Multi-Sourced AI Character Training Through Simulated Interaction Graphs
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Solution Overview
Problem
Existing AI character training systems struggle to account for the variability in human interactions due to differences in language, personality profiles, and context, making it difficult to generate realistic and effective social agent behaviors without relying on extensive human verification.
Innovation Solution
A multi-sourced machine learning model-based approach that generates and simulates interaction graphs, compares them for similarity, and iteratively adjusts behavior models to train AI characters, incorporating feedback from human users to refine interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human verification is used to verify all possible interaction scenarios, then interaction realism is improved, but time consumption and labor requirements increase significantly
Solution Approach 1:
The patent creates virtual copies of human users through AI agents that simulate diverse personality profiles, demographics, and interaction contexts. These virtual copies replicate human behavior patterns and response variations, allowing comprehensive interaction scenario verification without requiring actual human verification for each scenario.
Solution Approach 2:
The patent replaces manual human verification with automated machine learning models that analyze interaction scenarios. The system uses computational algorithms to evaluate whether AI character responses are appropriate, diverse, and realistic across numerous scenarios, eliminating the need for human verification while maintaining interaction quality standards.
2Adaptability or versatility
If the AI character is trained to handle diverse human responses and contexts, then adaptability is improved, but the complexity of training data processing increases
Solution Approach 1:
The patent segments the diverse training data into distinct categories based on personality profiles, demographics, and interaction contexts. The system processes and trains on these segmented data portions separately, then integrates the learnings to create a comprehensive AI character that can adapt to various human response patterns without overwhelming complexity.
Solution Approach 2:
The patent introduces machine learning models as intermediary components that bridge the gap between raw diverse interaction data and the AI character training process. These intermediary models preprocess, analyze, and transform complex diverse responses into structured training representations, reducing the overall complexity of processing and training on varied human behaviors.
3Productivity
If computational models are used to simulate interaction scenarios, then verification efficiency is improved, but the accuracy of predicting human responses may be insufficient
Solution Approach 1:
The patent implements feedback loops where the AI character interacts with virtual human copies, and the outcomes of these interactions are fed back into the training system. This continuous feedback mechanism allows the system to learn from simulated interactions and refine its prediction accuracy, gradually improving how well the AI character anticipates and responds to diverse human behaviors.
Solution Approach 2:
The patent performs preliminary training and simulation phases before final AI character deployment. During these preliminary stages, the system extensively simulates and tests interaction scenarios with diverse virtual human profiles, pre-adjusting and fine-tuning the AI character's response algorithms to maximize prediction accuracy before actual use.
Data Source
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AI summary
A system includes a hardware processor configured to execute software code to receive interaction data identifying an action and personality profiles corresponding respectively to multiple participant cohorts in the action, generate, using the interaction data, an interaction graph of behaviors of the participant cohorts in the action, simulate, using a behavior model, participation of each of the participant cohorts in the action to provide a predicted interaction graph, and compare the predicted and generated interaction graphs to identify a similarity score for the predicted interaction graph relative to the generated interaction graph. When the similarity score satisfies a similarity criterion, the software code is executed to train, using the behavior model, an artificial intelligence character for interactions. When the similarity score fails to satisfy the similarity criterion, the software code is executed to modify the behavior model based on one or more differences between the predicted and generated interaction graphs.