Heuristic bidding strategies for multiple heterogeneous auctions

This paper investigates utility maximising bidding heuristics for agents that participate in multiple heterogeneous auctions, in which the auction format and the starting and closing times can be different. Our strategy allows an agent to procure one or more items and to participate in any number of auctions. For this case, forming an optimal bidding strategy by global utility maximisation is computationally intractable, and so we develop two-stage heuristics that first provide reasonable bidding thresholds with simple strategies before deciding which auctions to participate in. The proposed approach leads to an average gain of at least 24% in agent utility over commonly used benchmarks

Consensus Acceleration in Multiagent Systems with the Chebyshev Semi-Iterative Method

We consider the fundamental problem of reaching consensus in multiagent systems; an operation required in many applications such as, among others, vehicle formation and coordination, shape formation in modular robotics, distributed target tracking, and environmental modeling. To date, the consensus problem (the problem where agents have to agree on their reported values) has been typically solved with iterative decentralized algorithms based on graph Laplacians. However, the convergence of these existing consensus algorithms is often too slow for many important multiagent applications, and thus they are increasingly being combined with acceleration methods. Unfortunately, state-of-the-art acceleration techniques require parameters that can be optimally selected only if complete information about the network topology is available, which is rarely the case in practice. We address this limitation by deriving two novel acceleration methods that can deliver good performance even if little information about the network is available. The first proposed algorithm is based on the Chebyshev semi-iterative method and is optimal in a well defined sense; it maximizes the worst-case convergence speed (in the mean sense) given that only rough bounds on the extremal eigenvalues of the network matrix are available. It can be applied to systems where agents use unreliable communication links, and its computational complexity is similar to those of simple Laplacian-based methods. This algorithm requires synchronization among agents, so we also propose an asynchronous version that approximates the output of the synchronous algorithm. Mathematical analysis and numerical simulations show that the convergence speed of the proposed acceleration methods decrease gracefully in scenarios where the sole use of Laplacian-based methods is known to be impractical

Run-Time Selection of Coordination Mechanisms in Multi-Agent Systems

This paper presents a framework that enables autonomous agents to dynamically select the mechanism they employ in order to coordinate their inter-related activities. Adopting this framework means coordination mechanisms move from the realm of being imposed upon the system at design time, to something that the agents select at run-time in order to fit their prevailing circumstances and their current coordination needs. Empirical analysis is used to evaluate the effect of various design alternatives for the agent's decision making mechanisms and for the coordination mechanisms themselves

Detection and emotional evaluation of an electric vehicle’s exterior sound in a simulated environment

Electric vehicles are quiet at low speeds and thus potentially pose a threat to pedestrians’ safety. Laws are formulating worldwide that mandate these vehicles emit sounds to alert the pedestrians of the vehicles’ approach. It is necessary that these sounds promote a positive perception of the vehicle brand, and understanding their impact on soundscapes is also important. Detection time of the vehicle sounds is an important measure to assess pedestrians’ safety. Emotional evaluation of these sounds influences assessment of the vehicle brand. Laboratory simulation is a new approach for evaluating exterior automotive sounds. This study describes the implementation of laboratory simulation to compare the detection time and emotional evaluation of artificial sounds for an electric vehicle. An Exterior Sound Simulator simulated audio-visual stimuli of an electric car passing a crossroad of a virtual town at 4.47 ms-1 (10 mph), from the perspective of a pedestrian standing at the crossroad. In this environment, 15 sounds were tested using experiments where participants detected the car and evaluated its sound using perceptual dimensions. Results show that these sounds vary significantly in their detection times and emotional evaluations, but crucially that traditional metrics like dB(A) do not always relate to the detection of these sounds. Detection time and emotional evaluation do not have significant correlation. Hence, sounds of a vehicle could be detected quickly, but may portray negative perceptions of the vehicle. Simulation provides a means to more fully evaluate potential electric vehicle sounds against the competing criteria

MAGSAT data processing: A report for investigators

The in-flight attitude and vector magnetometer data bias recovery techniques and results are described. The attitude bias recoveries are based on comparisons with a magnetic field model and are thought to be accurate to 20 arcsec. The vector magnetometer bias recoveries are based on comparisons with the scalar magnetometer data and are thought to be accurate to 3 nT or better. The MAGSAT position accuracy goals of 60 m radially and 300 m horizontally were achieved for all but the last 3 weeks of Magsat lifetime. This claim is supported by ephemeris overlap statistics and by comparisons with ephemerides computed with an independent orbit program using data from an independent tracking network. MAGSAT time determination accuracy is estimated at 1 ms. Several errors in prelaunch assumptions regarding data time tags, which escaped detection in prelaunch data tests, and were discovered and corrected postlaunch are described. Data formats and products, especially the Investigator-B tapes, which contain auxiliary parameters in addition to the basic magnetometer and ephemeris data, are described

