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Human-guided simple search

David Anderson, Emily Anderson, Neal Lesh, Joe Marks, Ken Perlin, David Ratajczak, Kathy Ryall
1999 Proceedings of the 1999 workshop on new paradigms in information visualization and manipulation in conjunction with the eighth ACM internation conference on Information and knowledge management - NPIVM '99  
search; using visualization and interaction techniques, the human user identifies promising regions of the search space for the computer to explore, and intervenes to help it escape nonoptimal local minima  ...  Our idea is to divide these two subtasks cleanly between human and computer: in our paradigm of human-guided simple search the computer is responsible only for finding local minima using a simple hill-climbing  ...  their comments and discussion.  ... 
doi:10.1145/331770.331778 fatcat:65226fbv4zhg5ikmkxbznsyvji

Object search by manipulation

Mehmet R. Dogar, Michael C. Koval, Abhijeet Tallavajhula, Siddhartha S. Srinivasa
2013 Autonomous Robots  
We also propose a greedy algorithm and show that it is optimal under certain conditions.  ...  We contribute a formulation of the object search by manipulation problem using visibility and accessibility relations between objects.  ...  This material is based upon work supported by NSF-IIS-0916557 and NSF-EEC-0540865.  ... 
doi:10.1007/s10514-013-9372-x fatcat:o53n4gmvb5g7bam5dm3fslnrgm

Interactive Natural Language-based Person Search

Vikram Shree, Wei-Lun Chao, Mark Campbell
2020 IEEE Robotics and Automation Letters  
An algorithm is proposed by adapting models, used for visual and language understanding, to search a person of interest (POI) in a principled way, achieving promising results without the need to re-design  ...  To this end, we introduce a greedy algorithm to rank questions in terms of their significance, and equip the algorithm with the capability to dynamically adjust the length of human-robot interaction according  ...  A Greedy Strategy for Information Retrieval Not all the query questions are equally valuable for the person search problem.  ... 
doi:10.1109/lra.2020.2969921 fatcat:xchyk4x44zg6rj6lizs5ffpxu4

Multi-Objective Graph Heuristic Search for Terrestrial Robot Design [article]

Jie Xu, Andrew Spielberg, Allan Zhao, Daniela Rus, Wojciech Matusik
2021 arXiv   pre-print
We compare the captured Pareto fronts across different methods and demonstrate that our multi-objective graph heuristic search quantitatively and qualitatively outperforms other techniques.  ...  In this work, we present Multi-Objective Graph Heuristic Search, which extends a single-objective graph heuristic search from previous work to enable a highly efficient multi-objective search in a combinatorial  ...  This work is supported by Intelligence Advanced Research Projects Agency (grant No. 2019-19020100001), and Defense Advanced Research Projects Agency (grant No. FA8750-20-C-0075).  ... 
arXiv:2107.05858v1 fatcat:qbejiqlh7vgpbpbw3ujv3ma7lu

A Comparison of Meta-heuristic Search for Interactive Software Design [article]

C. L. Simons, J. E. Smith
2012 arXiv   pre-print
We then evaluate three methods, namely greedy local search, an evolutionary algorithm and ant colony optimization, with a variety of representations for candidate solutions.  ...  Of particular note is the coupling of meta-heuristic search engines with user-provided evaluation and rating of solutions, usually in the form of Interactive Evolutionary Algorithms (IEAs).  ...  Rather, approaches rely on using either a meta-heuristic with a defined quality function and periodically using user interaction to guide search by reformulating a fitness function or preference weighting  ... 
arXiv:1211.3371v1 fatcat:tm2t2er3mrcwhctdprs4sx7ohi

Analysis and Augmentation of Human Performance on Telerobotic Search Problems

Kuo-Shih Tseng, Berenice Mettler
2020 IEEE Access  
Since the objective functions in search problems are submodular, greedy algorithms can generate near-optimal subgoals. These subgoals then can be used to guide humans in searching.  ...  The enhanced understanding of human search strategies can then be applied to the design of human-robot interfaces and search algorithms. The paper describes a technique for augmenting human search.  ...  ACKNOWLEDGMENT The authors thank the ten human subjects for their participation in the experiments.  ... 
doi:10.1109/access.2020.2981978 fatcat:bltvfi77ljf3poecnhtoiftykm

Attention-based Active Visual Search for Mobile Robots [article]

Amir Rasouli, Pablo Lanillos, Gordon Cheng, John K. Tsotsos
2018 arXiv   pre-print
In this paper, we propose a new model that actively extracts visual information via visual attention techniques and, in conjunction with a non-myopic decision-making algorithm, leads the robot to search  ...  Existing search strategies are either purely reactive or use simplified sensor models that do not exploit all the visual information available.  ...  A model for active visual search We define the visual search strategy as the fusion of information gained via new visual stimuli combined with the knowledge accumulated from past observations.  ... 
arXiv:1807.10744v1 fatcat:iqbh7cbpgzfhxchybghpfomwym

