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Finding concise plans: Hardness and algorithms

Jason M. O'Kane, Dylan A. Shell
2013 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems  
We introduce a planning algorithm that generates concise plans for planning problem that may involve both non-determinism and partial observability, and also show that finding the most concise plan is  ...  We describe an implementation of the algorithm, along with empirical results on the run time and solution quality for both manipulation and navigation problem domains.  ...  HARDNESS OF CONCISE PLANNING In this section, we prove that the concise planning problem introduced in Section III is NP-hard.  ... 
doi:10.1109/iros.2013.6697049 dblp:conf/iros/OKaneS13 fatcat:xxtp6jjiyjftrccbico775ir2i

Concise Finite-Domain Representations for Factored MA-PDDL Planning Tasks

Daniel Fišer, Antonín Komenda
2018 Proceedings of the 10th International Conference on Agents and Artificial Intelligence  
In this work, we propose such a scheme combining centralized processes of the grounding and the inference of mutex groups.  ...  Planning tasks for the distributed multi-agent planning in deterministic environments are described in highly expressive, but lifted, languages, similar to classical planning.  ...  ACKNOWLEDGEMENTS This research was supported by the Czech Science Foundation (grant no. 15-20433Y) and by the Grant Agency of the CTU in Prague (grant no. SGS14/202/OHK3/3T/13).  ... 
doi:10.5220/0006539503060313 dblp:conf/icaart/FiserK18 fatcat:v6gah2hhozcmdek4hzr4vcy324

Improper Filter Reduction [article]

Fatemeh Zahra Saberifar and Ali Mohades and Mohammadreza Razzazi and Jason M.O'Kane
2017 arXiv   pre-print
We present two metrics for measuring the distance between pairs of filters, describe dynamic programming algorithms for computing these distances, and show that improper filter reduction is NP-hard under  ...  We have implemented these algorithms and analyze the results of three sets of experiments.  ...  IIS-0953503 and IIS-1526862.  ... 
arXiv:1703.00812v1 fatcat:4b6nd2vvbrhcders6tmfa6hh7m

Automatic reduction of combinatorial filters

Jason M. O'Kane, Dylan A. Shell
2013 2013 IEEE International Conference on Robotics and Automation  
We also show that solving this problem optimally is NP-hard, and that the related decision problem is NP-complete. These hardness results justify the potentially sub-optimal output of our algorithm.  ...  Specifically, our algorithm accepts as input an arbitrary combinatorial filter, expressed as a transition graph, and outputs an equivalent filter that uses fewer information states to complete the same  ...  HARDNESS OF FILTER MINIMIZATION This section presents a hardness result for the filter minimization problem FM introduced in Section III.  ... 
doi:10.1109/icra.2013.6631153 dblp:conf/icra/OKaneS13 fatcat:bfs34wxwbrhbfbutgfxbos6c2a

Mapping filtering streaming applications with communication costs

Kunal Agrawal, Anne Benoit, Fanny Dufossé, Yves Robert
2009 Proceedings of the twenty-first annual symposium on Parallelism in algorithms and architectures - SPAA '09  
As already stated, the minimization problems (finding the optimal plan to minimize the period or the latency) are all NP-hard.  ...  This result is surprising, since polynomial algorithms exist for homogeneous machines when we do not model communication [1, 2] .  ...  We provide a polynomial algorithm for the OVERLAP model, and show that the problem is NP-hard for the INORDER and OUTORDER models.  ... 
doi:10.1145/1583991.1583997 dblp:conf/spaa/AgrawalBDR09 fatcat:d5fjjurk2jhfradve5ps2zbcpe

First-order logical filtering

Afsaneh Shirazi, Eyal Amir
2011 Artificial Intelligence  
In this paper we present polynomial-time algorithms for filtering belief states that are encoded as First-Order Logic (FOL) formulas. Our algorithms are exact in many cases of interest.  ...  Logical filtering is the process of updating a belief state (set of possible world states) after a sequence of executed actions and perceived observations.  ...  Acknowledgements We wish to thank Megan Nance and Adam Vogel for useful discussions on related topics.  ... 
doi:10.1016/j.artint.2010.04.015 fatcat:dclsftabfzau7ijz5ldecz5fmq

Automating the implementation of Kalman filter algorithms

Jon Whittle, Johann Schumann
2004 ACM Transactions on Mathematical Software  
algorithm.  ...  AUTOFILTER raises the level of discourse to the mathematics of the problem at hand rather than the details of what algorithms, data structures, optimizations and so on are required to implement it.  ...  code and in testing for the DS1 case study; and Ewen Denney, Pramod Gupta and Julian Richardson for comments on the article.  ... 
doi:10.1145/1039813.1039816 fatcat:uh5l3bx6cna6jhwlvmgva25diq

Toward a language-theoretic foundation for planning and filtering [article]

Fatemeh Zahra Saberifar, Shervin Ghasemlou, Dylan A. Shell, Jason M. O'Kane
2018 arXiv   pre-print
We introduce a new formal structure that generalizes and consolidates a variety of well-known structures including many forms of plans, planning problems, and filters, into a single data structure called  ...  We also highlight the connections between this new approach and existing threads of research, including combinatorial filtering, Erdmann's strategy complexes, and hybrid automata.  ...  O'Kane and Dylan A. Shell  ... 
arXiv:1807.08856v1 fatcat:n3hverw3ubfnjdo6oe6t7bnzre