Breaking the habit: measuring and predicting departures from routine in individual human mobility

Researchers studying daily life mobility patterns have recently shown that humans are typically highly predictable in their movements. However, no existing work has examined the boundaries of this predictability, where human behaviour transitions temporarily from routine patterns to highly unpredictable states. To address this shortcoming, we tackle two interrelated challenges. First, we develop a novel information-theoretic metric, called instantaneous entropy, to analyse an individual’s mobility patterns and identify temporary departures from routine. Second, to predict such departures in the future, we propose the first Bayesian framework that explicitly models breaks from routine, showing that it outperforms current state-of-the-art predictor

Sequential Decision Making with Untrustworthy Service Providers

In this paper, we deal with the sequential decision making problem of agents operating in computational economies, where there is uncertainty regarding the trustworthiness of service providers populating the environment. Specifically, we propose a generic Bayesian trust model, and formulate the optimal Bayesian solution to the exploration-exploitation problem facing the agents when repeatedly interacting with others in such environments. We then present a computationally tractable Bayesian reinforcement learning algorithm to approximate that solution by taking into account the expected value of perfect information of an agent's actions. Our algorithm is shown to dramatically outperform all previous finalists of the international Agent Reputation and Trust (ART) competition, including the winner from both years the competition has been run

Exploring a cardio-thoracic hospital ward soundscape in relation to restoration

Hospitals can provide stressful experiences for both patients and medical staff. A well-designed hospital soundscape should avoid adding to negative emotional states (e.g. stress), limit any detrimental cognitive effects (e.g. attentional fatigue), and enable restoration. Experiences of the cardio-thoracic ward soundscape, in a UK public University hospital, were explored via semi-structured interviews with 11 patients and 16 nurses. Thematic coding analysis resulted in 11 key themes including notions of restoration and emotional responses. The themes were used to develop a conceptual model to describe the processes involved in the perception and evaluation of the soundscape. The language used by patients and nurses indicated the emotional response to the soundscape was at times stressful and at others potentially restorative. Coping methods of accepting and habituating to individual sounds were noted. The impact of the patients' and nurses' ability to maintain these coping strategies are discussed in relation to restoration and the temporal variation of the soundscape. A period of 'quiet time' was in operation at the hospital and the importance of this was noted through various responses relating to emotion and restoration. The results suggest the soundscape has potentially, a beneficial role in facilitating restoration thus helping patients' recovery and medical staff's ability to remain productive. This research supports the need to study hospital soundscapes further so that design implications can be considered for the production of a more restorative environment, possibly through the masking/removal of unwanted sounds and optimising positive sounds

Information Agents for Pervasive Sensor Networks

In this paper, we describe an information agent, that resides on a mobile computer or personal digital assistant (PDA), that can autonomously acquire sensor readings from pervasive sensor networks (deciding when and which sensor to acquire readings from at any time). Moreover, it can perform a range of information processing tasks including modelling the accuracy of the sensor readings, predicting the value of missing sensor readings, and predicting how the monitored environmental parameters will evolve into the future. Our motivating scenario is the need to provide situational awareness support to first responders at the scene of a large scale incident, and we describe how we use an iterative formulation of a multi-output Gaussian process to build a probabilistic model of the environmental parameters being measured by local sensors, and the correlations and delays that exist between them. We validate our approach using data collected from a network of weather sensors located on the south coast of England

Towards Real-Time Information Processing of Sensor Network Data using Computationally Efficient Multi-output Gaussian Processes

In this paper, we describe a novel, computationally efficient algorithm that facilitates the autonomous acquisition of readings from sensor networks (deciding when and which sensor to acquire readings from at any time), and which can, with minimal domain knowledge, perform a range of information processing tasks including modelling the accuracy of the sensor readings, predicting the value of missing sensor readings, and predicting how the monitored environmental variables will evolve into the future. Our motivating scenario is the need to provide situational awareness support to first responders at the scene of a large scale incident, and to this end, we describe a novel iterative formulation of a multi-output Gaussian process that can build and exploit a probabilistic model of the environmental variables being measured (including the correlations and delays that exist between them). We validate our approach using data collected from a network of weather sensors located on the south coast of England