowns, which are later exploited to improve the revenue earned in the long-run. Simulation results comparing the two criteria indicate that setting offers to maximise revenue does not significantly detract from learning performance, but optimising offers for information gain substantially reduces expected revenue while not producing significantly better parameter estimates

Improving location prediction services for new users with probabilistic latent semantic analysis

Location prediction systems that attempt to determine the mobility patterns of individuals in their daily lives have become increasingly common in recent years. Approaches to this prediction task include eigenvalue decomposition [5], non-linear time series analysis of arrival times [10], and variable order Markov models [1]. However, these approachesall assume sufficient sets of training data. For new users, by definition, this data is typically not available, leading to poor predictive performance. Given that mobility is a highly personal behaviour, this represents a significant barrier to entry. Against this background, we present a novel framework to enhance prediction using information about the mobility habits of existing users. At the core of the framework is a hierarchical Bayesian model, a type of probabilistic semantic analysis [7], representing the intuition that the temporal features of the new user’s location habits are likely to be similar to those of an existing user in the system. We evaluate this framework on the real life location habits of 38 users in the Nokia Lausanne dataset, showing that accuracy is improved by 16%, relative to the state of the art, when predicting the next location of new users