AI 2024: Advances in Artificial Intelligence: 37th Australasian Joint Conference on Artificial Intelligence, AI 2024, Melbourne, VIC, Australia, Novem [Paperback]

This two-volume set LNAI 15442-15443 constitutes the refereed proceedings of the 37th Australasian Joint Conference on Artificial Intelligence, AI 2024, held in Melbourne, VIC, Australia, during November 25-29, 2024.
The 59 full papers presented together with 3 short papers were carefully reviewed and selected from 108 submissions.

Part 1: Knowledge Representation and NLP; Trustworthy and Explainable AI; Machine Learning and Data Mining.
Part 2: Reinforcement Learning and Robotics; Learning Algorithms; Computer Vision; AI for Healthcare.

.- Reinforcement Learning and Robotics.
.- ECoDe: A Sample-Efficient Method for Co-Design of Robotic Agents.
.- Causally driven hierarchies for Feudal Multi-Agent Reinforcement Learning.
.- Graceful Task Adaptation with a Bi-Hemispheric RL Agent.
.- Towards Virtual Character Control via Partial Story Sifting.
.- Boosting Reinforcement Learning Algorithms in Continuous Robotic Reaching Tasks using Adaptive Potential Functions.
.- Online Deep Reinforcement Learning of Servo Control for a Small-Scale Bio-Inspired Wing.
.- Posterior Tracking Algorithm for Multi-objective Classification Bandits.
.- Learning Algorithms
.- Approximate Nearest Neighbour Search on Dynamic Datasets: An Investigation.
.- Pathwise Gradient Variance Reduction with Control Variates in Variational Inference.
.- Active Continual Learning: On Balancing Knowledge Retention and Learnability.
.- Bayesian Parametric Proportional Hazards Regression with the Fused Lasso.
.- Revisiting Bagging for Stochastic Algorithms.
.- Sampling of Large Probabilistic Graphical Models Using Arithmetic Circuits.
.- Importance-based Pruning for Genetic Programming based Symbolic Regression.
.- Quantifying Manifolds: Do the Manifolds Learned by Generative Adversarl3Þ