18419369. ADAPTATION SYSTEM AND ADAPTATION METHOD simplified abstract (TOYOTA JIDOSHA KABUSHIKI KAISHA)
Contents
ADAPTATION SYSTEM AND ADAPTATION METHOD
Organization Name
TOYOTA JIDOSHA KABUSHIKI KAISHA
Inventor(s)
Akihiro Katayama of Toyota-shi (JP)
Kenichiro Kumada of Nagakute-shi (JP)
ADAPTATION SYSTEM AND ADAPTATION METHOD - A simplified explanation of the abstract
This abstract first appeared for US patent application 18419369 titled 'ADAPTATION SYSTEM AND ADAPTATION METHOD
Simplified Explanation: The patent application describes a system where a processing circuitry executes trials in a learning routine until a condition is met, then compares rewards from multiple executions of trials to determine the most rewarding trial.
Key Features and Innovation:
- Processing circuitry executes first and second trials in a learning routine.
- Reflects changes in trials with larger rewards in a control map.
- Two different processes based on meeting a specified condition.
- Compares rewards from multiple executions of trials to determine the most rewarding trial.
Potential Applications: This technology could be applied in:
- Machine learning systems
- Adaptive control systems
- Optimization algorithms
Problems Solved:
- Efficient learning routines
- Maximizing rewards in trials
- Adaptive decision-making processes
Benefits:
- Improved learning efficiency
- Enhanced decision-making capabilities
- Optimal trial selection based on rewards
Commercial Applications: Potential commercial uses include:
- Automated trading systems
- Recommendation engines
- Autonomous vehicles
Prior Art: Readers can explore prior art related to this technology in the fields of machine learning, adaptive control, and optimization algorithms.
Frequently Updated Research: Stay updated on research in machine learning, adaptive systems, and optimization algorithms to understand the latest advancements in this technology.
Questions about the Technology: 1. What are the potential real-world applications of this technology? 2. How does this technology compare to traditional learning algorithms in terms of efficiency and performance?
Original Abstract Submitted
Until a specified condition is met, a processing circuitry of an adaptation system executes a first trial and a second trial in each execution of a learning routine, reflects, in a control map, a change in the trial with a larger reward, and ends the learning routine (first process). After the specified condition is met, the processing circuitry executes the first trial and the second trial multiple times in each learning routine, compares the reward for the multiple executions of the first trial with the reward for the multiple executions of the second trial, and reflects, in a control map, a change in the trial with the larger reward, and ends the learning routine (second process).
