Top 3 Essential NLP Models for Gaming Development; One is a Must-Learn

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How to develop and deploy games with never before seen realism using these 3 most important NLP approaches, with one being a must-learn

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3 Essential NLP Methods for Gaming Development, Simply Explained

Essentially, the idea behind zero-shot learning is what it becomes: the knowledge about the labeled instances acquired during training (it can be confusing compared to deep learning.) On the other hand, deep learning is the knowledge about the unlabeled training instances learned during the evaluation phase [1].

NLP (natural language processing) is at the intersection of computer science, artificial intelligence, and linguistics concerned with the interactions between computers and human (natural) languages.

Game letters on cubes
By Andrey Metelev from Unsplash

I predict the future of gaming will be concentrated around high degrees of empathy such that the player would feel the authenticity, optimized sincerity, and naturalness of the AI models (deployed to interact with the user).

With the ever-growing popularity of video games, there has been an increase in demand for more realistic and lifelike gaming experiences. This has led to developers looking into new ways to create more believable game worlds as well as create characters that players can empathize with.

We can anticipate a significant penetration of user-AI interactions in gaming in things like:

— Classifying player speech into categories such as commands, questions, or overall appropriateness in the language being used

Board game characters
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— Understanding passages of text to distill key information for the player

To decompose further

— NLP techniques can be used to help analyze a player’s dialogue and gestures within the game to provide better feedback during gameplay. As an illustration, if a player consistently uses profanity while playing a certain game character, that information could be conveyed back to the code applied for near real-time adjustments.

By understanding how players engage with one another and what they state (in writing or verbally, for instance), NLP can help create NPCs that respond realistically to player input. This makes for a more immersive and believable experience for the player.

— NLP can be implemented to generate new quests or objectives based on player behavior. By tracking what players do in-game, NPL can identify patterns and offer quests or goals tailored specifically for each player. This latter implementation method helps keep players engaged by giving them dynamic content that changes as they play through the game.

Here are the three NLP methods.

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This method tags each word in a sentence with its appropriate grammatical category. For instance, “I” would be tagged as a pronoun, “write” as a verb, and “the” as an article. This information can be applied to identify the syntactic structure of a player’s spoken or written sentence (in the context of which words are acting together to form phrases).

Three algorithmic examples

The Hidden Markov Model (HMM) [5], Maximum Entropy Model (MEM) [6], and Support Vector Machine (SVM) [7]. Here are simple explanations of each (they may or may not directly correlate with NLP — these below are succinct descriptors of each):

A statistical model, consider chess such that it can be applied for the subsequent moves on the board based on all the known positions.

The MEM is used to predict how likely it is that a player will make a certain move based on their past behavior [9]. This can be deployed to find the most likely explanation for a given set of data or observations. Back to the HMM chess use case, MEM could be used to calculate the probability that a certain sequence of moves [10] will lead to checkmate in chess, given past games where checkmate was achieved.

The SVM is used to determine whether two players are equally matched or if one player has an advantage over another [11]. Classification and regression challenges are use cases, such as identifying different objects in a scene or facial recognition systems.

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This method examines specific types of entities in text, such as proper names (e.g., people or places), organization names, and numeric expressions (dates or monetary values). Recognizing these entities can be useful for game development tasks such as automatically generating quest dialogues or extracting data from unstructured text sources (such as item descriptions scraped from websites in accordance with the storyline in the game).

Three algorithmic examples for an approach can include RNN and CRF.

— RNN: A recurrent neural network (RNN) models text by considering the ordering of words [12]. An RNN can learn long-term dependencies between words in a text [13]. Since they can process sequential data, such as text, such capability makes them well-suited for tasks like named entity recognition, where we need to identify entities in a sentence based on the context around them.

— CRF: A conditional random field (CRF) [14] can account for both local information (individual words and their tags) as well as global information (the sequence of tags). CRFs can be applied with unknown data (labeling), such as predicting part-of-speech tags or named entities [15].

Dice falling out of a bag
By Alperen Yazgı from Unsplash

Other algorithmic approaches

Convolutional neural networks (CNNs) are like RNNs, but they operate on whole sentences or paragraphs at once instead of processing sequentially [16]. This means they can learn global features about the input data and may be better suited for certain types of named entity recognition tasks. Finally, another algorithm that has shown promise for named entity recognition is long short-term memory Networks (LSTMs). LSTMs are a type of recurrent neural network [3] that have been designed to model long-range dependencies in sequential data [17]. They can perform well compared to other methods on some natural language processing tasks, including named entity recognition.

Board game in early evening sunlight.
By Christopher Paul High from Unsplash

This is my must-learn: use this approach to determine whether some piece of text expresses positive/negative opinions about topics or conversations (versus how neutral the input from the player may be).

