Pseudo-Genetic AI Actors Evolving Dynamic Reward Functions
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Solution Overview
Problem
Traditional artificial intelligence systems using genetic algorithms and genetic programming face limitations in evolving diverse and robust autonomous actors, as they often result in homogenization, reliance on static reward functions, and inability to adapt to changing environments, leading to vulnerabilities and inefficiencies in behavior and physical attribute evolution.
Innovation Solution
The implementation of pseudo-genetic meta-knowledge systems that allow for the evolution of unique behavioral and physical attributes, dynamic reward functions, and adaptive reproduction strategies, enabling actors to learn from meta-knowledge and adapt to new environments without pre-defined templates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If static reward functions are used with fixed weights, then the evaluation process is simple and fast, but the system cannot adapt to changing environments and requires thousands of experiments to find optimal parameters
Solution Approach 1:
The patent implements dynamic reward functions where weights and parameters can evolve over time through the pseudo-genetic system. This allows the evaluation mechanism to adapt to changing environments without requiring extensive re-experimentation, as the reward structure itself learns and adjusts based on accumulated experiences.
Solution Approach 2:
The system applies self-service by allowing the reward function to automatically adjust its own parameters through pseudo-genetic evolution. Rather than requiring external tuning through thousands of experiments, the reward function evolves its optimal configuration autonomously based on actor performances and environmental feedback.
2Productivity
If universal reproduction timing is applied to all actors, then convergence to successful behaviors occurs rapidly, but homogenization of the population creates universal weaknesses
Solution Approach 1:
The patent applies local quality by allowing different actors to have individualized reproduction timing based on their unique pseudo-genetic characteristics and performance levels. Rather than enforcing uniform reproduction schedules, each actor's reproduction timing is tailored to its specific evolution stage and capabilities, maintaining diversity while achieving convergence.
Solution Approach 2:
The system uses selective copying where successful actors are reproduced with variations in their pseudo-genetic code. This allows the population to converge on successful behaviors through copying high-performing actors while maintaining diversity through pseudo-genetic mutations and individualized reproduction timing.
3Ease of operation
If Markov Decision Process is used for reward functions, then immediate decisions can be made based on current state, but historical states and actions cannot influence current or future decisions
Solution Approach 1:
The patent extends the decision-making framework by adding a temporal dimension to the Markov Decision Process. The pseudo-genetic system incorporates historical states and actions as additional dimensions that influence current decisions, allowing actors to learn from past experiences while maintaining the computational efficiency of immediate decision-making.
Data Source
AI summary
Systems and methods for artificially intelligent physical and virtual actors including pseudo-genetic information and configured to retain meta-knowledge are described. The actors are adapted with unique behavioral and physical attributes which may independently evolve over time. The attributes may include reproduction based attributes which direct how, when, and with whom the actor reproduces and may be subject to mutation and reproductive forces. The attributes may include evaluation attributes which dictate when and how each actor evaluates their performance. The evaluation attributes may also be subject to mutation and reproductive forces. The attributes may consider meta-knowledge which is a reflection of information gathered by an actor. What and how much meta-knowledge is collected may also be expressed as one or more evolvable attributes.