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Learning Heuristic Search via Imitation
[article]
2017
arXiv
pre-print
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We present SaIL, an efficient algorithm that trains heuristic policies by imitating "clairvoyant oracles" - oracles that have full information about the world and demonstrate decisions that minimize search ...
Our approach paves the way forward for learning heuristics that demonstrate an anytime nature - finding feasible solutions quickly and incrementally refining it over time. ...
We demonstrate that we are able to learn heuristic policies with widely varying characteristics simply by training on different data distributions (Section 4.3). ...
arXiv:1707.03034v1
fatcat:wvaf7se5kndohlisqlhphj7bra
Learning to Speed Up Query Planning in Graph Databases
[article]
2018
arXiv
pre-print
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Subsequently, we learn greedy search control knowledge to imitate the search behavior of the target query plans. ...
In this paper, we study the problem of learning to speedup query planning in graph databases towards the goal of improving the computational-efficiency of query processing via training queries.We present ...
heuristic // Pruning 14: end while 15: return state-action pair sequence from s0 to s *
Learning Greedy Policies via Imitation Learning Our goal is to learn a greedy policy (Π select , Π f etch ) that ...
arXiv:1801.06766v1
fatcat:2yexjo3eyjfmlorogc3sqawi7a
HC-Search: A Learning Framework for Search-based Structured Prediction
2014
The Journal of Artificial Intelligence Research
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Given a structured input, the framework uses a search procedure guided by a learned heuristic H to uncover high quality candidate outputs and then employs a separate learned cost function C to select a ...
Guided by this decomposition, we minimize the overall loss in a greedy stage-wise manner by first training H to quickly uncover high quality outputs via imitation learning, and then training C to correctly ...
Following our staged learning approach, one could start with learning a heuristic via exact imitation of the oracle search. ...
doi:10.1613/jair.4212
fatcat:ixnlsxzwurcv5kaejmsyu2x37e
A Survey for Solving Mixed Integer Programming via Machine Learning
[article]
2022
arXiv
pre-print
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Then, we advocate further promoting the different integration of machine learning and MIP and introducing related learning-based methods, which can be classified into exact algorithms and heuristic algorithms ...
Like other CO problems, the human-designed heuristic algorithms for MIP rely on good initial solutions and cost a lot of computational resources. ...
Heuristic Algorithms Though MIP can be solved via B&B exactly, it is time and resource-consuming due to its NP-hard nature. ...
arXiv:2203.02878v1
fatcat:wiezy5ilird3fgbf3qdjzj4wmu
ℋC-search for structured prediction in computer vision
2015
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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We complement this search operator by applying the DAGGER algorithm to robustly train the search heuristic so it learns from its previous mistakes. ...
Search-based methods provide an alternative that has the potential to achieve higher performance. These methods learn to control a search procedure that constructs and evaluates candidate solutions. ...
Exact Imitation Learning: In exact imitation learning, the heuristic function H is learned by "imitating" the search trajectory of the oracle heuristic. ...
doi:10.1109/cvpr.2015.7299126
dblp:conf/cvpr/LamDTD15
fatcat:cag6ahnl4bealizhguaahvtupm
Learning to Search via Retrospective Imitation
[article]
2019
arXiv
pre-print
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2018 pre-print arXiv:1804.00846v3 fatcat:fsfqbaqw7zfb7cm7eixbzoajnyOther Versions
We study the problem of learning a good search policy for combinatorial search spaces. ...
We propose retrospective imitation learning, which, after initial training by an expert, improves itself by learning from retrospective inspections of its own roll-outs. ...
In this paper, we take a learning approach to finding an effective search heuristic. ...
arXiv:1804.00846v4
fatcat:pw4vb2mi6ffppoct3wsdmceo4y
Data-driven Planning via Imitation Learning
[article]
2017
arXiv
pre-print
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We hence present a novel data-driven imitation learning framework to efficiently train planning policies by imitating a clairvoyant oracle - an oracle that at train time has full knowledge about the world ...
In this paper, we address the problem of enabling planners to adapt their search strategies by inferring such good actions in an efficient manner using only the information uncovered by the search up until ...
Solving POMDP via Imitation of a Clairvoyant Oracle To examine the applicability of imitation learning in the POMDP framework, we compare the loss function (9) to the action value function (5) . ...
arXiv:1711.06391v1
fatcat:2nltaus5bfhdldnuequtw3c4fe
Interorganizational imitation heuristics arising from cognitive frames
2014
Journal of Business Research
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imitation heuristics. ...