Agent-Centered Search

Sven Koenig
2001 The AI Magazine  
I In this article, I describe agent-centered search (also called real-time search or local search) and illustrate this planning paradigm with examples.  ...  I discuss the design and properties of several agent-centered search methods, focusing on robot exploration and localization. Articles  ...  These theoretical planning methods can outperform greedy heuristic planning methods.  ... 
doi:10.1609/aimag.v22i4.1596 dblp:journals/aim/Koenig01 fatcat:nfhb67ts2jawri2qmk3l35y7mi

Conflict-Based Search for Explainable Multi-Agent Path Finding [article]

Justin Kottinger, Shaull Almagor, Morteza Lahijanian
2022 arXiv   pre-print
We show how to add explainability constraints on top of the standard CBS tree and its underlying A* search.  ...  In safety-critical applications, a human supervisor may want to verify that the plan is indeed collision-free.  ...  The central element is the heuristic guiding the search.  ... 
arXiv:2202.09930v2 fatcat:oohtpatubfcq7ilxzwigw3ihze

Experience-Based Heuristic Search: Robust Motion Planning with Deep Q-Learning

Julian Bernhard, Robert Gieselmann, Klemens Esterle, Alois Knol
2018 2018 21st International Conference on Intelligent Transportation Systems (ITSC)  
Specifically, we show how experiences in the form of a Deep Q-Network can be integrated as heuristic into a heuristic search algorithm.  ...  Thus, we propose the Experience-Based-Heuristic-Search algorithm, which overcomes the statistical failure rate of a Deep-reinforcement-learning-based planner and still benefits computationally from the  ...  RELATED WORK The heuristic function plays an important role in all informed search algorithms.  ... 
doi:10.1109/itsc.2018.8569436 dblp:conf/itsc/BernhardGEK18 fatcat:misqukk7yvgqfeg5dsecbdlkpy

Learning to locate from demonstrated searches

Paul Vernaza, Anthony Stentz
2014 Robotics: Science and Systems X  
On the inference side, we advance the stateof-the-art by proposing novel relaxations that are integrated into a heuristic-driven search algorithm.  ...  We consider the problem of learning to locate targets from demonstrated searches.  ...  This work was supported in part by ONR under MURI grant 'Reasoning in Reduced Information Spaces' (no. N00014-09-1-1052).  ... 
doi:10.15607/rss.2014.x.035 dblp:conf/rss/VernazaS14 fatcat:bgpih3qxtzbc5cv3mw2cp6qpv4

AutoGPart: Intermediate Supervision Search for Generalizable 3D Part Segmentation [article]

Xueyi Liu, Xiaomeng Xu, Anyi Rao, Chuang Gan, Li Yi
2022 arXiv   pre-print
AutoGPart builds a supervision space with geometric prior knowledge encoded, and lets the machine to search for the optimal supervisions from the space for a specific segmentation task automatically.  ...  machines do not necessarily understand in the exact human way.  ...  for a task where human prior knowledge is not that available or hard to be translated to guide a network's learning process.  ... 
arXiv:2203.06558v4 fatcat:otjmwdo7sraq3m75ba5b6p45mq

Construction and optimal search of interpolated motion graphs

Alla Safonova, Jessica K. Hodgins
2007 ACM SIGGRAPH 2007 papers on - SIGGRAPH '07  
Figure 1: Optimal and sub-optimal solutions for walking a given distance (left) and for picking up an object (right).  ...  Acknowledgments The authors would like to thank Moshe Mahler for his help in modeling and rendering and Justin Macey for his assistance in collecting and cleaning the motion capture data.  ...  The second technique computes an informative heuristic function that guides the search toward states that are more likely to appear in an optimal solution.  ... 
doi:10.1145/1275808.1276510 fatcat:uxpwvxbeovaevk72xuvycsa3yi

Playing Multi-Action Adversarial Games: Online Evolutionary Planning versus Tree Search

Niels Justesen, Tobias Mahlmann, Sebastian Risi, Julian Togelius
2018 IEEE Transactions on Games  
In this paper, we introduce Online Evolutionary Planning (OEP) to address this challenge, which searches for combinations of actions to perform during a single turn guided by a fitness function that evaluates  ...  This leads to the breakdown of standard tree search methods, including Monte Carlo Tree Search (MCTS), as they become unable to reach a sufficient depth in the game tree.  ...  In fact, many well performing game-playing programs rely on search algorithms that are guided by some heuristic function that evaluates the desirability of a given state.  ... 
doi:10.1109/tciaig.2017.2738156 fatcat:yiliiyfmnvgvpd7tdwbr5uye7a

Learning search polices from humans in a partially observable context

Guillaume de Chambrier, Aude Billard
2014 Robotics and Biomimetics  
We contrast the performance of this human-inspired search model with respect to greedy and coastal navigation search methods.  ...  We further categorize the type of behaviours demonstrated by humans as being either risk-prone or risk-averse and find that more than 70% of the human searches were considered to be risk-averse.  ...  The hybrid was not fully considered since it is a heuristic combination of the greedy and GMM methods.  ... 
doi:10.1186/s40638-014-0008-1 fatcat:5bxynnzp5zbt5keu2cgljnfxhi
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