AI Character Model Probabilistic Behavior Adaptation
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Solution Overview
Problem
Conventional virtual character models lack flexibility and adaptability, as they are tailored for specific applications and rely on predefined rules, hindering their ability to dynamically adjust behavior in response to user emotions or environmental changes, resulting in predictable and non-dynamic interactions.
Innovation Solution
A system and method that provide AI characters with changeable behavior by associating multiple behavioral types with probabilities, allowing the AI character model to randomly select and adjust parameters based on these probabilities during interactions, enabling dynamic and context-dependent behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional virtual character models use predefined rules and specific logic, then the character behavior is predictable and consistent, but the adaptability and flexibility to dynamically adjust behavior are hindered
Solution Approach 1:
The patent applies dynamics by transitioning from static predefined rules to dynamic probabilistic behavioral selection. The virtual character model now randomly selects behavioral types during runtime based on probability distributions, allowing behavior to change dynamically while maintaining statistical consistency. This resolves the contradiction by making the system adaptable through randomization while preserving reliability through controlled probability distributions.
Solution Approach 2:
The patent changes the parameter of behavior selection from deterministic (fixed rules) to probabilistic (random selection with probability distributions). By associating behavioral types with probability values and selecting based on these probabilities, the system achieves both adaptability (through random variation) and reliability (through controlled probability distributions that maintain expected behavior patterns).
2Ease of manufacture
If virtual character models are tailored for specific applications with predefined rules, then the development process is simplified for that specific use, but the flexibility to integrate into other environments is reduced
Solution Approach 1:
The patent implements universality by creating a virtual character model that can function across multiple environments and applications. The probabilistic behavioral type selection mechanism is environment-agnostic and can be applied universally. The model maintains ease of development through a standardized framework while achieving versatility by adapting behavior through probability distributions rather than environment-specific hardcoding.
Solution Approach 2:
The dynamic probabilistic selection mechanism allows the same virtual character model to adapt to different environments without requiring environment-specific customization. The model maintains development simplicity through a unified approach while achieving environmental flexibility through runtime behavioral adaptation based on probability distributions.
3Reliability
If AI characters exhibit deterministic behavioral characteristics, then the behavior is consistent under the same conditions, but the dynamic virtual character experience is limited
Solution Approach 1:
The patent applies dynamics by introducing randomization into behavioral selection. Instead of deterministic behavior, the system randomly selects behavioral types based on probability distributions. This creates dynamic, varied user experiences while maintaining reliability through controlled probability distributions that ensure expected behavior patterns emerge over time.
Solution Approach 2:
The patent changes the behavior parameter from deterministic to probabilistic. By associating behavioral types with probability values and selecting based on these probabilities, the system improves user experience through variety and unpredictability while maintaining reliability through the statistical properties of the probability distributions that guide behavior.
Data Source
AI summary
A method for providing artificial intelligence (AI) characters with changeable behavior includes providing a plurality of behavioral types associated with an AI character model, where the plurality of behavioral types are associated with probabilities, and where the AI character model is configured to generate an AI character; randomly selecting, during interaction of the AI character, and based on the probabilities, a behavioral type from the plurality of behavioral types; and adjusting, based on the selected behavioral type, parameters associated with the AI character, thereby causing the AI character to follow the selected behavioral type.