Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

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Flow The Psychology Of Optimal Experience

Computers are often used as tools to design, implement, and even visualize a variety of narrative forms. Many researchers and artists are now further attempting to engage the computer actively throughout the development of the narrative itself. Any form of computational narrative authoring is at some level always mixed-initiative , meaning that the processing capabilities of the computer are utilized with a varying degree to automate certain features of the authoring process. We structure this survey by focusing on two key components of stories, plot and space , and more specifically the degree to which these are either automated by the computer or authored manually . By examining the successes of existing research, we identify potential new research directions in the field of computational narrative. We also identify the advantages of developing a standard model of narrative to allow for collaboration between plot and space automation techniques. This would likely benefit the field of automated space generation with the strengths in the field of automated plot generation.

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A Survey on Story Generation Techniques for Authoring Computational Narratives

Transportation cyber–physical systems (TCPSs) require simulation-based testing and evaluation due to the prohibitive cost of building realistic test beds. Given the transdisciplinary nature of TCPSs, various simulation models and frameworks have been proposed in civil engineering, computer science, and related fields. Traditionally, researchers in different areas have developed their own set of simulation tools, which provide limited capability for TCPS research. In recent years, we have witnessed a growing interest of combining two or more features of traditional simulators to capture the unique characteristics of TCPSs. In this paper, we describe several mainstream simulation models used in transportation, communication, and human-factor studies in TCPS research. Moreover, we present our unique design and implementation of an integrated traffic–driving–network simulator (ITDNS). Finally, we discuss future enhancements that will promote best simulation practices for TCPS research.

Simulation-Based Testing and Evaluation Tools for Transportation Cyber–Physical Systems

The ability to engage and retain players is perceived as a major factor in the success of games. However, the end-goal of retention differs between entertainment and serious contexts. For an entertainment game, engagement and retention are linked to monetization; for a serious game, this needs to persist for as long as is required for learning or behavioral objectives to be met. User engagement is strongest when a balance is achieved between difficulty and skill, leading to a state of “flow.” Hence, adapting difficulty could lead to increased and sustained engagement. Implementing this requires the identification of variables linked to mechanics, manipulated based upon a player performance model. In some cases, this is possible by adjusting simple properties of objects, though more comprehensive solutions require extending or adapting content applying procedural techniques. This paper proposes a six step plan, validated against two case studies: an existing serious game, with easily manipulated parameters, and a platformer game built from scratch, where additional content is required, showing the process for different mechanics. To explore limitations, the results of two small-scale user evaluations with 45 users in total, are reported, contributing to the understanding of how adaptive difficulty might be implemented and received.

Implementing Adaptive Game Difficulty Balancing in Serious Games

In recent years, the use of computer-based simulations in the transportation domain has become increasingly important to analyze and test measures for Intelligent Transport System (ITS) policies. Simulators were built to address several aspects of transport, including traffic, driving experience, and pedestrian behavior. However, as the majority of available simulators are single-user stand-alone systems, traffic engineers cannot easily analyze more complex phenomena, such as the interaction between multiple human drivers or pedestrians. Furthermore, this limitation makes it difficult to collect large-scale behavioral data, which is necessary to draw valid conclusions on driving behavior. Emerging virtual world technology offers a convenient alternative. As a networked multi-user framework that allows users to immerse in the virtual world via a graphical self-representation (an 'avatar'), it allows to develop integrated simulation applications that are conveniently accessible by Internet. In this paper, we present OpenEnergySim, a virtual world based visualization application that integrates traffic simulation and immersive multi-user driving.

