2,440 research outputs found
Combining Subgoal Graphs with Reinforcement Learning to Build a Rational Pathfinder
In this paper, we present a hierarchical path planning framework called SG-RL
(subgoal graphs-reinforcement learning), to plan rational paths for agents
maneuvering in continuous and uncertain environments. By "rational", we mean
(1) efficient path planning to eliminate first-move lags; (2) collision-free
and smooth for agents with kinematic constraints satisfied. SG-RL works in a
two-level manner. At the first level, SG-RL uses a geometric path-planning
method, i.e., Simple Subgoal Graphs (SSG), to efficiently find optimal abstract
paths, also called subgoal sequences. At the second level, SG-RL uses an RL
method, i.e., Least-Squares Policy Iteration (LSPI), to learn near-optimal
motion-planning policies which can generate kinematically feasible and
collision-free trajectories between adjacent subgoals. The first advantage of
the proposed method is that SSG can solve the limitations of sparse reward and
local minima trap for RL agents; thus, LSPI can be used to generate paths in
complex environments. The second advantage is that, when the environment
changes slightly (i.e., unexpected obstacles appearing), SG-RL does not need to
reconstruct subgoal graphs and replan subgoal sequences using SSG, since LSPI
can deal with uncertainties by exploiting its generalization ability to handle
changes in environments. Simulation experiments in representative scenarios
demonstrate that, compared with existing methods, SG-RL can work well on
large-scale maps with relatively low action-switching frequencies and shorter
path lengths, and SG-RL can deal with small changes in environments. We further
demonstrate that the design of reward functions and the types of training
environments are important factors for learning feasible policies.Comment: 20 page
Factors Influencing Purchase Intention on Mobile Shopping Web Site in China and South Korea: An Empirical Study
The research objective of this study is to analyze the factors that influence consumers\u27 perceptions of using mobile commerce services for online shopping in China and South Korea using ordered logistic regression analysis. We constructed the research model using the three dimensions of individual characteristics, shopping motivations and the characteristics of mobile shopping. We discovered that shopping frequency, utilitarianism, instant connectivity, and personalized information push positively impact the customers’ intention to use mobile phones in China. The results of the marginal effects indicated that the behavioral intentions of Chinese consumers increased when shopping frequency and instant connectivity increased. In addition, when utilitarianism and the personalized information push reach certain values, the shopping intention of online customers in China will decrease. Likewise, shopping frequency, hedonism, utilitarianism, instant connectivity, and SNS (Social Networking Services) accessibility positively affect the intention to use the Internet for m-shopping of South Korean consumers. In addition, the results regarding the marginal effects suggested that the intention to use m-shopping services on m-shopping web site of South Korean consumers increased as shopping frequency, hedonism, and instant connectivity increased. However, South Korean consumers\u27 adoption intention will decrease when utilitarianism and SNS accessibility reach certain values. These results provide important implications for mobile commerce literature and practice
Transductive Kernels for Gaussian Processes on Graphs
Kernels on graphs have had limited options for node-level problems. To
address this, we present a novel, generalized kernel for graphs with node
feature data for semi-supervised learning. The kernel is derived from a
regularization framework by treating the graph and feature data as two Hilbert
spaces. We also show how numerous kernel-based models on graphs are instances
of our design. A kernel defined this way has transductive properties, and this
leads to improved ability to learn on fewer training points, as well as better
handling of highly non-Euclidean data. We demonstrate these advantages using
synthetic data where the distribution of the whole graph can inform the pattern
of the labels. Finally, by utilizing a flexible polynomial of the graph
Laplacian within the kernel, the model also performed effectively in
semi-supervised classification on graphs of various levels of homophily
OTS: A One-shot Learning Approach for Text Spotting in Historical Manuscripts
Historical manuscript processing poses challenges like limited annotated
training data and novel class emergence. To address this, we propose a novel
One-shot learning-based Text Spotting (OTS) approach that accurately and
reliably spots novel characters with just one annotated support sample. Drawing
inspiration from cognitive research, we introduce a spatial alignment module
that finds, focuses on, and learns the most discriminative spatial regions in
the query image based on one support image. Especially, since the low-resource
spotting task often faces the problem of example imbalance, we propose a novel
loss function called torus loss which can make the embedding space of distance
metric more discriminative. Our approach is highly efficient and requires only
a few training samples while exhibiting the remarkable ability to handle novel
characters, and symbols. To enhance dataset diversity, a new manuscript dataset
that contains the ancient Dongba hieroglyphics (DBH) is created. We conduct
experiments on publicly available VML-HD, TKH, NC datasets, and the new
proposed DBH dataset. The experimental results demonstrate that OTS outperforms
the state-of-the-art methods in one-shot text spotting. Overall, our proposed
method offers promising applications in the field of text spotting in
historical manuscripts
TF-IDF Based Contextual Post-Filtering Recommendation Algorithm in Complex Interactive Situations of Online to Offline: An Empirical Study
O2O accelerates the integration of online and offline, promotes the upgrading of industrial structure and consumption pattern, meanwhile brings the information overload problem. This paper develops a post-context filtering recommendation algorithm based on TF-IDF, which improves the existing algorithms. Combined with contextual association probability and contextual universal importance, a contextual preference prediction model was constructed to adjust the initial score of the traditional recommendation combined with item category preference to generate the final result. The example of the catering industry shows that the proposed algorithm is more effective than the improved algorithm
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