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

Mechanism design for eliciting probabilistic estimates from multiple suppliers with unknown costs and limited precision

This paper reports on the design of a novel two-stage mechanism, based on strictly proper scoring rules, that allows a centre to acquire a costly probabilistic estimate of some unknown parameter, by eliciting and fusing estimates from multiple suppliers. Each of these suppliers is capable of producing a probabilistic estimate of any precision, up to a privately known maximum, and by fusing several low precision estimates together the centre is able to obtain a single estimate with a specified minimum precision. Specifically, in the mechanism's first stage M from N agents are pre-selected by eliciting their privately known costs. In the second stage, these M agents are sequentially approached in a random order and their private maximum precision is elicited. A payment rule, based on a strictly proper scoring rule, then incentivises them to make and truthfully report an estimate of this maximum precision, which the centre fuses with others until it achieves its specified precision. We formally prove that the mechanism is incentive compatible regarding the costs, maximum precisions and estimates, and that it is individually rational. We present empirical results showing that our mechanism describes a family of possible ways to perform the pre-selection in the first stage, and formally prove that there is one that dominates all others

It’s not the model that doesn’t fit, it’s the controller! The role of cognitive skills in understanding the links between natural mapping, performance, and enjoyment of console video games

This study examines differences in performance, frustration, and game ratings of individuals playing first person shooter video games using two different controllers (motion controller and a traditional, pushbutton controller) in a within-subjects, randomized order design. Structural equation modeling was used to demonstrate that cognitive skills such as mental rotation ability and eye/hand coordination predicted performance for both controllers, but the motion control was significantly more frustrating. Moreover, increased performance was only related to game ratings for the traditional controller input. We interpret these data as evidence that, contrary to the assumption that motion controlled interfaces are more naturally mapped than traditional push-button controllers, the traditional controller was more naturally mapped as an interface for gameplay

Resource-Aware Junction Trees for Efficient Multi-Agent Coordination

In this paper we address efficient decentralised coordination of cooperative multi-agent systems by taking into account the actual computation and communication capabilities of the agents. We consider coordination problems that can be framed as Distributed Constraint Optimisation Problems, and as such, are suitable to be deployed on large scale multi-agent systems such as sensor networks or multiple unmanned aerial vehicles. Specifically, we focus on techniques that exploit structural independence among agents’ actions to provide optimal solutions to the coordination problem, and, in particular, we use the Generalized Distributive Law (GDL) algorithm. In this settings, we propose a novel resource aware heuristic to build junction trees and to schedule GDL computations across the agents. Our goal is to minimise the total running time of the coordination process, rather than the theoretical complexity of the computation, by explicitly considering the computation and communication capabilities of agents. We evaluate our proposed approach against DPOP, RDPI and a centralized solver on a number of benchmark coordination problems, and show that our approach is able to provide optimal solutions for DCOPs faster than previous approaches. Specifically, in the settings considered, when resources are scarce our approach is up to three times faster than DPOP (which proved to be the best among the competitors in our settings)

A classification of New Zealand’s terrestrial ecosystems

This study produces a comprehensive terrestrial ecosystem classification by subjectively constructing a heirarchy of perceived key environmental drivers. Introduction: The ecosystem concept is at the centre of international agreements, New Zealand legislation, and modern policy and planning systems that aim to sustainably manage natural resources. All definitions of ecosystems include the concept of the physical environment being integrated with its biotic components. Functionally, the concept embodies disturbance cycles, and flows of energy, nutrients and non-living materials, with these processes underpinning the concept of ecosystem health or integrity. Since these processes operate at variable spatio-temporal scales, and species and communities intergrade variably along environmental gradients, there is no single optimal scale at which to apply the ecosystem concept. Rather, the openness and hierarchical nature of ecosystem processes lead to any one classification scale being viewed as nested within coarser and finer scale components. One of the goals of the New Zealand Biodiversity Strategy is to ‘maintain and restore a full range of remaining habitats and ecosystems …’. However, although many environmental agencies and individuals can contribute to this goal, any investment decisions are currently being made in the absence of a comprehensive list of ecosystems or a systematic threat ranking. Therefore, classification of the full range of ecosystem types for New Zealand is overdue.   &nbsp

