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Recent action learning methods take various approaches and employ a wide variety of tools from different areas of artificial intelligence and computational logic.As an example of a method based on propositional logic, we can mention SLAF (Simultaneous Learning and Filtering) algorithm, [1] which uses agent's observations to construct a long propositional formula over time and subsequently ...
The action learning approach was originated by Reg Revans. [6] [7] Formative influences for Revan included his time working as a physicist at the University of Cambridge, wherein he noted the importance of each scientist describing their own ignorance, sharing experiences, and communally reflecting in order to learn. [8]
Action selection is a way of characterizing the most basic problem of intelligent systems: what to do next. In artificial intelligence and computational cognitive science, "the action selection problem" is typically associated with intelligent agents and animats—artificial systems that exhibit complex behavior in an agent environment.
A large language model (LLM) is a type of machine learning model designed for natural language processing tasks such as language generation. LLMs are language models with many parameters, and are trained with self-supervised learning on a vast amount of text. The largest and most capable LLMs are generative pretrained transformers (GPTs).
Q-learning is a model-free reinforcement learning algorithm that teaches an agent to assign values to each action it might take, conditioned on the agent being in a particular state. It does not require a model of the environment (hence "model-free"), and it can handle problems with stochastic transitions and rewards without requiring adaptations.
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 ...
Model selection is the task of selecting a model from among various candidates on the basis of performance criterion to choose the best one. [1] In the context of machine learning and more generally statistical analysis, this may be the selection of a statistical model from a set of candidate models, given data. In the simplest cases, a pre ...
A problem is a request for the learner…to supply one or more of the model’s behaviors, elements, or interrelations that are missing”. [1] Problems act as filters or masks that focus learner attention on specific information about the objects or models. Problems also trigger learning processes used in the construction of mental models.