100 Best State Machine Videos

Notes:

A state machine is a mathematical model that describes the behavior of a system as a sequence of states. It is a mathematical concept that is used in a variety of different fields, including computer science, engineering, and social sciences.

In the context of computer science, state machines are used to describe the behavior of systems that have a finite number of states. For example, a state machine might describe the behavior of a traffic light, which can be in one of three states: red, yellow, or green. The state machine would define the rules for transitioning between these states, such as when to switch from red to green, or from green to yellow.

State machines are often used to design and implement systems that have complex, dynamic behaviors. By defining the system’s behavior as a sequence of states, it is possible to specify the rules for transitioning between states, and to ensure that the system behaves in a predictable and consistent way.

State machines are also used in fields such as engineering, where they can be used to model the behavior of physical systems, such as electronic circuits, mechanical devices, and chemical reactions. In the social sciences, state machines can be used to model the behavior of individuals, groups, and organizations.

Hierarchical state machine is a type of state machine that is organized into a hierarchy, with states and transitions defined at different levels of the hierarchy. In a hierarchical state machine, each state can contain its own set of sub-states and transitions, allowing the state machine to model complex behavior that involves multiple levels of abstraction. A hierarchical state machine can be used to represent systems that exhibit different behaviors depending on the context or conditions in which they operate, or to represent systems that have a large number of possible states or transitions.
Neural state machine is a type of state machine that is implemented using artificial neural networks. Neural state machines are able to learn from data and adapt their behavior over time, making them useful for modeling dynamic systems or for tasks that require decision-making or pattern recognition. Neural state machines can be trained using supervised learning methods, in which the machine is provided with labeled training examples, or unsupervised learning methods, in which the machine is able to learn from raw data without explicit labels. Neural state machines are often used in machine learning and artificial intelligence applications.

Wikipedia:

Subsumption architecture

References:

Learning by Abstraction: The Neural State Machine (2019)