OpenEnergySim: A 3D Internet Based Experimental Framework for Integrating Traffic Simulation and Multi-User Immersive Driving

iCO2 is a networked game that aims to collect human driving behavior data at scale and help researchers better understand how people apply eco-driving practices. iCO2 is an integrated multiplatform game that can be accessed via Web client on Facebook and iPad clients, and supports a shared traffic experience among its users. Here, the authors describe iCO2 and discuss two key efforts and their results. First, they investigated the scalability of Distributed Virtual Environments (DiVE), the networking framework they developed for multiuser 3D virtual world applications. The "area of interest" method that DiVE uses was tested in a laboratory setting and showed promising results on two important metrics: bandwidth and frames per second. Second, they ran a small marketing campaign to collect data from users interacting with the iPad version of iCO2 "in the wild." They then analyzed the users' acceleration behavior, an important aspect of eco-driving.

iCO2: A Networked Game for Collecting Large-Scale Eco-Driving Behavior Data

We present DiVE, a networking framework for the rapid development of massively multiuser three-dimensional (3D) virtual world applications. The primary contribution of the paper is twofold. First, we investigate zone-based area of interest (AOI) techniques aimed at improving DiVE’s scalability. Specifically, we present an interest management technique that is based on dividing the world into two-dimensional (2D) zones and a ‘double’ (inner/outer) AOI concept. Here the novelty is the investigation of different movement patterns of client entities to determine the optimal AOI/zone ratio. The empirical evaluation of our approach shows promising results on four metrics: bandwidth, frames per second, CPU, and RAM. Second, we extend the AOI concept from 2D space to 3D space, which has not yet been addressed in the literature. Among others, 3D space suggests interesting applications for the safety of unmanned aerial vehicles. Besides the investigation of the optimal size of volumetric AOI, we also consider different forms of volumetric zone alignment. The secondary contribution consists in a technical description of the DiVE framework, which has already been used successfully in multiuser applications in the traffic and evacuation domains.

Evaluation of 2D and 3D interest management techniques in the distributed virtual environment DiVE

Multi-agent systems have already been successfully applied to a variety of traffic control problems and demonstrated the potential to lower travel times and environmental impact. Sharing this goal, we have developed iCO2, an online tool for training eco-friendly driving in a multi-user three-dimensional environment. iCO2 supports eco-driving practice by instructing computer-controlled agents, such as traffic lights and other vehicles, to create traffic situations that make eco-driving more difficult. Hence the agents take the role of "opponents" that try to achieve the optimal challenge level for the skill level of each user. The research challenge is to find the optimal challenge level for all user drivers in a shared simulation space that (1) involves both controllable entities ("opponents") and non-controllable entities (users) and (2) is highly dynamic, with dependencies between entities being created and destroyed in real time. We try to solve this problem by modeling the scenario as a distributed constraint optimization problem (DCOP). The main contribution of our paper is the application of a DCOP algorithm to such a new type of application scenario. We evaluate our approach by running scenarios both in terms of speed and optimality of the solutions proposed by the DCOP algorithm.

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iCO2: multi-user eco-driving training environment based on distributed constraint optimization

Multiplayer games are an important and popular game mode for networked players. Since games are played by a diverse audience, it is important to scale the difficulty, or challenge, according to the skill level of the players. However, current approaches to real-time challenge balancing (RCB) in games are only applicable to single-player scenarios. In multiplayer scenarios, players with different skill levels may be present in the same area, and hence adjusting the game difficulty to match the skill of one player may affect the other players in an undesirable way. To address this problem, we have previously developed a new approach based on distributed constraint optimization, which achieves the optimal challenge level for multiple players in real-time. The main contribution of this paper is an experiment that was performed with our new multiplayer real-time challenge balancing method applied to eco-driving. The results of the experiment suggest the effectiveness of RCB.

Marconi Madruga

Papers

OpenEnergySim: A 3D Internet Based Experimental Framework for Integrating Traffic Simulation and Multi-User Immersive Driving

iCO2: A Networked Game for Collecting Large-Scale Eco-Driving Behavior Data

Evaluation of 2D and 3D interest management techniques in the distributed virtual environment DiVE

iCO2: multi-user eco-driving training environment based on distributed constraint optimization

Extending Real-Time Challenge Balancing to Multiplayer Games: A Study on Eco-Driving