arXiv subjects

Man Luo

Publications and source records attributed to Man Luo.

74 records · Page 5Linked to original sources

arXiv2019

Strong equivalence for $\rm LP^{MLN}$ programs

Strong equivalence is a well-studied and important concept in answer set programming (ASP). $\rm LP^{MLN}$ is a probabilistic extension of answer set programs with the weight scheme adapted from Markov Logic. Because of the semantic differences, strong equivalence for ASP does not simply carry over to $\rm LP^{MLN}$. I study the concept of strong equivalence in $\rm LP^{MLN}$ with the goal of extending strong equivalence to $\rm LP^{MLN}$ programs. My study shows that the verification of strong equivalence in $\rm LP^{MLN}$ can be reduced to equivalence checking in classical logic plus weight consideration.The result allows us to leverage an answer set solver for checking strong equivalence in $\rm LP^{MLN}$. Furthermore, this study also suggests us a few reformulations of the $\rm LP^{MLN}$ semantics using choice rules, logic of here and there, and the second-order logic. I will present my work result of strong equivalence for $\rm LP^{MLN}$ and talk about my next steps for research: one is approximately strong equivalence, and another is the integration of fuzzy logic with neural network.

cs.LO↗

arXiv2019

Demand Prediction for Electric Vehicle Sharing

Electric Vehicle (EV) sharing systems have recently experienced unprecedented growth across the globe. Many car sharing service providers as well as automobile manufacturers are entering this competition by expanding both their EV fleets and renting/returning station networks, aiming to seize a share of the market and bring car sharing to the zero emissions level. During their fast expansion, one fundamental determinant for success is the capability of dynamically predicting the demand of stations. In this paper we propose a novel demand prediction approach, which is able to model the dynamics of the system and predict demand accordingly. We use a local temporal encoding process to handle the available historical data at individual stations, and a spatial encoding process to take correlations between stations into account with graph convolutional neural networks. The encoded features are fed to a prediction network, which forecasts both the long-term expected demand of the stations. We evaluate the proposed approach on real-world data collected from a major EV sharing platform. Experimental results demonstrate that our approach significantly outperforms the state of the art.

cs.AI↗