The literature on organizational imitation mostly disregards its cognitive aspect. Yet, imitation is a cognitive heuristic for complex strategic decisions. ...
Thus, when the adoption issue is viewed as an opportunity, but the non-adoption is perceived as a threat, social learning would occur either via conformist transmission or via prestige-biased transmission ...
doi:10.1016/j.jbusres.2014.03.001
fatcat:nzv2rsvkyjan3py4bzk22beyhy
Bounded Suboptimal Search with Learned Heuristics for Multi-Agent Systems
2019
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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Building upon recent results on applying deep learning to learn generalized reactive policies, we propose to learn heuristics by imitation learning. ...
However, directly applying learned heuristics in search algorithms such as A∗ breaks optimality guarantees, since learned heuristics are not necessarily admissible. ...
Multi-Agent Policy via Imitation Learning As mentioned earlier, we first train a DNN to imitate expert behaviors. ...
doi:10.1609/aaai.v33i01.33012387
fatcat:26zm7zxebzfrppybpflukmi66q
Learning TSP Requires Rethinking Generalization
[article]
2021
arXiv
pre-print
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2021 pre-print arXiv:2006.07054v2 fatcat:ez23vjfabnf4xppaxt6dj7rbryOther Versions
Towards leveraging transfer learning to solve large-scale TSPs, this paper identifies inductive biases, model architectures and learning algorithms that promote generalization to instances larger than ...
While state-of-the-art Machine Learning approaches perform closely to classical solvers when trained on trivially small sizes, they are unable to generalize the learnt policy to larger instances of practical ...
Models can be trained to imitate optimal solvers via supervised learning or by minimizing the length of TSP tours via reinforcement learning. ...
arXiv:2006.07054v3
fatcat:fsxtrv2tzveabftlxzjuzpdura
Extracting Relations between Non-Standard Entities using Distant Supervision and Imitation Learning
2015
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing
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We propose to ameliorate this issue by jointly training the named entity classifier and the relation extractor using imitation learning which reduces structured prediction learning to classification learning ...
Compared to using FIGER and Stanford NER, average precision is 10 points and 19 points higher with our imitation learning approach. ...
Also referred to as search-based structured prediction or learning to search. ...
doi:10.18653/v1/d15-1086
dblp:conf/emnlp/AugensteinVM15
fatcat:txoviervmzhz3fkvsph4h4eboe
A General Large Neighborhood Search Framework for Solving Integer Linear Programs
[article]
2020
arXiv
pre-print
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We show that one can learn a good neighborhood selector using imitation and reinforcement learning techniques. ...
We focus on solving integer programs, and ground our approach in the large neighborhood search (LNS) paradigm, which iteratively chooses a subset of variables to optimize while leaving the remainder fixed ...
Learning to Search. In learning to search, one typically operates within the framework of a search heuristic, and trains a local decision policy from training data. ...
arXiv:2004.00422v3
fatcat:w4rfod67pfgqhlquol3mbamvju
Heuristic Search Planning with Deep Neural Networks using Imitation, Attention and Curriculum Learning
[article]
2021
arXiv
pre-print
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This paper presents a network model to learn a heuristic capable of relating distant parts of the state space via optimal plan imitation using the attention mechanism, which drastically improves the learning ...
Learning a well-informed heuristic function for hard task planning domains is an elusive problem. ...
Neural search policies (Gomoluch et al. 2020) rely on parameter learning to generate heuristics during the actual search process. ...
arXiv:2112.01918v1
fatcat:6ihocezymngurk2bcgzvdfpbcm
Adaptive Imitation Scheme for Memetic Algorithms
[chapter]
2011
IFIP Advances in Information and Communication Technology
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These algorithms are similar in nature to genetic algorithms as they follow evolutionary strategies, but they also incorporate a refinement phase during which they learn about the problem and search space ...
The efficiency of these algorithms depends on the nature and architecture of the imitation operator used. ...
the exploration capacities of underlying GA and the exploitation capabilities of local search heuristic. ...
doi:10.1007/978-3-642-19170-1_12
fatcat:yxt5pyb5ljcvdmvxzxzvze6gfi
Learning Generalized Reactive Policies using Deep Neural Networks
[article]
2018
arXiv
pre-print
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We show that our approach can also be used to automatically learn a heuristic function that can be used in directed search algorithms. ...
heuristic functions with minimal human input. ...
As seen in Figure 3b the learned GRP heuristic significantly outperforms the Manhattan heuristic in both greedy search and A* search, on the 9x9 problems. ...
arXiv:1708.07280v3
fatcat:fybtlstknbck7popa3lbgxgob4
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