AI Narrative Validation for Interactive Synthetic Characters
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Solution Overview
Problem
Generative AI models in tabletop games and playsets often produce erroneous, biased, or inappropriate responses due to inaccuracies in training data, lack of transparency, and the black-box nature of AI decision-making, leading to a poor user experience and reduced engagement.
Innovation Solution
A validation framework that integrates multiple AI models with pre-loaded query contexts to validate user inputs and outputs, ensuring accuracy, relevance, and reliability through parallel processing, including checks for topic, localization, hallucination, profanity, jailbreak, accuracy, and format, using consensus modules to aggregate results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple validation models are used to check AI outputs, then reliability of AI-generated content is improved, but device complexity increases
Solution Approach 1:
The validation framework is segmented into multiple independent validation models, each responsible for specific aspects of content validation (e.g., hallucination detection, bias detection, format checking). This segmentation allows each model to specialize in particular validation tasks, improving overall reliability while maintaining manageable complexity through modular design.
Solution Approach 2:
A consensus module acts as an intermediary between the multiple validation models and the final output generation. The consensus module aggregates results from various validation models, reconciles conflicting validations, and determines whether content should be approved or rejected. This intermediary layer manages the complexity of coordinating multiple validation models while ensuring comprehensive reliability checking.
2Productivity
If parallel processing of validation checks is implemented, then productivity of content validation is improved, but use of energy increases
Solution Approach 1:
The validation framework dynamically adjusts the level of parallel processing based on content characteristics, user preferences, and system resources. For low-risk content or when energy is constrained, fewer validation models are activated simultaneously. For high-risk content or when resources are abundant, more models run in parallel, optimizing the balance between validation speed and energy consumption.
Solution Approach 2:
The system changes operational parameters such as the number of active validation models, the depth of validation checks, and the parallelism level based on content sensitivity, user settings, and available computational resources. This allows the system to maintain high productivity when needed while reducing energy consumption during normal operations.
3Measurement precision
If comprehensive validation checks are performed on user inputs, then accuracy of AI responses is improved, but loss of time in processing increases
Solution Approach 1:
Validation rules and criteria are pre-loaded into the system during initialization or offline preparation. Common validation patterns, acceptable answer formats, and expected content structures are established beforehand. This preliminary action allows the validation process to proceed efficiently during runtime without requiring complex real-time analysis, reducing processing time while maintaining accuracy.
Solution Approach 2:
The system performs partial validation on all content and excessive (comprehensive) validation only when necessary. For example, format checking is applied to all responses, while full hallucination detection and bias analysis are performed only on sensitive topics or when confidence scores indicate uncertainty. This selective approach maintains accuracy for critical checks while minimizing time loss on routine validations.
Data Source
AI summary
Disclosed herein are systems and associated methods for constructing dynamic artificial intelligence (AI)-driven narratives for interactive synthetic characters. The systems and methods include one or more synthetic users, which include characters and/or objects. The figurines are identified by a stage through Near Field Communication (NFC) tags, audio inputs, image inputs, and/or video inputs. Users place the identified characters and/or objects on the stage. The AI model processes the identified characters and/or objects on the stage to dynamically generate personalized and evolving narratives between the identified characters and/or objects on the stage. The generated narratives incorporate dialogue, motorized animations, and/or character interactions.