Discovering Underlying Plans Based on Shallow Models

Zhuo, Hankz Hankui; Zha, Yantian; Kambhampati, Subbarao; Tian, Xiaohui

doi:10.1145/3368270

Cited by 11 publications

(8 citation statements)

References 27 publications

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“…In the feature, we will investigate how to explore temporal information and semantic hierarchies based on our model. It is also interesting to investigate the possibility of applying our DualQuatE model to learning representations of propositions for helping learning action models [25][26][27][28] and recognizing plans [29][30][31] in planning community.…”

Section: Discussionmentioning

confidence: 99%

Dual Quaternion Embeddings for Link Prediction

et al. 2021

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The applications of knowledge graph have received much attention in the field of artificial intelligence. The quality of knowledge graphs is, however, often influenced by missing facts. To predict the missing facts, various solid transformation based models have been proposed by mapping knowledge graphs into low dimensional spaces. However, most of the existing transformation based approaches ignore that there are multiple relations between two entities, which is common in the real world. In order to address this challenge, we propose a novel approach called DualQuatE that maps entities and relations into a dual quaternion space. Specifically, entities are represented by pure quaternions and relations are modeled based on the combination of rotation and translation from head to tail entities. After that we utilize interactions of different translations and rotations to distinguish various relations between head and tail entities. Experimental results exhibit that the performance of DualQuatE is competitive compared to the existing state-of-the-art models.

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Section: Discussionmentioning

confidence: 99%

Dual Quaternion Embeddings for Link Prediction

et al. 2021

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“…In the future, we will develop a budget feasible mechanism that solves the uncertainties in completion time of trailer tasks and build an iterative scenario generation approach in which a trailer can pick bikes from one or several stations and then drop them at one or more stations. It is also interesting to investigate the possibility of learning action models [29][30][31][32] and recognizing plans [33][34][35] to help improving bike repositioning under the framework of DRRPVT.…”

Section: Discussionmentioning

confidence: 99%

Repositioning Bikes with Carrier Vehicles and Bike Trailers in Bike Sharing Systems

et al. 2021

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Bike sharing systems (BSSs) are widely adopted in major cities of the world due to traffic congestion and carbon emissions. Although there have been approaches to exploit either bike trailers via crowdsourcing or carrier vehicles to reposition bikes in the “right” stations in the “right” time, they did not jointly consider the usage of both bike trailers and carrier vehicles. In this paper, we aim to take advantage of both bike trailers and carrier vehicles to reduce the loss of demand by determining whether bike trailers or carrier vehicles (or both) should be used. In addition, we also would like to maximize the overall profit with regard to the crowdsourcing of bike trailers and the fuel cost of carrier vehicles. In the experiment, we exhibit that our approach outperforms baselines in multiple data sets from bike sharing companies.

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“…In the future, we would like to extend our work to complex domains and consider objects in our framework that can better leverage the benefit of both deep learning and classical AI planning. It is also interesting to investigate the possibility of applying our approach to learning action models [ 33 , 34 , 35 , 36 ] and recognizing plans [ 37 , 38 , 39 ] in the planning community.…”

Section: Discussionmentioning

confidence: 99%

Action Generative Networks Planning for Deformable Object with Raw Observations

Sheng

Jin

et al. 2021

Sensors

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Synthesizing plans for a deformable object to transit from initial observations to goal observations, both of which are represented by high-dimensional data (namely “raw” data), is challenging due to the difficulty of learning abstract state representations of raw data and transition models of continuous states and continuous actions. Even though there have been some approaches making remarkable progress regarding the planning problem, they often neglect actions between observations and are unable to generate action sequences from initial observations to goal observations. In this paper, we propose a novel algorithm framework, namely AGN. We first learn a state-abstractor model to abstract states from raw observations, a state-generator model to generate raw observations from states, a heuristic model to predict actions to be executed in current states, and a transition model to transform current states to next states after executing specific actions. After that, we directly generate plans for a deformable object by performing the four models. We evaluate our approach in continuous domains and show that our approach is effective with comparison to state-of-the-art algorithms.

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Discovering Underlying Plans Based on Shallow Models

Cited by 11 publications

References 27 publications

Dual Quaternion Embeddings for Link Prediction

Dual Quaternion Embeddings for Link Prediction

Repositioning Bikes with Carrier Vehicles and Bike Trailers in Bike Sharing Systems

Action Generative Networks Planning for Deformable Object with Raw Observations

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