17 citations · 35 across the 13 of their papers we have counts for
5 papers · 1 filter
Conditions for Length Generalization in Learning Reasoning Skills
Changnan Xiao, Bing Liu
Reasoning is a fundamental capability of AI agents. Recently, large language models (LLMs) have shown remarkable abilities to perform reasoning tasks. However, numerous evaluations…
Learnability and Algorithm for Continual Learning
Gyuhak Kim, Changnan Xiao, Tatsuya Konishi +1
This paper studies the challenging continual learning (CL) setting of Class Incremental Learning (CIL). CIL learns a sequence of tasks consisting of disjoint sets of concepts or cl…
Open-World Continual Learning: Unifying Novelty Detection and Continual Learning
Gyuhak Kim, Changnan Xiao, Tatsuya Konishi +2
As AI agents are increasingly used in the real open world with unknowns or novelties, they need the ability to (1) recognize objects that (a) they have learned before and (b) detec…
Mastering Strategy Card Game (Legends of Code and Magic) via End-to-End Policy and Optimistic Smooth Fictitious Play
Wei Xi, Yongxin Zhang, Changnan Xiao +5
Deep Reinforcement Learning combined with Fictitious Play shows impressive results on many benchmark games, most of which are, however, single-stage. In contrast, real-world decisi…
Mastering Strategy Card Game (Hearthstone) with Improved Techniques
Changnan Xiao, Yongxin Zhang, Xuefeng Huang +3
Strategy card game is a well-known genre that is demanding on the intelligent game-play and can be an ideal test-bench for AI. Previous work combines an end-to-end policy function…