17933362. STOCHASTICITY MITIGATION IN DEPLOYED AI AGENTS simplified abstract (MICROSOFT TECHNOLOGY LICENSING, LLC)

STOCHASTICITY MITIGATION IN DEPLOYED AI AGENTS

Organization Name

MICROSOFT TECHNOLOGY LICENSING, LLC

Inventor(s)

Kingsuk Maitra of Fremont CA (US)

Brendan Lee Bryant of Kirkland WA (US)

Chris Allen Premoe of Redmond WA (US)

Kence Anderson of Berkeley CA (US)

STOCHASTICITY MITIGATION IN DEPLOYED AI AGENTS - A simplified explanation of the abstract

This abstract first appeared for US patent application 17933362 titled 'STOCHASTICITY MITIGATION IN DEPLOYED AI AGENTS

Simplified Explanation

The techniques disclosed in this patent application aim to mitigate stochasticity in controlling a mechanical system with AI agents. Stochasticity is addressed in near-term and long-term timeframes using different strategies, such as adjusting the reward function for long-term stochasticity and applying margins to AI agent outputs for short-term stochasticity.

• AI agents are created using data from a machine learning model.
• Stochasticity is segmented into near-term and long-term categories.
• Long-term stochasticity is addressed by modifying the reward function.
• Short-term stochasticity is mitigated by applying margins to AI agent outputs.
• Example margins include window averaging, clamps, and statistical process control bounds.
• AI agents may be regression brains generated from inferred setpoints.
• Limitations in fitting a regression line may result in predicted setpoints outside of allowed ranges.

Potential Applications

The technology described in this patent application could be applied in various industries where precise control of mechanical systems is crucial, such as manufacturing, robotics, and autonomous vehicles.

Problems Solved

This innovation addresses the challenge of dealing with stochasticity when controlling mechanical systems with AI agents, ensuring more stable and reliable performance in both short-term and long-term scenarios.

Benefits

The benefits of this technology include improved accuracy and efficiency in controlling mechanical systems, reduced downtime due to unexpected variations, and enhanced overall system performance.

Potential Commercial Applications

Potential commercial applications of this technology include automated manufacturing processes, autonomous vehicles, robotic systems, and smart infrastructure management.

Possible Prior Art

One possible prior art in this field could be the use of traditional control systems to address stochasticity in mechanical systems. However, the specific techniques and strategies outlined in this patent application may offer unique advantages over existing methods.

How does this technology compare to other methods of mitigating stochasticity in mechanical systems with AI agents?

The article does not provide a direct comparison with other methods or technologies used to address stochasticity in mechanical systems. It would be beneficial to understand the specific advantages and limitations of this approach compared to existing solutions.

What are the potential challenges or limitations of implementing this technology in real-world applications?

The article does not discuss any potential challenges or limitations that may arise when implementing this technology in practical settings. It would be important to consider factors such as scalability, adaptability to different systems, and integration with existing infrastructure.

Original Abstract Submitted

The techniques disclosed herein mitigate stochasticity when controlling a mechanical system with artificial intelligence (AI) agents. In some configurations, AI agents are created using data generated by a machine learning model. Stochasticity is segmented temporally into near term and long term, and different strategies are used to address stochasticity in the different timeframes. For example, long term stochasticity may be addressed with changes to the reward function used to train the model. Short term stochasticity may be addressed by applying a margin to the output of an AI agent. Example margins include window averaging, clamps, and statistical process control bounds. In one configuration, AI agents are regression brains that are generated from setpoints inferred by the model from environmental states. The limitations inherent to fitting a regression line to this data may result in some predicted setpoints being outside of an allowed range.