From 19370829e6cbad1f6f9c377b0edb8f0bb4fd49d4 Mon Sep 17 00:00:00 2001 From: Logan Mondal Bhamidipaty <76822456+FlyingWorkshop@users.noreply.github.com> Date: Thu, 25 Apr 2024 20:38:27 -0700 Subject: [PATCH] updated DOIs + added JOSS status --- README.md | 1 + paper.bib | 54 ++++++++++++++++++++++++++++++++++-------------------- 2 files changed, 35 insertions(+), 20 deletions(-) diff --git a/README.md b/README.md index fcf71da..76a119f 100644 --- a/README.md +++ b/README.md @@ -3,6 +3,7 @@ [![Build Status](https://github.com/JuliaPOMDP/CompressedBeliefMDPs.jl/actions/workflows/CI.yml/badge.svg?branch=main)](https://github.com/JuliaPOMDP/CompressedBeliefMDPs.jl/actions/workflows/CI.yml?query=branch%3Amain) [![Dev-Docs](https://img.shields.io/badge/docs-latest-blue.svg)](https://JuliaPOMDP.github.io/CompressedBeliefMDPs.jl/dev/) [![codecov](https://codecov.io/gh/JuliaPOMDP/CompressedBeliefMDPs.jl/graph/badge.svg?token=FXmEi9Fscd)](https://codecov.io/gh/JuliaPOMDP/CompressedBeliefMDPs.jl) +[![status](https://joss.theoj.org/papers/e47a49f030233de4fbada2c389de9b34/status.svg)](https://joss.theoj.org/papers/e47a49f030233de4fbada2c389de9b34) ## Introduction diff --git a/paper.bib b/paper.bib index 93efee7..e61416e 100644 --- a/paper.bib +++ b/paper.bib @@ -30,7 +30,7 @@ @article{Kaelbling pages = {99-134}, year = {1998}, issn = {0004-3702}, - doi = {https://doi.org/10.1016/S0004-3702(98)00023-X}, + doi = {10.1016/S0004-3702(98)00023-X}, url = {https://www.sciencedirect.com/science/article/pii/S000437029800023X}, author = {Leslie Pack Kaelbling and Michael L. Littman and Anthony R. Cassandra}, keywords = {Planning, Uncertainty, Partially observable Markov decision processes}, @@ -43,7 +43,8 @@ @misc{carbon year={2023}, eprint={2304.09352}, archivePrefix={arXiv}, - primaryClass={cs.AI} + primaryClass={cs.AI}, + doi={10.48550/arXiv.2304.09352} } @article{drugs, @@ -68,8 +69,8 @@ @article{planes pages = {1729881421999587}, year = {2021}, doi = {10.1177/1729881421999587}, -URL = { https://doi.org/10.1177/1729881421999587 }, -eprint = { https://doi.org/10.1177/1729881421999587 }, +URL = { 10.1177/1729881421999587 }, +eprint = { 10.1177/1729881421999587 }, abstract = { Mission-critical exploration of uncertain environments requires reliable and robust mechanisms for achieving information gain. Typical measures of information gain such as Shannon entropy and KL divergence are unable to distinguish between different bimodal probability distributions or introduce bias toward one mode of a bimodal probability distribution. The use of a standard deviation (SD) metric reduces bias while retaining the ability to distinguish between higher and lower risk distributions. Areas of high SD can be safely explored through observation with an autonomous Mars Helicopter allowing safer and faster path plans for ground-based rovers. First, this study presents a single-agent information-theoretic utility-based path planning method for a highly correlated uncertain environment. Then, an information-theoretic two-stage multiagent rapidly exploring random tree framework is presented, which guides Mars helicopter through regions of high SD to reduce uncertainty for the rover. In a Monte Carlo simulation, we compare our information-theoretic framework with a rover-only approach and a naive approach, in which the helicopter scouts ahead of the rover along its planned path. Finally, the model is demonstrated in a case study on the Jezero region of Mars. Results show that the information-theoretic helicopter improves the travel time for the rover on average when compared with the rover alone or with the helicopter scouting ahead along the rover’s initially planned route. }} @INPROCEEDINGS{markets, @@ -91,7 +92,8 @@ @article{factor number={5}, pages={406}, year={1931}, -publisher={Psychological Review Company} +publisher={Psychological Review Company}, +doi={10.1037/h0069792} } @@ -103,14 +105,16 @@ @article{autoencoder number={2}, pages={233--243}, year={1991}, - publisher={Wiley Online Library} + publisher={Wiley Online Library}, + doi={10.1002/aic.690370209} } @article{VAE, title={Auto-encoding variational bayes}, author={Kingma, Diederik P and Welling, Max}, journal={arXiv preprint arXiv:1312.6114}, - year={2013} + year={2013}, + doi={10.48550/arXiv.1312.6114} } @inproceedings{PBVI, @@ -128,14 +132,16 @@ @article{perseus journal={Journal of artificial intelligence research}, volume={24}, pages={195--220}, - year={2005} + year={2005}, + joi={10.1613/jair.1659} } @article{hsvi, title={Point-based POMDP algorithms: Improved analysis and implementation}, author={Smith, Trey and Simmons, Reid}, journal={arXiv preprint arXiv:1207.1412}, - year={2012} + year={2012}, + doi={10.48550/arXiv.1207.1412} } @article{isomap, @@ -146,7 +152,8 @@ @article{isomap number={5500}, pages={2319--2323}, year={2000}, - publisher={American Association for the Advancement of Science} + publisher={American