AI Gameplay Assistant With Context Validation for Rule Adjudication
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Solution Overview
Problem
Existing AI models struggle to accurately and reliably adjudicate complex tabletop game rules, particularly in card games like Magic: The Gathering, due to insufficient training data, colloquial question phrasing, context identification, and potential for biased or inaccurate responses, which limits accessibility and enjoyment for new players.
Innovation Solution
A validation framework integrates multiple AI models and heuristic evaluations to ensure accuracy and relevance of game-related outputs, using a pre-loaded query context to validate user inputs and outputs through parallel processing, including topic, localization, completeness, hallucination, profanity, jailbreak, accuracy, format, user feedback, difficulty level, and temporal relevance checks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If general-purpose generative AI models are used to respond to user queries, then the model can handle a wide variety of questions, but the initial response quality is middling and requires query refinement from the user
Solution Approach 1:
The system performs preliminary actions by pre-loading game-specific context, rules, and data into the AI model before it needs to respond to queries. This preparation ensures that when users ask questions, the model already has relevant game knowledge available, eliminating the need for users to refine their queries and providing accurate responses from the first attempt.
2Productivity
If AI models are trained on extensive training data, then the model can recognize patterns and produce outputs, but the training data may be insufficient for complex tabletop game rules
Solution Approach 1:
The system merges general-purpose generative AI capabilities with specialized tabletop game knowledge bases, rules engines, and structured game data. By combining these different components, the system achieves both the pattern recognition of general AI models and the rule accuracy of specialized game systems, enabling reliable adjudication of complex game scenarios.
3Ease of operation
If the AI model provides detailed game rule adjudications, then the gameplay experience is improved, but the complexity of validating accuracy and relevance increases
Solution Approach 1:
The system implements feedback mechanisms where AI-generated game rule adjudications are validated against pre-loaded game rules, context, and expected outcomes. This feedback loop ensures that detailed and accurate game information is provided to players while maintaining validation through automated checking processes that compare AI responses against known correct answers and game mechanics.
4Reliability
If multiple validation checks are performed on AI outputs, then the reliability of game rule adjudications is improved, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary validation by pre-loading game rules, context, and validation criteria into the AI model before it generates responses. This allows the model to self-validate its outputs against pre-loaded standards, reducing the need for extensive post-processing validation checks and minimizing the time required to ensure response accuracy.
Data Source
AI summary
Disclosed herein are systems and associated methods for adjudicating rules and providing gameplay advice during gameplay using an artificial intelligence (AI) model. The systems and methods include an AI model that assists users in querying rules for a game match by analyzing specific card interactions and game board states. The AI model is trained to request additional information when initial user queries lack sufficient detail for accurate adjudication of the query. The system receives a query from a user, determines a confidence score for adjudicating the query based on gameplay rules and card data, and, if the confidence score is below a predefined threshold, requesting further clarification. If the confidence score exceeds the threshold, the AI model provides a response to adjudicate the query.