Can machine learning provide system stability to automatically learn and improve from experience without being explicitly programmed?
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| Papers (8) | Insight |
|---|---|
04 Dec 2014 24 Citations | Machine learning could be useful to support the robot to adapt to the environment and learn the correct navigation skills quickly. |
28 Jan 2007 | If this resource--the users themselves--could somehow work hand-in-hand with machine learning systems, the accuracy of learning systems could be improved and the users' understanding and trust of the system could improve as well. |
52 Citations | Like human learning, we can make a computer program learn from previous input data to optimize its performance on processing new data. |
| Our study shows that a neural network can learn from an expert system's experience and guide the expert system when the expert system does not have enough knowledge to reason. | |
83 Citations | Machine learning is the study of computer programs and algorithms that learn about their environment and improve automatically with experience. This approach thus contrasts with today's predominant approach of directly specifying at design time how the hardware should accomplish the desired goal. |
26 Citations | Instead of viewing the system as a random dynamical system, we see it as a true machine-learning model, which can be fully optimized. |
220 Citations | Machine learning includes a broad class of computer programs that improve with experience. |
| Consequently, a virtual learning environment is a true human-machine symbiosis, paired by human learning and system learning. |
Related Questions
What is stability?5 answersStability can be broadly defined as the tendency of a system to return to its former state after a disturbance. It is a concept that is studied in various academic disciplines, including ecology, solid mechanics, strategic analysis, and systems theory. In the natural world, stability refers to the capacity of an ecosystem to resist environmental disturbances. In solid mechanics, stability refers to the ability of a solid to maintain a certain configuration under the action of a load. In strategic analysis, stability is the condition in which a slight disturbance in a system does not produce too disturbing an effect. In systems theory, stability represents the property of a system where its states have final values. Overall, stability is a fundamental concept that is relevant in understanding the behavior and resilience of various systems in different fields of study.
What is stability?5 answersStability is a concept that is used in various academic disciplines to describe the tendency of a system to return to its former state after a disturbance. It can be applied to ecosystems, where it refers to the capacity of an ecosystem to resist environmental disturbances. In the context of strategic analysis, stability refers to the condition in which a slight disturbance in a system does not produce too disturbing an effect. In solid mechanics, stability refers to the behavior of a solid to maintain a certain configuration under the action of a load. There are three basic concepts of stability: constancy, robustness, and resilience, which are found in both the natural and social sciences. In mathematical ecology, stability is used to analyze the equilibrium behavior of biological communities and determine the conditions they must satisfy to be stable.
When system is stable?5 answersA system is considered stable when it operates at an equilibrium or steady state and is able to withstand small perturbations without deviating significantly from this state. Stability is crucial for the successful operation of various dynamical systems, including mathematical, natural, social, and engineering systems. In the context of real-time systems, a mean system utilization smaller than one is a necessary condition for stability and feasibility. Stable real-time systems have two distinct states: a transient state and a steady-state, where the same distribution of response times is repeated infinitely for each task. Control systems can also exhibit stability and robustness to disturbances and model inaccuracies, as well as alterations in the control signal pattern, such as sporadic late completions or deadline misses. In some cases, a system may be unstable overall but stable within a specific subregion.
How is the stability problem handled in reinforcement learning agents?5 answersReinforcement learning agents handle the stability problem through various techniques. One approach is to use offline reinforcement learning with a fixed dataset, which allows agents to learn without interacting with the environment. However, the performance of pre-trained agents may collapse during online fine-tuning due to distribution shifts. Another method involves selectively sharing learning results among multiple agents to prevent homogenization while learning cooperatively. Ensemble methods, such as combining Q-function approximator estimates using a bagging approach, can increase stability and improve learning efficiency. Additionally, a decentralized architecture for reinforcement learning allows multiple agents to learn optimal control policies on their own devices, enhancing learning speed and service quality. Adaptive methods that leverage action priors, adjusting the strength of teacher feedback based on usefulness estimates, can also improve stability in reinforcement learning.
How is the controller stability problem handled in reinforcement learning agents?3 answersReinforcement learning agents handle the controller stability problem by regulating the input-output gradients of policies and exploiting problem-specific structures to obtain strong guarantees of robust stability. They also use active disturbance rejection controller (ADRC) and Q-learning algorithms to keep the load within the rated range and select adaptive parameters for the controller. Another approach is to employ reinforcement learning in the context of model-based control, allowing for safe learning of a feedback controller to compensate for uncertainties due to model mismatch or external disturbances. Additionally, the use of disturbance observers (DOB) helps enhance the robustness of RL-based controllers by compensating for the mismatch between the plant and simulator and rejecting disturbances while maintaining nominal performance and robust stability. Finally, a residual learning methodology is used to adapt conventional controllers using reinforcement learning, with constraints on the residual actions to ensure safe operation and stability.
Can machine learning provide ability to automatically learn and improve from experience without being explicitly programmed o Yes o no?11 answers