Related references
Note: Only part of the references are listed.Interestingness elements for explainable reinforcement learning: Understanding agents' capabilities and limitations
Pedro Sequeira et al.
ARTIFICIAL INTELLIGENCE (2020)
Mutual Explanations for Cooperative Decision Making in Medicine
Ute Schmid et al.
KUNSTLICHE INTELLIGENZ (2020)
Learning efficient logic programs
Andrew Cropper et al.
MACHINE LEARNING (2019)
Explanation in artificial intelligence: Insights from the social sciences
Tim Miller
ARTIFICIAL INTELLIGENCE (2019)
Machine Discovery of Comprehensible Strategies for Simple Games Using Meta-interpretive Learning
Stephen H. Muggleton et al.
NEW GENERATION COMPUTING (2019)
Summarizing agent strategies
Ofra Amir et al.
AUTONOMOUS AGENTS AND MULTI-AGENT SYSTEMS (2019)
The teaching size: computable teachers and learners for universal languages
Jan Arne Telle et al.
MACHINE LEARNING (2019)
TED: Teaching AI to Explain its Decisions
Michael Hind et al.
AIES '19: PROCEEDINGS OF THE 2019 AAAI/ACM CONFERENCE ON AI, ETHICS, AND SOCIETY (2019)
Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP
Stephen H. Muggleton et al.
MACHINE LEARNING (2018)
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
Amina Adadi et al.
IEEE ACCESS (2018)
Mastering the game of Go with deep neural networks and tree search
David Silver et al.
NATURE (2016)
Faster Teaching via POMDP Planning
Anna N. Rafferty et al.
COGNITIVE SCIENCE (2016)
Human-level control through deep reinforcement learning
Volodymyr Mnih et al.
NATURE (2015)
Meta-interpretive learning: application to grammatical inference
Stephen H. Muggleton et al.
MACHINE LEARNING (2014)
Inductive rule learning on the knowledge level
Ute Schmid et al.
COGNITIVE SYSTEMS RESEARCH (2011)
The faculty of language: What is it, who has it, and how did it evolve?
MD Hauser et al.
SCIENCE (2002)
Relational reinforcement learning
S Dzeroski et al.
MACHINE LEARNING (2001)
Share Paper
