This article presents MOCO, a learnable meta optimizer for combinatorial optimization problems, which are often NP-hard. Traditional methods use handcrafted heuristics, but advances in neural networks have motivated the development…
Continue readingThe article discusses the role of cognitive biases in decision-making and the need for automated detection of these biases. The research explores the use of conversational agents as a tool…
Continue readingThis study introduces a method for multi-robot motion planning that combines centralized, sampling-based tree search with decentralized, data-driven steer and distance heuristics. The method has been tested on a range…
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