Search Results for author:
Found 5 papers, 1 papers with code
A Test for Evaluating Performance in Human-Computer Systems
In this case, we find a speed improvement ratio of 1. 27 (a 27% improvement).
Human-Level Reinforcement Learning through Theory-Based Modeling, Exploration, and Planning
Here we propose a new approach to this challenge based on a particularly strong form of model-based RL which we call Theory-Based Reinforcement Learning, because it uses human-like intuitive theories -- rich, abstract, causal models of physical objects, intentional agents, and their interactions -- to explore and model an environment, and plan effectively to achieve task goals.
Learning with AMIGo: Adversarially Motivated Intrinsic Goals
A key challenge for reinforcement learning (RL) consists of learning in environments with sparse extrinsic rewards.
Logical Rule Induction and Theory Learning Using Neural Theorem Proving
Our approach is neuro-symbolic in the sense that the rule pred- icates and core facts are given dense vector representations.
A First Step in Combining Cognitive Event Features and Natural Language Representations to Predict Emotions
Cognitive science has proposed appraisal theory as a view on human emotion with previous research showing how human-rated abstract event features can predict fine-grained emotions and capture the similarity space of neural patterns in mentalizing brain regions.
