Behaviourally cloning river raid agents

We investigate the feasibility and difficulties of using behavioural cloning to obtain player models using the 1982 video game River Raid. We attempt to clone both virtual game-playing agents (a fixed (non-improving) reinforcement learning agent and a random agent sampling actions uniformly) as well as an actual human agent. The behavioural clones' performance is evaluated on the micro-level through comparison of the state-conditioned and unconditional action distributions, and on the macro-level by comparing the (cloned) agents' survival time and score per episode. Using our methodology, cloning virtual agents seems feasible to varying extents, even with somewhat limited amounts of data. However, our method fails to create reliable behavioural clones of human players. We conclude with a discussion of some of the more important reasons that might cause this: a lack of training data, the problem of covariate shift, and improving and inconsistent play-style over time

The Forecastability of Underlying Building Electricity Demand from Time Series Data

Forecasting building energy consumption has become a promising solution in Building Energy Management Systems for energy saving and optimization. Furthermore, it can play an important role in the efficient management of the operation of a smart grid. Different data-driven approaches to forecast the future energy demand of buildings at different scale, and over various time horizons, can be found in the scientific literature, including extensive Machine Learning and Deep Learning approaches. However, the identification of the most accurate forecaster model which can be utilized to predict the energy demand of such a building is still challenging.In this paper, the design and implementation of a data-driven approach to predict how forecastable the future energy demand of a building is, without first utilizing a data-driven forecasting model, is presented. The investigation utilizes a historical electricity consumption time series data set with a half-hour interval that has been collected from a group of residential buildings located in the City of London, United Kingdo

Adversarial Stacked Auto-Encoders for Fair Representation Learning

Training machine learning models with the only accuracy as a final goal may promote prejudices and discriminatory behaviors embedded in the data. One solution is to learn latent representations that fulfill specific fairness metrics. Different types of learning methods are employed to map data into the fair representational space. The main purpose is to learn a latent representation of data that scores well on a fairness metric while maintaining the usability for the downstream task. In this paper, we propose a new fair representation learning approach that leverages different levels of representation of data to tighten the fairness bounds of the learned representation. Our results show that stacking different auto-encoders and enforcing fairness at different latent spaces result in an improvement of fairness compared to other existing approaches.Comment: ICML2021 ML4data Workshop Pape

Measuring Business Performance: Comparison of Financial, Non Financial and Qualitative Indicators

Purpose: To review research contributions to performance measurement systems. Design/methodology/approach:Critical review of the literature using the systems developed by previous contributors: performance measurement systems; financial measures v/s nonfinancial measures; quantification of qualitative performance indicators; and generalization v/s specification in performance measurement system. Findings:The absence of unified performance measurement systems means that the existing literature is capturing a wide range of financial measures and qualitative specifications. As a result, performance measurement system appears scattered rather than summative. New measurement systems are needed for correct measurement of performance comprising both financial variables with nonfinancial variables and also inclusion of qualitative perspective is inevitable. Research limitations/implications: Similar researches have suggested performance measurement systems must always be tailored according to requirement of assessment entity. Empirical work must explain the measurement of performance explicitly. Originality/value:This paper synthesizes the existing literature in the area of performance measurement systems that has been critical for the performance evaluator in terms of advice given to strategic manager, business owners and policy makers. Keywords: Performance Measurement Systems, Financial Performance, Non-Financial Performance

Teaching the \u27comprehensive dental care\u27 in formative years of education and training: A new model for dental internship

There is a need of a new model of education and training to be implemented in the Bachelors of Dental Surgery curriculum in the relevant Pakistani institutions. The current review article was planned to suggest such a model in the light of literature aimed at building the capacity of dental graduates in a competency-driven approach with the objective of offering safe, efficient and comprehensive care to dental patients. The outcome of the reforms suggested shall prepare dental graduates suitably geared towards providing community-oriented family dental care right from their formative years. Moreover, the suggested internship model can also help to address the issue of inefficiency related to patient-care

Compact rover surveying and laser scanning for BIM development

This paper presents a custom made small rover based surveying, mapping and building information modeling solution. Majority of the commercially available mobile surveying systems are larger in size which restricts their maneuverability in the targeted indoor vicinities. Furthermore their functional cost is unaffordable for low budget projects belonging to developing markets. Keeping in view these challenges, an economical indigenous rover based scanning and mapping system has developed using orthogonal integration of two low cost RPLidar A1 laser scanners. All the instrumentation of the rover has been interfaced with Robot Operating System (ROS) for online processing and recording of all sensorial data. The ROS based pose and map estimations of the rover have performed using Simultaneous Localization and Mapping (SLAM) technique. The perceived class 1 laser scans data belonging to distinct vicinities with variable reflective properties have been successfully tested and validated for required structural modeling. Systematically the recorded scans have been used in offline mode to generate the 3D point cloud map of the surveyed environment. Later the structural planes extraction from the point cloud data has been done using Random Sampling and Consensus (RANSAC) technique. Finally the 2D floor plan and 3D building model have been developed using point cloud processing in appropriate software. Multiple interiors of existing buildings and under construction indoor sites have been scanned, mapped and modelled as presented in this paper. In addition, the validation of the as-built models have been performed by comparing with the actual architecture design of the surveyed buildings. In comparison to available surveying solutions present in the local market, the developed system has been found faster, accurate and user friendly to produce more enhanced structural results with minute details