Sentiment analysis is “a natural language processing technique that identifies the polarity of a given text” [2][4].

While not strictly required for all games, many modern titles make use of player feedback to improve future versions; consequently, being able accurately to receive and process user reviews can give developers valuable insights into where their product needs improvement.

Capabilities to activate

— Markov Decision Process (MDP): They are a mathematical framework for modeling decision-making problems and can be activated to solve sentiment analysis [18] use cases in gaming development.

-Monte Carlo Tree Search (MCTS): An optimization technique to quickly find near-optimal solutions to difficult problems. It has been applied successfully to numerous domains, including game AI and robotics control [19].

— Reinforcement Learning (RL); RL algorithms learn by trial and error, using feedback from the environment to improve their performance over time.

Puzzle pieces on a game board
By Christopher Paul High from Unsplash

I touched upon the impact of sentiment analysis, named entity recognition, and part-of-speech tagging for how these fit into gaming development based on natural language processing.

Please share your thoughts with me if you recommend any edits for this post or recommendations on further expanding this topic area.

References:

1. Tilbe, Anil (2022, July 21). Zero-shot learning deep dive. Towards AI. https://pub.towardsai.net/zero-shot-learning-deep-dive-how-to-select-one-and-challenges-60ae243e040a

2. Tilbe, Anil (2022, July 26). 16 NLP models for sentiment analysis. Towards AI. https://pub.towardsai.net/16-open-source-nlp-models-for-sentiment-analysis-one-rises-on-top-b5867e247116

3. LH — -Computational Tutorial: An Introduction to LSTMs in TensorFlow. https://cbmm.mit.edu/learning-hub/tutorials/computational-tutorial/introduction-lstms-tensorflow

4. Getting Started with Sentiment Analysis using Python. https://huggingface.co/blog/sentiment-analysis-python

5. Morwal, S., Jahan, N., & Chopra, D. (n.d.). Named Entity Recognition using Hidden Markov Model (HMM). Retrieved August 2, 2022, from https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3758852

6. Bishop et al. Maximum entropy spectral analysis and autoregressive decomposition. https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/RG013i001p00183

7. Noble. (n.d.). What is a support vector machine? Nature Biotechnology, 24(12), 1565–1567. https://doi.org/10.1038/nbt1206-1565

8. Mount, D. W. (2009). Using hidden markov models to align multiple sequences. Cold Spring Harbor Protocols, 2009(7). https://doi.org/10.1101/pdb.top41

9. Predictive modeling for archaeological site locations: Comparing logistic regression and maximal entropy in north Israel and north-east China. (n.d.). Journal of Archaeological Science, 92, 28–36. https://doi.org/10.1016/j.jas.2018.02.001

10. Time series modelling and maximum entropy. (n.d.). Physics of the Earth and Planetary Interiors, 12(2–3), 188–200. https://doi.org/10.1016/0031-9201(76)90047-9

11. Vision based Human Activity Recognition using Deep Transfer Learning and Support Vector Machine. (n.d.). IEEE Xplore. Retrieved August 2, 2022, from https://ieeexplore.ieee.org/abstract/document/9667661/

12. Zaremba, W., Sutskever, I., & Vinyals, O. (2014, September 8). Recurrent neural network regularization. ArXiv.Org. https://arxiv.org/abs/1409.2329

13. Learning long-term dependencies in NARX recurrent neural networks. (n.d.). IEEE Xplore. Retrieved August 2, 2022, from https://ieeexplore.ieee.org/abstract/document/548162

14. Manning, J. R. F. ; A. K. ; C. D. (n.d.). Efficient, feature-based, conditional random field parsing. https://aclanthology.org/P08-1109.pdf

15. Gali et al. Part-Of-Speech Tagging for Gujarati Using Conditional Random Fields. https://aclanthology.org/I08-3019.pdf

16. Kalchbrenner, N., Grefenstette, E., & Blunsom, P. (2014, April 8). A convolutional neural network for modelling sentences. ArXiv.Org. https://arxiv.org/abs/1404.2188

17. Tilbe, Anil. (2022, July 24). 10 most important recurrent neural networks. Towards AI. https://medium.com/p/8de9989db315

18. Sentiment analysis using Neuro-Fuzzy and Hidden Markov models of text. (n.d.). IEEE Xplore. Retrieved August 2, 2022, from https://ieeexplore.ieee.org/abstract/document/6567382

19. Nagai, K. K. ; I. K. ; D. M. ; H. A. ; T. N. ; T. (n.d.). Human-like natural language generation using monte carlo tree search. https://aclanthology.org/W16-5502.pdf


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