Quan Bai

Bing Xue

Sankalp Khanna

Chao Qian

Yi Mei

The five-volume proceedings set LNAI 16451-16455, constitutes the refereed proceedings of the 22nd Pacific Rim International Conference on Artificial Intelligence, PRICAI 2025, held in Wellington, New Zealand, in November 17–21, 2025. The 197 full papers and 89 short papers included in this book were carefully reviewed and selected from 679 submissions. The papers are organized in the following topical sections: Part I:Clustering, Representation Learning, and Learning Systems; Deep learning; Graph learning Part II: Evolutionary Learning, Transfer and Continual Learning; Real-world Applications Part III: Human-Centric AI; Large Language Models Part IV: Interpretability, Fairness, and Trustworthy AI; Multi-Agent; Natural Language Processing; Planning, Scheduling, and Optimisation Part V: Computer Vision; Time Series Analysis

22nd Pacific Rim International Conference on Artificial Intelligence, PRICAI 2025, Wellington, New Zealand, November 17–21, 2025, Proceedings, Part II

PRICAI 2025: Trends in Artificial Intelligence

Jun Yang

Xiangming Xiao

Jianhong Xia

Xinli Ke

Meta-Scenario Computation for Social-Geographical Sustainability

Xiaodong Li

Tanja Mitrovic

This book constitutes the proceedings of the 31st Australasian Joint Conference on Artificial Intelligence, AI 2018, held in Wellington, New Zealand, in December 2018. The 50 full and 26 short papers presented in this volume were carefully reviewed and selected from 125 submissions. The paper were organized in topical sections named: agents, games and robotics; AI applications and innovations; computer vision; constraints and search; evolutionary computation; knowledge representation and reasoning; machine learning and data mining; planning and scheduling; and text mining and NLP.

31st Australasian Joint Conference, Wellington, New Zealand, December 11-14, 2018, Proceedings

AI 2018: Advances in Artificial Intelligence

Mengjie Zhang

Ying Bi

This book offers several new GP approaches to feature learning for image classification. Image classification is an important task in computer vision and machine learning with a wide range of applications. Feature learning is a fundamental step in image classification, but it is difficult due to the high variations of images. Genetic Programming (GP) is an evolutionary computation technique that can automatically evolve computer programs to solve any given problem. This is an important research field of GP and image classification. No book has been published in this field. This book shows how different techniques, e.g., image operators, ensembles, and surrogate, are proposed and employed to improve the accuracy and/or computational efficiency of GP for image classification. The proposed methods are applied to many different image classification tasks, and the effectiveness and interpretability of the learned models will be demonstrated. This book is suitable as a graduate and postgraduate level textbook in artificial intelligence, machine learning, computer vision, and evolutionary computation.

An Automated Approach to Feature Learning

Genetic Programming for Image Classification

Bing Xue

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