
Contents
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11.1 Probabilistic planning-based approach 11.1 Probabilistic planning-based approach
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11.1.1 The approach 11.1.1 The approach
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11.1.2 What does this mean for current commercial games? 11.1.2 What does this mean for current commercial games?
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11.1.3 Applying this approach to games 11.1.3 Applying this approach to games
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11.2 Bayesian Networks (BNs) or Dynamic Bayesian Networks (DBNs) 11.2 Bayesian Networks (BNs) or Dynamic Bayesian Networks (DBNs)
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11.2.1 The approach 11.2.1 The approach
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11.2.2 What does this mean for current commercial games? 11.2.2 What does this mean for current commercial games?
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11.2.3 Applying this approach to games 11.2.3 Applying this approach to games
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11.3 Hidden Markov Models (HMMs) 11.3 Hidden Markov Models (HMMs)
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11.3.1 The approach 11.3.1 The approach
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11.3.2 What does this mean for current commercial games? 11.3.2 What does this mean for current commercial games?
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11.3.3 Applying this approach to games 11.3.3 Applying this approach to games
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11.4 Markov Decision Process (MDP) 11.4 Markov Decision Process (MDP)
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11.4.1 The approach 11.4.1 The approach
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11.4.2 What does this mean for current commercial games? 11.4.2 What does this mean for current commercial games?
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11.4.3 Applying this approach to games 11.4.3 Applying this approach to games
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11.4.4 An extension of this approach—POMDP 11.4.4 An extension of this approach—POMDP
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11.4.5 Can we apply POMDP in current commercial games? 11.4.5 Can we apply POMDP in current commercial games?
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11.5 Markov Logic Networks (MLNs) 11.5 Markov Logic Networks (MLNs)
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11.5.1 The approach 11.5.1 The approach
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11.5.2 What does this mean for current commercial games? 11.5.2 What does this mean for current commercial games?
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11.5.3 Applying this approach to games 11.5.3 Applying this approach to games
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11.6 Recurrent Neural Networks (RNNs) and Deep Recurrent Neural Networks (DRNNs) 11.6 Recurrent Neural Networks (RNNs) and Deep Recurrent Neural Networks (DRNNs)
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11.6.1 The approach 11.6.1 The approach
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11.6.2 What does this mean for current commercial games? 11.6.2 What does this mean for current commercial games?
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11.6.3 Applying RNN to games 11.6.3 Applying RNN to games
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11.7 Summary 11.7 Summary
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Acknowledgments Acknowledgments
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Bibliography Bibliography
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12 Case Study: Social Network Analysis Applied to In-game Communities to Identify Key Social Players
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11 Advanced Sequence Analysis
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Published:October 2021
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Abstract
This chapter discusses more advanced methods for sequence analysis. These include: probabilistic methods using classical planning, Bayesian Networks (BN), Dynamic Bayesian Networks (DBNs), Hidden Markov Models (HMMs), Markov Logic Networks (MLNs), Markov Decision Process (MDP), and Recurrent Neural Networks (RNNs), specifically concentrating on LSTM (Long Short-Term Memory). These techniques are all great but, at this time, are mostly used in academia and less in the industry. Thus, the chapter takes a more academic approach, showing the work and its application to games when possible. The techniques are important as they cultivate future directions of how you can think about modeling, predicting players’ strategies, actions, and churn. We believe these methods can be leveraged in the future as the field advances and will have an impact in the industry. Please note that this chapter was developed in collaboration with several PhD students at Northeastern University, specifically Nathan Partlan, Madkour Abdelrahman Amr, and Sabbir Ahmad, who contributed greatly to this chapter and the case studies discussed.
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