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Found 9 papers, 2 papers with code
Surrogate Assisted Monte Carlo Tree Search in Combinatorial Optimization
Industries frequently adjust their facilities network by opening new branches in promising areas and closing branches in areas where they expect low profits.
Integrating Action Knowledge and LLMs for Task Planning and Situation Handling in Open Worlds
Each situation corresponds to a state instance wherein a robot is potentially unable to complete a task using a solution that normally works.
Robot Task Planning and Situation Handling in Open Worlds
This paper introduces a novel algorithm (COWP) for open-world task planning and situation handling that dynamically augments the robot's action knowledge with task-oriented common sense.
Reasoning with Scene Graphs for Robot Planning under Partial Observability
Robot planning in partially observable domains is difficult, because a robot needs to estimate the current state and plan actions at the same time.
Guiding Robot Exploration in Reinforcement Learning via Automated Planning
Reinforcement learning (RL) enables an agent to learn from trial-and-error experiences toward achieving long-term goals; automated planning aims to compute plans for accomplishing tasks using action knowledge.
Tractable Approximate Gaussian Inference for Bayesian Neural Networks
In this paper, we propose an analytical method for performing tractable approximate Gaussian inference (TAGI) in Bayesian neural networks.
Learning and Reasoning for Robot Sequential Decision Making under Uncertainty
The key contribution of this work is a robot SDM framework, called LCORPP, that supports the simultaneous capabilities of supervised learning for passive state estimation, automated reasoning with declarative human knowledge, and planning under uncertainty toward achieving long-term goals.
Clustering categorical data via ensembling dissimilarity matrices
Our method extends to high dimensional categorical data of equal lengths by ensembling over many choices of explanatory variables.
A General Hybrid Clustering Technique
For a given desired number of clusters $K$, we use three stages to find a clustering.