Association for the Advancement of Science}, + doi={10.1126/science.290.5500.2319} } @article{kd-trees, @@ -159,7 +166,7 @@ @article{kd-trees volume = {18}, number = {9}, issn = {0001-0782}, -url = {https://doi.org/10.1145/361002.361007}, +url = {10.1145/361002.361007}, doi = {10.1145/361002.361007}, abstract = {This paper develops the multidimensional binary search tree (or k-d tree, where k is the dimensionality of the search space) as a data structure for storage of information to be retrieved by associative searches. The k-d tree is defined and examples are given. It is shown to be quite efficient in its storage requirements. A significant advantage of this structure is that a single data structure can handle many types of queries very efficiently. Various utility algorithms are developed; their proven average running times in an n record file are: insertion, O(log n); deletion of the root, O(n(k-1)/k); deletion of a random node, O(log n); and optimization (guarantees logarithmic performance of searches), O(n log n). Search algorithms are given for partial match queries with t keys specified [proven maximum running time of O(n(k-t)/k)] and for nearest neighbor queries [empirically observed average running time of O(log n).] These performances far surpass the best currently known algorithms for these tasks. An algorithm is presented to handle any general intersection query. The main focus of this paper is theoretical. It is felt, however, that k-d trees could be quite useful in many applications, and examples of potential uses are given.}, journal = {Commun. ACM}, @@ -184,7 +191,8 @@ @misc{Julia year={2012}, eprint={1209.5145}, archivePrefix={arXiv}, - primaryClass={cs.PL} + primaryClass={cs.PL}, + doi={10.48550/arXiv.1209.5145} } @INPROCEEDINGS{SARSOP, @@ -202,7 +210,8 @@ @article{despot author={Somani, Adhiraj and Ye, Nan and Hsu, David and Lee, Wee Sun}, journal={Advances in neural information processing systems}, volume={26}, - year={2013} + year={2013}, + doi={10.1613/jair.5328}, } @inproceedings{sunberg2018online, @@ -211,7 +220,8 @@ @inproceedings{sunberg2018online booktitle={Proceedings of the International Conference on Automated Planning and Scheduling}, volume={28}, pages={259--263}, - year={2018} + year={2018}, + doi={10.1609/icaps.v28i1.13882} } @article{pomcp, @@ -228,7 +238,8 @@ @inproceedings{mcts booktitle={European conference on machine learning}, pages={282--293}, year={2006}, - organization={Springer} + organization={Springer}, + doi={10.1007/11871842_29} } @inproceedings{AEMS, @@ -248,7 +259,8 @@ @inproceedings{EPCA title = {A Generalization of Principal Components Analysis to the Exponential Family}, url = {https://proceedings.neurips.cc/paper_files/paper/2001/file/f410588e48dc83f2822a880a68f78923-Paper.pdf}, volume = {14}, - year = {2001} + year = {2001}, + doi={10.7551/mitpress/1120.003.0084} } @misc{epca-MATLAB, @@ -299,7 +311,8 @@ @article{kNN title = {Discriminatory Analysis. Nonparametric Discrimination: Consistency Properties}, urldate = {2024-03-19}, volume = {57}, - year = {1989} + year = {1989}, + doi={10.1037/e471672008-001} } @article{PCA, @@ -309,7 +322,8 @@ @article{PCA year={1933}, volume={24}, pages={498-520}, - url={https://api.semanticscholar.org/CorpusID:144828484} + url={https://api.semanticscholar.org/CorpusID:144828484}, + doi={10.1037/h0070888} } @ARTICLE{kernelPCA, @@ -347,7 +361,7 @@ @incollection{error_bound pages = {261-268}, year = {1995}, isbn = {978-1-55860-377-6}, -doi = {https://doi.org/10.1016/B978-1-55860-377-6.50040-2}, +doi = {10.1016/B978-1-55860-377-6.50040-2}, url = {https://www.sciencedirect.com/science/article/pii/B9781558603776500402}, author = {Geoffrey J. Gordon}, abstract = {The success of reinforcement learning in practical problems depends on the ability to combine function approximation with temporal difference methods such as value iteration. Experiments in this area have produced mixed results; there have been both notable successes and notable disappointments. Theory has been scarce, mostly due to the difficulty of reasoning about function approximators that generalize beyond the observed data. We provide a proof of convergence for a wide class of temporal difference methods involving function approximators such as k-nearest-neighbor, and show experimentally that these methods can be useful. The proof is based on a view of function approximators as expansion or contraction mappings. In addition, we present a novel view of fitted value iteration: an approximate algorithm for one environment turns out to be an exact algorithm for a different environment.} @@ -369,7 +383,7 @@ @article{belief-state-MDP pages = {174-205}, year = {1965}, issn = {0022-247X}, -doi = {https://doi.org/10.1016/0022-247X(65)90154-X}, +doi = {10.1016/0022-247X(65)90154-X}, url = {https://www.sciencedirect.com/science/article/pii/0022247X6590154X}, author = {K.J Åström} } \ No newline at end of file