Personalized active learning for collaborative filtering

Abhay S. Harpale, Yiming Yang
2008 Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '08  
Collaborative Filtering (CF) requires user-rated training examples for statistical inference about the preferences of new users.  ...  We propose an extended form of Bayesian active learning and use the Aspect Model for CF to illustrate and examine the idea.  ...  ACKNOWLEDGMENTS This work is supported in parts by the National Science Foundation (NSF) under grant IIS-0434035 and IIS-0704689.  ... 
doi:10.1145/1390334.1390352 dblp:conf/sigir/HarpaleY08 fatcat:ew4dqarjprfddhf3omwysfs7yq

Spectral Filtering for General Linear Dynamical Systems [article]

Elad Hazan, Holden Lee, Karan Singh, Cyril Zhang, Yi Zhang
2018 arXiv   pre-print
We give a polynomial-time algorithm for learning latent-state linear dynamical systems without system identification, and without assumptions on the spectral radius of the system's transition matrix.  ...  The algorithm extends the recently introduced technique of spectral filtering, previously applied only to systems with a symmetric transition matrix, using a novel convex relaxation to allow for the efficient  ...  Our plan is as follows.  ... 
arXiv:1802.03981v1 fatcat:bjszlmwn4rgabgrp73btunt5ny

MAFF-Net: Filter False Positive for 3D Vehicle Detection with Multi-modal Adaptive Feature Fusion [article]

Zehan Zhang, Ming Zhang, Zhidong Liang, Xian Zhao, Ming Yang, Wenming Tan, ShiLiang Pu
2020 arXiv   pre-print
is suitable for filtering more difficult false positive and has better overall performance.  ...  Based on the above mechanism, two fusion technologies are proposed to adapt to different usage scenarios: PointAttentionFusion is suitable for filtering simple false positive and faster; DenseAttentionFusion  ...  Multimodal Adaptive Feature Fusion(MAFF) In this study, two concise technologies that can fuse raw RGB data with the point cloud data are proposed to filter false positive in 3D detection.  ... 
arXiv:2009.10945v1 fatcat:rrn24sfo6feaxd3i5kq6zkbe5y

Situated language understanding as filtering perceived affordances

Peter Gorniak, Deb Roy
2007 Cognitive Science  
We introduce a computational theory of situated language understanding in which the meaning of words and utterances depends on the physical environment and the goals and plans of communication partners  ...  Language understanding is treated as a process of filtering perceived affordances.  ...  Descriptions The second problem with an omniscient plan recognizer is that it makes it hard to interpret descriptions.  ... 
doi:10.1080/15326900701221199 pmid:21635295 fatcat:zgtfapuujnfyvj3uuxa2zofo4u

Correlation Filters for Unmanned Aerial Vehicle-Based Aerial Tracking: A Review and Experimental Evaluation [article]

Changhong Fu, Bowen Li, Fangqiang Ding, Fuling Lin, Geng Lu
2021 arXiv   pre-print
Recently, the discriminative correlation filter (DCF)-based trackers have stood out for their high computational efficiency and appealing robustness on a single CPU, and have flourished in the UAV visual  ...  Besides, exhaustive and quantitative experiments have been extended on various prevailing UAV tracking benchmarks, i.e., UAV123, UAV123@10fps, UAV20L, UAVDT, DTB70, and VisDrone2019-SOT, which contain  ...  ACKNOWLEDGMENTS The work is supported by the National Natural Science Foundation of China under Grant 61806148 and the Natural Science Foundation of Shanghai under Grant 20ZR1460100.  ... 
arXiv:2010.06255v5 fatcat:jqqwvlynmrcgdly6ydvqssqidy

Filtering algorithms for the multiset ordering constraint

Alan M. Frisch, Brahim Hnich, Zeynep Kiziltan, Ian Miguel, Toby Walsh
2009 Artificial Intelligence  
We propose efficient and effective filtering algorithms for propagating this global constraint. We show that the algorithms are sound and complete and we discuss possible extensions.  ...  Our experimental results on a number of benchmark problems demonstrate that propagating the multiset ordering constraint via a dedicated algorithm can be very beneficial.  ...  Hnich is supported by Scientific and Technological Research Council of Turkey (TUBITAK) under Grant No: SOBAG-108K027. I.  ... 
doi:10.1016/j.artint.2008.11.001 fatcat:srv3l2a3svdq3omysx7fokuwny

Exploiting Causality for Selective Belief Filtering in Dynamic Bayesian Networks

Stefano V. Albrecht, Subramanian Ramamoorthy
2016 The Journal of Artificial Intelligence Research  
This can be a hard problem in complex processes with large state spaces. In this article, we explore the idea of accelerating the filtering task by automatically exploiting causality in the process.  ...  Belief filtering in DBNs is the task of inferring the belief state (i.e. the probability distribution over process states) based on incomplete and noisy observations.  ...  Acknowledgements This article is the result of a long debate on the presented topic, and in the process benefited from a number of discussions and suggestions.  ... 
doi:10.1613/jair.5044 fatcat:keacctvpcbfvnbtghvavqnjbuy
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