Explanation-Based Learning of Action Models

Autor:	Eva Onaindia, Sergio Jiménez, Diego Aineto
Rok vydání:	2020
Předmět:	Action (philosophy) Programming language Computer science law Explanation-based learning Plan (drawing) STRIPS Action model computer.software_genre computer law.invention
Zdroj:	Knowledge Engineering Tools and Techniques for AI Planning ISBN: 9783030385606 Knowledge Engineering Tools and Techniques for AI Planning
DOI:	10.1007/978-3-030-38561-3_1
Popis:	The paper presents a classical planning compilation for learning STRIPS action models from partial observations of plan executions. The compilation is flexible to different amounts and types of input knowledge, from learning samples that comprise partially observed intermediate states of the plan execution to samples in which only the initial and final states are observed. The compilation accepts also partially specified action models and it 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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::7e03ccb47b391840e12993def85bc0b3 https://doi.org/10.1007/978-3-030-38561-3_1 Zobrazit plný text záznamu