Planning

[TOC] Title: Detecting Ai Planning Modelling Mistakes Potential Errors and Benchmark Domains Author: Pascal Bercher et. al. Publish Year: 2023 Review Date: Mon, Nov 13, 2023 url: https://bercher.net/publications/2023/Sleath2023PossibleModelingErrors.pdf Summary of paper Contribution the author provided a compilation of potential modelling errors the author supply a public repository of 56 (flawed) benchmark domains conducted an evaluation of well-known AI planning tools for their ability to diagnose those errors, showing that not a single tool is able to spot all errors, with no tool being strictly stronger than another. Some key terms list of errors ...

[TOC] Title: Planning With Diffusion for Flexible Behaviour Synthesis Author: Michael Janner et. al. Publish Year: 21 Dec 2022 Review Date: Mon, Jan 30, 2023 Summary of paper Motivation use the diffusion model to learn the dynamics tight coupling of the modelling and planning our goal is to break this abstraction barrier by designing a model and planning algorithm that are trained alongside one another, resulting in a non-autoregressive trajectory-level model for which sampling and planning are nearly identical. Some key terms ideal model-based RL ...

[TOC] Title: Generalised Domain Model Acquisition from Action Traces (LOCM2) Author: Stephen Cresswell et. al. Publish Year: 2013 Review Date: Mar 2022 Summary of paper Motivation One approach to the problem of formulating domain models for planning is to learn the models from example action sequences. This work extended LOCM by allowing multiple parameterised state machine to represent a single object. In other words, it is possible to automatically infer the underlying transition system from sample action sequences of the domain. Using such an approach removes the necessity for the domain expert to also be an expert at modelling transition systems. ...