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Found 7 papers, 2 papers with code
Semi-Supervised Hierarchical Multi-Label Classifier Based on Local Information
Scarcity of labeled data is a common problem in supervised classification, since hand-labeling can be time consuming, expensive or hard to label; on the other hand, large amounts of unlabeled information can be found.
Knowledge Transfer for Cross-Domain Reinforcement Learning: A Systematic Review
Reinforcement Learning (RL) provides a framework in which agents can be trained, via trial and error, to solve complex decision-making problems.
Similarity-based Knowledge Transfer for Cross-Domain Reinforcement Learning
In this article, we study how to measure the similarity between cross-domain reinforcement learning tasks to select a source of knowledge that will improve the performance of the learning agent.
Knowledge-Based Hierarchical POMDPs for Task Planning
The main goal in task planning is to build a sequence of actions that takes an agent from an initial state to a goal state.
Causal Structure Learning: a Bayesian approach based on random graphs
We test our method over two different scenarios, and the experiments mainly confirm that our technique can learn a causal structure.
Competence-Based Student Modelling with Dynamic Bayesian Networks
We present a general method for using a competences map, created by defining generalization/specialization and inclusion/part-of relationships between competences, in order to build an overlay student model in the form of a dynamic Bayesian network in which conditional probability distributions are defined per relationship type.
Choosing with unknown causal information: Action-outcome probabilities for decision making can be grounded in causal models
Decision-making under uncertainty and causal thinking are fundamental aspects of intelligent reasoning.
