The application of cognitive mechanisms to support knowledge acquisition is, from our point of view, crucial for making the resulting models coherent, efficient, credible, easy to use and understandable. In particular, there are two characteristic features of intelligence that are essential for knowledge development: forgetting and consolidation. Both plays an important role in knowledge bases and learning systems to avoid possible information overflow and redundancy, and in order to preserve and strengthen important or frequently used rules and remove (or forget) useless ones. We present an incremental, long-life view of knowledge acquisition which tries to improve task after task by determining what to keep, what to consolidate and what to forget, overcoming The Stability-Plasticity dilemma. In order to do that, we rate rules by introducing several metrics through the first adaptation, to our knowledge, of the Minimum Message Length (MML) principle to a coverage graph, a hierarchical assessment structure which treats evidence and rules in a unified way. The metrics are not only used to forget some of the worst rules, but also to set a consolidation process to promote those selected rules to the knowledge base, which is also mirrored by a demotion system. We evaluate the framework with a series of tasks in a chess rule learning domain.
CEL: A Continual Learning Model for Disease Outbreak Prediction by Leveraging Domain Adaptation via Elastic Weight Consolidation
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；University of Macau（澳门大学）
Why the Brain Consolidates: Predictive Forgetting for Optimal Generalisation
Huawei Noah’s Ark Lab（华为诺亚实验室）；AI Centre, Department of Computer Science, University College London（大学计算机科学系人工智能中心）；UCL Institute of Cognitive Neuroscience, University College London（大学认知神经科学研究所）；UCL Queen Square Institute of Neurology, University College London（大学Queen Square神经学研究所）
Task-Core Memory Management and Consolidation for Long-term Continual Learning
School of Computer Science and Technology, East China Normal University（东华师范大学计算机科学与技术学院）；School of Electrical and Electronic Engineering, Nanyang Technological University（南洋理工大学电子与电气工程学院）；Computation and Artificial Intelligence Innovative College, Fudan University（复旦大学计算与人工智能创新学院）
Distribution-aware Forgetting Compensation for Exemplar-Free Lifelong Person Re-identification
State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences（机器人国家重点实验室，沈阳自动化研究所，中国科学院）；University of Chinese Academy of Sciences（中国科学院大学）；Key Laboratory of Manufacturing Industrial Integrated Automation, Shenyang University（制造工业集成自动化重点实验室，沈阳大学）；Automation and AI College, Nanjing University of Posts and Telecommunications（自动化与人工智能学院，南京邮电大学）；Department of Statistics and Actuarial Science and the Institute of Data Science, The University of Hong Kong（统计与精算系和数据科学研究所，香港大学）
DESIRE: Dynamic Knowledge Consolidation for Rehearsal-Free Continual Learning
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Centre for Artificial Intelligence and Robotics, HKISI-CAS（香港中文大学（深圳）人工智能与机器人中心（中国科学院深圳先进技术研究院））；Department of Computer Science, University of Illinois at Chicago（芝加哥大学计算机科学系）
Semi-parametric Memory Consolidation: Towards Brain-like Deep Continual Learning
Centre for Artificial Intelligence and Robotics（人工智能与机器人中心）；Hong Kong Institute of Science and Innovation（香港科学与创新研究院）；Chinese Academy of Sciences（中国科学院）；Department of Computer Science（计算机科学系）；City University of Hong Kong（香港城市大学）；The Department of Neurosurgery（神经外科部门）；Peking University First Hospital（北京大学第一医院）；State Key Laboratory of Multimodal Artificial Intelligence Systems（多模态人工智能系统国家重点实验室）；Institute of Automation（自动化研究所）；Hong Kong SAR, China（香港特别行政区，中国）