Knapsack based Optimal Policies for Budget-Limited Multi-Armed Bandits

In budget-limited multi-armed bandit (MAB) problems, the learner's actions are costly and constrained by a fixed budget. Consequently, an optimal exploitation policy may not be to pull the optimal arm repeatedly, as is the case in other variants of MAB, but rather to pull the sequence of different arms that maximises the agent's total reward within the budget. This difference from existing MABs means that new approaches to maximising the total reward are required. Given this, we develop two pulling policies, namely: (i) KUBE; and (ii) fractional KUBE. Whereas the former provides better performance up to 40% in our experimental settings, the latter is computationally less expensive. We also prove logarithmic upper bounds for the regret of both policies, and show that these bounds are asymptotically optimal (i.e. they only differ from the best possible regret by a constant factor)

A Hierarchical Bayesian Trust Model based on Reputation and Group Behaviour

In many systems, agents must rely on their peers to achieve their goals. However, when trusted to perform an action, an agent may betray that trust by not behaving as required. Agents must therefore estimate the behaviour of their peers, so that they may identify reliable interaction partners. To this end, we present a Bayesian trust model (HABIT) for assessing trust based on direct experience and (potentially unreliable) reputation. Although existing approaches claim to achieve this, most rely on heuristics with little theoretical foundation. In contrast, HABIT is based on principled statistical techniques; can be used with any representation of behaviour; and can assess trust based on observed similarities between groups of agents. In this paper, we describe the theoretical aspects of the model and present experimental results in which HABIT was shown to be up to twice as accurate at predicting trustee performance as an existing state-of-the-art trust model

Cooperatives for demand side management

We propose a new scheme for efficient demand side management for the Smart Grid. Specifically, we envisage and promote the formation of cooperatives of medium-large consumers and equip them (via our proposed mechanisms) with the capability of regularly participating in the existing electricity markets by providing electricity demand reduction services to the Grid. Based on mechanism design principles, we develop a model for such cooperatives by designing methods for estimating suitable reduction amounts, placing bids in the market and redistributing the obtained revenue amongst the member agents. Our mechanism is such that the member agents have no incentive to show artificial reductions with the aim of increasing their revenue

Fun Versus Meaningful Video Game Experiences: A Qualitative Analysis of User Responses

Emerging research on video games has suggested that feelings of both enjoyment and meaningfulness can be elicited from gameplay. Studies have shown enjoyment and meaningfulness evaluations to be associated with discrete elements of video games (ratings of gameplay and narrative, respectively), but have relied on closed-end data analysis. The current study analyzed participants’ open-ended reviews of either their “most fun” or “most meaningful” video game experience (N = 575, randomly assigned to either condition). Results demonstrated that “fun” games were explained in terms of gameplay mechanics, and “meaningful” games were explained in terms of connections with players and in-game characters

Mechanism design for eliciting probabilistic estimates from multiple suppliers with unknown costs and limited precision

This paper reports on the design of a novel two-stage mechanism, based on strictly proper scoring rules, that allows a centre to acquire a costly probabilistic estimate of some unknown parameter, by eliciting and fusing estimates from multiple suppliers. Each of these suppliers is capable of producing a probabilistic estimate of any precision, up to a privately known maximum, and by fusing several low precision estimates together the centre is able to obtain a single estimate with a specified minimum precision. Specifically, in the mechanism's first stage M from N agents are pre-selected by eliciting their privately known costs. In the second stage, these M agents are sequentially approached in a random order and their private maximum precision is elicited. A payment rule, based on a strictly proper scoring rule, then incentivises them to make and truthfully report an estimate of this maximum precision, which the centre fuses with others until it achieves its specified precision. We formally prove that the mechanism is incentive compatible regarding the costs, maximum precisions and estimates, and that it is individually rational. We present empirical results showing that our mechanism describes a family of possible ways to perform the pre-selection in the first stage, and formally prove that there is one that dominates all others