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Learning STRIPS Operators from Noisy and Incomplete Observations [article]

Kira Mourao, Luke S. Zettlemoyer, Ronald P. A. Petrick, Mark Steedman
2012 arXiv   pre-print
Even in standard STRIPS domains, existing approaches cannot learn from noisy, incomplete observations typical of real-world domains.  ...  We evaluate our approach on simulated standard planning domains from the International Planning Competition, and show that it learns useful domain descriptions from noisy, incomplete observations.  ...  This work was partially funded by the European Commission through the EU Cognitive Systems project Xperience (FP7-ICT-270273) and the UK EPSRC/MRC through the Neuroinformatics and Computational Neuroscience  ... 
arXiv:1210.4889v1 fatcat:k66lyi4poff6bhdjbll32sedom

A Review on Learning Planning Action Models for Socio-Communicative HRI [article]

Ankuj Arora and Humbert Fiorino and Damien Pellier and Sylvie Pesty
2018 arXiv   pre-print
Classic approaches to program HRI from observed human-human interactions fails to capture the subtlety of multimodal interactions as well as the key structural differences between robots and humans.  ...  AI techniques, such as machine learning, can be used to learn behavioral models (also known as symbolic action models in AI), intended to be reusable for AI planning, from the aforementioned multimodal  ...  AMAN AMAN (Action-Model Acquisition from Noisy plan traces) [28] finds a domain model that best explains the observed noisy plan traces.  ... 
arXiv:1810.09245v1 fatcat:5ga2klfbcfaife7wylepgsxtzq

A review of learning planning action models

Ankuj Arora, Humbert Fiorino, Damien Pellier, Marc Métivier, Sylvie Pesty
2018 Knowledge engineering review (Print)  
It is also a conscious effort to decrease laborious manual coding and increase quality. This paper presents a survey of the machine learning techniques applied for learning planning action models.  ...  It then details the learning techniques that have been used in the literature during the past decades, and finally presents some open issues.  ...  The learning module learns operator preconditions and effects by observing experts executing sample problems, then refining the operators collected from these observations.  ... 
doi:10.1017/s0269888918000188 fatcat:yjwlfpz4efhnrbhxczr2mxh4km

Learning Numerical Action Models from Noisy Input Data [article]

José Á. Segura-Muros and Juan Fernández-Olivares and Raúl Pérez
2021 arXiv   pre-print
The PlanMiner algorithm is able to infer arithmetic and logical expressions to learn numerical planning domains from the input data, but it was designed to work under situations of incompleteness making  ...  In this paper, we propose a series of enhancements to the learning process of PlanMiner to expand its capabilities to learn from noisy data.  ...  Acknowledgements This research is being developed and partially funded by the Spanish MINECO R&D Project RTI2018-098460-B-I00  ... 
arXiv:2111.04997v1 fatcat:dm2vugtj4bcb7p5kkx2g3mag5a

Hyperspectral Image Inpainting Based on Robust Spectral Dictionary Learning

Xiaorui Song, Lingda Wu
2019 Applied Sciences  
signal reconstruction problem with incomplete observations using the theory of sparse representation, and proposed an HSI inpainting algorithm based on spectral dictionary learning.  ...  We subsequently proposed a new algorithm for constructing a spectral dictionary directly from hyperspectral data by introducing an online learning optimization method and performing dictionary learning  ...  in incomplete observations of the target area.  ... 
doi:10.3390/app9153062 fatcat:p2nkz5gh7vdtvke2rgsge67j4i

AMLSI: A Novel Accurate Action Model Learning Algorithm [article]

Maxence Grand, Humbert Fiorino, Damien Pellier
2020 arXiv   pre-print
Unlike other algorithms, we show that AMLSI is able to lift this lock by learning domains from partial and noisy observations with sufficient accuracy to allow planners to solve new problems.  ...  AMLSI proceeds by trial and error: it queries the system to learn with randomly generated action sequences, and it observes the state transitions of the system, then AMLSI returns a PDDL domain corresponding  ...  operators are necessary because the observations are partial and noisy.  ... 
arXiv:2011.13277v1 fatcat:epdg76a2jvbrpebxcn6o2pjkn4

Fit capability indices and their applications

Z. ZHANG, X. D. FANG‡
1996 International Journal of Production Research  
from observations and interactions.  ...  This is, however, unavoidable for planning using partial and incomplete models (e.g., considering planning using action models learned from partial and noisy plan traces).  ...  Acknowledgments This research is supported in part by the ARO grant W911NF-13-1-0023, and the ONR grants N00014-13-1-0176, N00014-13-1-0519 and N00014-15-1-2027.  ... 
doi:10.1080/00207549608905078 fatcat:x4tzyyih7jbpjhjq2ya44i4bqi

