Rule-Based Post-Processing for NLP Game Responses
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Solution Overview
Problem
Machine learning algorithms are not widely adopted in video game development due to the significant time and resource commitment required to train them, and pre-trained models often produce results that contradict game developers' intentions due to real-world interpretations, making it difficult to refine results without retraining.
Innovation Solution
Implementing a rule-based post-processing system for pre-trained semantic natural language processing (NLP) machine learning algorithms that allows game developers to modify results without retraining, by using input and response rules to adjust scores based on semantic similarity, enabling fine-tuning of player experience and non-player character responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If pre-trained NLP machine learning algorithms are used to enhance player experience, then the system can provide context-aware content delivery, but the results may contradict game developers' intentions due to real-world interpretations
Solution Approach 1:
The patent introduces a rule-based post-processing system that acts as an intermediary between the pre-trained NLP model and the game content delivery system. This mediator adjusts and refines the model's outputs to align with developer intentions without requiring retraining of the underlying ML algorithm, thus resolving the conflict between adaptability and precision.
Solution Approach 2:
The system segments the content delivery process into two independent components: (1) the pre-trained NLP model that provides contextual understanding and adaptability, and (2) the rule-based post-processing layer that ensures alignment with developer intentions. This segmentation allows each component to excel at its specific function without compromising the other.
2Manufacturing precision
If machine learning algorithms are trained with custom-crafted examples to improve accuracy, then the model can better understand game-specific contexts, but significant time and resources are required to produce training data
Solution Approach 1:
The patent applies preliminary action by pre-training the NLP model on general-purpose corpora before deployment in the game context. This allows the model to acquire foundational language understanding in advance, eliminating the need for time-consuming custom training data production while still enabling effective game-specific content delivery through post-processing rules.
3Manufacturing precision
If a large corpus of training data is collected to train ML algorithms, then the model can achieve better performance, but the computational resources and expertise required for training are typically not available to game development teams
Solution Approach 1:
The patent uses copying by leveraging pre-trained models that have already been trained on large corpora by others. Instead of training new models from scratch, the system copies and adapts existing pre-trained NLP models through post-processing rules, thereby achieving good performance without requiring substantial computational resources or ML expertise.
4Ease of manufacture
If pre-trained models are used to avoid training costs, then resources are saved, but the models produce results based on real-world interpretations that may not align with game-specific contexts
Solution Approach 1:
The rule-based post-processing system serves as an intermediary that bridges the gap between pre-trained models and game-specific contexts. It maintains the deployment efficiency of using pre-trained models while improving contextual accuracy by adjusting outputs based on game-specific rules and developer intentions.
Data Source
AI summary
Program code representing a semantic natural language processing (NLP) machine learning (ML) algorithm is stored in a memory. A processor executes the semantic NLP ML algorithm to generate initial scores that represent a degree of matching between candidate responses and an input phrase provided by a user during execution of program code. The processor also modifies one or more of the initial scores using one or more rules that associate a first phrase with a second phrase. The one or more rules are selected to modify the initial scores based on semantic similarity of the user input phrase and the first phrase determined by the semantic NLP ML algorithm and the semantic similarity of the response phrase with a corresponding candidate response. Execution of the program code is modified based on the modified initial scores. In some cases, the semantic NLP ML algorithm is used to implement a video game.