Guiming Luo

EAAI Journal 2017 Journal Article

Inferring causal directions from uncertain data

• Yulai Zhang
• Weifeng Ma
• Guiming Luo

EUMAS Conference 2016 Conference Paper

Boolean Matrix Approach for Abstract Argumentation

• Fuan Pu
• Guiming Luo
• Yucheng Chen 0002

Abstract In this paper, we propose a Boolean matrix approach to encode Dung’s acceptability semantics. Each semantics is encoded into one or more Boolean constraint models, which can be solved by Boolean constraint solvers. In addition, based on our Boolean matrix representations, we also propose a bit-vector-based approach to compute the grounded semantics, and the experimental results show that this approach can achieve a good performance.

AAAI Conference 2016 Conference Paper

Counter-Transitivity in Argument Ranking Semantics

• Fuan Pu
• Jian Luo
• Guiming Luo

The principle of counter-transitivity plays a vital role in argumentation. It states that an argument is strong when its attackers are weak, but weak when its attackers are strong. In this work, we develop a formal theory about the argument ranking semantics based on this principle. Three approaches, i. e. , quantity-based, quality-based and the unity of them, are de- fined to implement the principle. Then, we show an iterative refinement algorithm for capturing the ranking on arguments based on the recursive nature of the principle.

AAAI Conference 2014 Conference Paper

Computing Preferences Based on Agents’ Beliefs

• Jian Luo
• Fuan Pu
• Yulai Zhang
• Guiming Luo

The knowledgebase uncertainty and the argument preferences are considered in this paper. The uncertainty is captured by weighted satisfiability degree, while a preference relation over arguments is derived by the beliefs of an agent.

AAAI Conference 2014 Conference Paper

Fast Algorithm for Non-Stationary Gaussian Process Prediction

• Yulai Zhang
• Guiming Luo

The FNSGP algorithm for Gaussian process model is proposed in this paper. It reduces the time cost to accelerate the task of non-stationary time series prediction without loss of accuracy. Some experiments are verified on the real world power load data.

AAAI Conference 2014 Conference Paper

Inferring Causal Directions in Errors-in-Variables Models

• Yulai Zhang
• Guiming Luo

A method for inferring causal directions based on errors-in-variables models where both the cause variable and the effect variable are observed with measurement errors is concerned in this paper. The inference technique and estimation algorithms are given. Some experiments are included to illustrate our method.