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In computer science, a deterministic algorithm is an algorithm that, given a particular input, will always produce the same output, with the underlying machine always passing through the same sequence of states. Deterministic algorithms are by far the most studied and familiar kind of algorithm, as well as one of the most practical, since they ...
The systems studied in chaos theory are deterministic. If the initial state were known exactly, then the future state of such a system could theoretically be predicted. However, in practice, knowledge about the future state is limited by the precision with which the initial state can be measured, and chaotic systems are characterized by a strong dependence on the initial condit
Moreover, Gros et al. (1997), posit the inflexibility of traditional linear design processes, calling for a more iterative process, while Winn (1997) and Jonassen et al. criticize the positivist assumptions that learning situations are closed systems, imparting knowledge is the instructor's responsibility, and that human behavior is predictable.
The standard Q-learning algorithm (using a table) applies only to discrete action and state spaces. Discretization of these values leads to inefficient learning, largely due to the curse of dimensionality. However, there are adaptations of Q-learning that attempt to solve this problem such as Wire-fitted Neural Network Q-Learning.
Pronounced "A-star". A graph traversal and pathfinding algorithm which is used in many fields of computer science due to its completeness, optimality, and optimal efficiency. abductive logic programming (ALP) A high-level knowledge-representation framework that can be used to solve problems declaratively based on abductive reasoning. It extends normal logic programming by allowing some ...
The Sapir-Whorf hypothesis branches out into two theories: linguistic determinism and linguistic relativity. Linguistic determinism is viewed as the stronger form – because language is viewed as a complete barrier, a person is stuck with the perspective that the language enforces – while linguistic relativity is perceived as a weaker form of the theory because language is discussed as a ...
The probability path in diffusions model is defined through an Itô process and one can retrieve the deterministic process by using the Probability ODE flow formulation. [ 2 ] In flow-based diffusion models, the forward process is a deterministic flow along a time-dependent vector field, and the backward process is also a deterministic flow ...
The difference between learning automata and Q-learning is that the former technique omits the memory of Q-values, but updates the action probability directly to find the learning result. Learning automata is a learning scheme with a rigorous proof of convergence. [21] In learning automata theory, a stochastic automaton consists of: