Computing programs for generalized planning using a classical planner

TL;DR: The paper presents a classical planning compilation for learning STRIPS action models from partial observations of plan executions, which can be used to validate whether an observation of a plan execution follows a given STRIps action model, even if the given model or the given observation is incomplete.

TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.

TL;DR: A computational model is described that learns in a similar fashion and does so better than current deep learning algorithms and can generate new letters of the alphabet that look “right” as judged by Turing-like tests of the model's output in comparison to what real humans produce.

TL;DR: This book contains tutorial overviews and research papers on contemporary trends in the area of machine learning viewed from an AI perspective, including learning from examples, modeling human learning strategies, knowledge acquisition for expert systems, learning heuristics, discovery systems, and conceptual data analysis.