Stochastic cubic-regularized policy gradient method

Article Computer Science, Artificial Intelligence

A knowledge infused context driven dialogue agent for disease diagnosis using hierarchical reinforcement learning

Abhisek Tiwari et al.

Summary: Disease diagnosis is a crucial step in the treatment process, and automatic disease diagnosis has gained popularity due to its efficiency, accessibility, and reliability. This study proposes a knowledge-infused context-driven hierarchical reinforcement learning diagnosis dialogue system, which utilizes a Bayesian learning-inspired symptom investigation module to aid context-aware and knowledge-grounded symptom investigation. The framework also incorporates a hierarchical disease classifier to alleviate symptom state sparsity issues.

KNOWLEDGE-BASED SYSTEMS (2022)

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Article Computer Science, Information Systems

An enhanced fast non-dominated solution sorting genetic algorithm for multi-objective problems

Wu Deng et al.

Summary: This paper proposes an enhanced fast NSGA-II algorithm (ASDNSGA-II) for solving multi-modal multi-objective optimization problems. By using a special congestion strategy and adaptive crossover strategy, ASDNSGA-II improves the distribution and convergence of solutions. Experimental results show that ASDNSGA-II can effectively find the global Pareto solution set and improve the distribution and convergence of solutions.

INFORMATION SCIENCES (2022)

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Article Computer Science, Artificial Intelligence

Distributed agent-based deep reinforcement learning for large scale traffic signal control

Qiang Wu et al.

Summary: Traffic signal control is an engineering solution that coordinates vehicle movements at road intersections to alleviate congestion. Current systems rely on simplified rule-based methods, but this paper proposes two game theory-aided reinforcement learning algorithms and a distributed computing Internet of Things architecture, achieving better performance.

KNOWLEDGE-BASED SYSTEMS (2022)

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Article Multidisciplinary Sciences

Highly accurate protein structure prediction with AlphaFold

John Jumper et al.

Summary: Proteins are essential for life, and accurate prediction of their structures is a crucial research problem. Current experimental methods are time-consuming, highlighting the need for accurate computational approaches to address the gap in structural coverage. Despite recent progress, existing methods fall short of atomic accuracy in protein structure prediction.

NATURE (2021)

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Article Engineering, Electrical & Electronic