A Survey of Learning Classifier Systems in Games [Review Article]

Shafi, Kamran; Abbass, Hussein A.

doi:10.1109/mci.2016.2627670

Cited by 18 publications

(14 citation statements)

References 108 publications

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“…While some of these issues are minor and could be addressed through adaptivity mechanisms or procedural content generation, other issues may pose health or safety concerns (Jacob et al, 2017). Specifically, techniques such as Action Prediction (McGee and Abraham, 2010), Player Modeling (Yannakakis et al, 2013), Adaptive Pacing (Thompson, 2014), Dynamic Game Difficulty Adjustment (Hawkins et al, 2012), and Learning Classifier Systems (Shafi and Abbass, 2017) have been applied successfully to games and could also be adapted to pervasive games to help mitigate some of these issues.…”

Section: Issues In Serious Pervasive Gamesmentioning

confidence: 99%

Serious Pervasive Games

Coelho

Rodrigues

Nóbrega

et al. 2020

Front. Comput. Sci.

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Serious Pervasive Games extend the magic circle (Huizinga, 1938) to the players' context and surrounding environment. The blend of both physical and fictive game worlds provides a push in player engagement and promotes situated learning approaches. Space and time, as well as social context, acquire a more meaningful impact on the gameplay. From pervasive learning towards science communication with location-based games, this article presents research and case studies that exemplify their benefits and related problems. Pervasive learning can be defined as "learning at the speed of need through formal, informal and social learning modalities" (Pontefract, 2013). The first case study-the BEACONING project-aims to contextualize the teaching and learning process, connecting it with problem-based game mechanics within STEM. The main goal of this project is to provide the missing connection between STEM subjects and real-world interactions and applications. The pedagogical foundation is supported on problem-based learning (PBL), in which active learning is in the center, and learners have to work with different tools and resources in order to solve problems (quests). Teachers create, facilitate, and assess pervasive and gamified learning activities (missions). Furthermore, these quests are gamified in order to provide non-linear game plots. In a second case study, we demonstrate and evaluate how natural heritage can benefit from pervasive games. This study is based on a set of location-based games for an existing natural park, which have been developed in order to provide enhanced experiences, as well as additional information about some species that are more difficult to observe or that are seasonal. Throughout the research and development of these projects, we have encountered and identified several problems, of different nature, present in pervasive games.

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Section: Issues In Serious Pervasive Gamesmentioning

confidence: 99%

Serious Pervasive Games

Coelho

Rodrigues

Nóbrega

et al. 2020

Front. Comput. Sci.

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“…One limitation of such approaches is the lack of capability to learn or adapt to dynamic fault signatures, and rely on comprehensive prior modeling in order to be effective. To retain the advantages of BFVs, we would argue that it is more appropriate for an autonomous fault diagnosis mechanism to establish models of faulty behavior online, in which case the resulting system will bear a closer resemblance to Learning Classifier Systems (Shafi and Abbass, 2017 ) than the supervised learning methods described by Daigle et al ( 2007 ) and Carrasco et al ( 2011 ), and used in our earlier work O'Keeffe et al ( 2017a ). Faults can also be diagnosed through more explicit assessment.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Online Fault Diagnosis in Autonomous Robot Swarms

et al. 2018

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Previous work has shown that robot swarms are not always tolerant to the failure of individual robots, particularly those that have only partially failed and continue to contribute to collective behaviors. A case has been made for an active approach to fault tolerance in swarm robotic systems, whereby the swarm can identify and resolve faults that occur during operation. Existing approaches to active fault tolerance in swarms have so far omitted fault diagnosis, however we propose that diagnosis is a feature of active fault tolerance that is necessary if swarms are to obtain long-term autonomy. This paper presents a novel method for fault diagnosis that attempts to imitate some of the observed functions of natural immune system. The results of our simulated experiments show that our system is flexible, scalable, and improves swarm tolerance to various electro-mechanical faults in the cases examined.

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“…Whether in the past, present, or future, forecasting has been an intriguing and challenging research topic. In ancient societies, due to the limited ability to understand the objective world, people usually used natural signs of change to organize their travels, farming, and harvesting [5]. In the modern society, scholars use various mathematical models to predict the future by collecting historical data and putting them into mathematical models for training and then modifying the corresponding mathematical models by constantly adjusting the errors between the prediction results and the real values.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Algorithm‐Based Financial Prediction Models

Jia

2021

Complexity

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In this paper, a new FEPA portfolio forecasting model is based on the EMD decomposition method. The model is based on the special empirical modal decomposition of financial time series, principal component analysis, and artificial neural network to model and forecast for nonlinear, nonstationary, multiscale complex financial time series to predict stock market indices and foreign exchange rates and empirically investigate this hot area in financial market research. The combined forecasting model proposed in this paper is based on the idea of decomposition-reconstruction synthesis, which effectively improves the model’s prediction of internal financial time series. In this paper, we select the CSI 300 Index and foreign exchange rate as the empirical market and data and establish seven forecasting models to make predictions about the short-term running trend of the closing price. The interval EMD decomposition algorithm is introduced in this paper, considering both high and low prices to be contained in the input and output. By analyzing the closing price, high and low prices of the stock index at the same time, the volatility of this interval time series of the index and its trend can be better captured.

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A Survey of Learning Classifier Systems in Games [Review Article]

Cited by 18 publications

References 108 publications

Serious Pervasive Games

Serious Pervasive Games

Adaptive Online Fault Diagnosis in Autonomous Robot Swarms

Deep Learning Algorithm‐Based Financial Prediction Models

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