专家系统(Expert Systems)发表的论文涉及知识工程的各个方面,包括知识获取和表达的各个方法和技术,以及它们在基于这些方法和技术的系统(包括专家系统)构建中的应用。详细的科学评价是任何论文的重要组成部分。除了传统的应用领域,如软件与需求工程、人机交互和人工智能,我们还瞄准了这些技术的新兴市场,如商业、经济、市场研究和医疗卫生保健。向这一新的重点的转变将以一系列特别问题为标志,这些问题包括热点和新出现的主题。 官网地址:http://dblp.uni-trier.de/db/journals/es/

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Learning from demonstrations then outperform the demonstrator is the advanced target of the inverse reinforcement learning (IRL), which is entitled as beyond-demonstrator (BD)-IRL. The BD-IRL provides an entirely new method to build expert systems, which gets rid of the dilemma of reward function design and reduces the computation costs. Currently, most of the BD-IRL algorithms are two-stage, it first infer a reward function then learn the policy via reinforcement learning (RL). Because of the two separate procedures, the two-stage algorithms have high computation complexity and low robustness. To overcome these flaw, we propose a BD-IRL framework entitled hybrid adversarial inverse reinforcement learning (HAIRL), which successfully integrates the reward learning and exploration into one procedure. The simulation results show that the HAIRL is more efficient and robust when compared with other similar state-of-the-art (SOTA) algorithms.

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Learning from demonstrations then outperform the demonstrator is the advanced target of the inverse reinforcement learning (IRL), which is entitled as beyond-demonstrator (BD)-IRL. The BD-IRL provides an entirely new method to build expert systems, which gets rid of the dilemma of reward function design and reduces the computation costs. Currently, most of the BD-IRL algorithms are two-stage, it first infer a reward function then learn the policy via reinforcement learning (RL). Because of the two separate procedures, the two-stage algorithms have high computation complexity and low robustness. To overcome these flaw, we propose a BD-IRL framework entitled hybrid adversarial inverse reinforcement learning (HAIRL), which successfully integrates the reward learning and exploration into one procedure. The simulation results show that the HAIRL is more efficient and robust when compared with other similar state-of-the-art (SOTA) algorithms.

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