Learning to Infer Final Plans in Human Team Planning

Joseph Kim, Matthew E. Woicik, Matthew C. Gombolay, Sung-Hyun Son, Julie A. Shah
2018 Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence  
We present a novel learning technique to infer teams' final plans directly from a processed form of their planning conversation.  ...  Our method employs reinforcement learning to train a model that maps features of the discussed plan and patterns of dialogue exchange among participants to a final, agreed-upon plan.  ...  For each domain, we analyzed the ratios of noisy and missing observations, which ranged from 0.23-0.46 and 0.05-0.49, respectively.  ... 
doi:10.24963/ijcai.2018/663 dblp:conf/ijcai/KimWGSS18 fatcat:svxgflm6knalzkray5tmu4zvci

PCAN: 3D Attention Map Learning Using Contextual Information for Point Cloud Based Retrieval [article]

Wenxiao Zhang, Chunxia Xiao
2019 arXiv   pre-print
All these works focus on extracting features from 3D data at a global level, but most of them aim at handling complete 3D models instead of 3D scanned data which is incomplete and noisy.  ...  Most of these hand-crafted descriptors are designed for specific tasks and they are sensitive to noisy, incomplete RGB-D images captured by sensors.  ... 
arXiv:1904.09793v1 fatcat:i6ggdi64ejhbtgajfcxu6fjx4a

Neurodegenerative disease diagnosis using incomplete multi-modality data via matrix shrinkage and completion

Kim-Han Thung, Chong-Yaw Wee, Pew-Thian Yap, Dinggang Shen
2014 NeuroImage  
might be incomplete.  ...  Treating each target output as the outcome of a prediction task, we apply a 2-step multi-task learning algorithm to select the most discriminative features and samples in each submatrix.  ...  ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Abbott, AstraZeneca AB, Amorfix  ... 
doi:10.1016/j.neuroimage.2014.01.033 pmid:24480301 pmcid:PMC4096013 fatcat:xmzdv3oiyrbxnja5s3n3rlm2hm

Heuristic Approaches for Goal Recognition in Incomplete Domain Models [article]

Ramon Fraga Pereira, Felipe Meneguzzi
2018 arXiv   pre-print
We show the efficiency and accuracy of our approaches empirically against a large dataset of goal and plan recognition problems with incomplete domains.  ...  Recent approaches to goal recognition have progressively relaxed the assumptions about the amount and correctness of domain knowledge and available observations, yielding accurate and efficient algorithms  ...  The latter stems from imperfections in the way actions themselves may be interpreted from real-world noisy data, e.g., if one uses machine learning algorithms to classify objects to be used as features  ... 
arXiv:1804.05917v1 fatcat:vjxlz6tg6bas3jh35w5r3xna7e

Learning action models with minimal observability

Diego Aineto, Sergio Jiménez Celorrio, Eva Onaindia
2019 Artificial Intelligence  
Abstract This paper presents FAMA, a novel approach for learning STRIPS action models from observations of plan executions that compiles the learning task into a classical planning task.  ...  Learning action models with minimal observability.  ...  Diego Aineto is partially supported by the FPU16/03184 and Sergio Jiménez by the RYC15/18009, both programs funded by the Spanish government.  ... 
doi:10.1016/j.artint.2019.05.003 fatcat:rs65cvp37ffi3chui633puvue4

INTEGRATING PLANNING, EXECUTION, AND LEARNING TO IMPROVE PLAN EXECUTION

Sergio Jiménez, Fernando Fernández, Daniel Borrajo
2012 Computational intelligence  
Keywords cognitive architectures, relational reinforcement learning, symbolic planning. Plan Observations (a1,a2,...  ...  The new component allows the architecture to learn probabilistic rules of the success of actions from the execution of plans and to automatically upgrade the planning model with these rules.  ...  The EXPO system (Gil 1992) refine incomplete planning operators, that is, operators with some missing preconditions and effects.  ... 
doi:10.1111/j.1467-8640.2012.00447.x fatcat:l6scnc6hgbegzk2zkxnk6jz2oa

Goal Recognition over Imperfect Domain Models [article]

Ramon Fraga Pereira
2020 arXiv   pre-print
approximated from past observations and not well-defined.  ...  levels of observability and imperfections.  ...  noisy or missing observations), and recognizes both goals and plans.  ... 
arXiv:2005.05712v1 fatcat:totc3sll7be6fdas7ud562lkoe

Explanation-based learning: a survey of programs and perspectives

Thomas Ellman
1989 ACM Computing Surveys  
Explanation-based learning (EBL) is a technique by which an intelligent system can learn by observing examples.  ...  Subsequently EBL is placed in its historical context and the relation between EBL and other areas of machine learning is described.  ...  This research is surveyed by Angluin and Smith [1983] , Cohen and Feigenbaum [1982], Michalski [1983] , Michalski et al. Fred  ... 
doi:10.1145/66443.66445 fatcat:o25qzli5sza3nczyifhhcl2roi
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