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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 ...
In reinforcement learning (RL), a model-free algorithm is an algorithm which does not estimate the transition probability distribution (and the reward function) associated with the Markov decision process (MDP), [1] which, in RL, represents the problem to be solved. The transition probability distribution (or transition model) and the reward ...
In psychology, the four stages of competence, or the "conscious competence" learning model, relates to the psychological states involved in the process of progressing from incompetence to competence in a skill. People may have several skills, some unrelated to each other, and each skill will typically be at one of the stages at a given time.
Given a training set consisting of examples = (,, ′), where , ′ are observations of a world state from two consecutive time steps , ′ and is an action instance observed in time step , the goal of action model learning in general is to construct an action model , , where is a description of domain dynamics in action description formalism like STRIPS, ADL or PDDL and is a probability ...
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:
The nature of chaos theory suggests that the predictability of any system is limited because it is impossible to know all of the minutiae of a system at the present time. In principle, the deterministic systems that chaos theory attempts to analyze can be predicted, but uncertainty in a forecast increases exponentially with elapsed time. [2]
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 ...
Each behavioural change theory or model focuses on different factors in attempting to explain behaviour change. Of the many that exist, the most prevalent are learning theories, social cognitive theory, theories of reasoned action and planned behaviour, transtheoretical model of behavior change, the health action process approach, and the BJ Fogg model of behavior change.