Causality

[Engineering20] Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sense

Recent progress in deep learning is essentially based on a "big data for small tasks" paradigm, under which massive amounts of data are used to train a classifier for a single narrow task. In this paper, we call for a shift that flips this paradigm …

[AAAI20] Theory-based Causal Transfer: Integrating Instance-level Induction and Abstract-level Structure Learning

Learning transferable knowledge across similar but different settings is a fundamental component of generalized intelligence. In this paper, we approach the transfer learning challenge from a causal theory perspective. Our agent is endowed with two …

[CogSci19] Decomposing Human Causal Learning: Bottom-up Associative Learning and Top-down Schema Reasoning

Transfer learning is fundamental for intelligence; agents expected to operate in novel and unfamiliar environments must be able to transfer previously learned knowledge to new domains or problems. However, knowledge transfer manifests at different …

[CogSci18] Human Causal Transfer: Challenges for Deep Reinforcement Learning

Discovery and application of causal knowledge in novel problem contexts is a prime example of human intelligence. As new information is obtained from the environment during interactions, people develop and refine causal schemas to establish a …

[VR2018] Spatially Perturbed Collision Sounds Attenuate Perceived Causality in 3D Launching Events

When a moving object collides with an object at rest, people immediately perceive a causal event: i.e., the first object has launched the second object forwards. However, when the second object's motion is delayed, or is accompanied by a collision …