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General AI Challenge - Round One: Gradual Learning

2017/08/17 by Jan Feyereisl, Feyereisl, Jan, Matěj Nikl +7
Computer Science · #Artificial Intelligence (cs.AI) #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Machine Learning and Algorithms #Reinforcement Learning in Robotics

paper · pdf · doi:10.48550/arxiv.1708.05346

openalex publication_date 2017/08/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

The General AI Challenge is an initiative to encourage the wider artificial intelligence community to focus on important problems in building intelligent machines with more general scope than is currently possible. The challenge comprises of multiple rounds, with the first round focusing on gradual learning, i.e. the ability to re-use already learned knowledge for efficiently learning to solve subsequent problems. In this article, we will present details of the first round of the challenge, its inspiration and aims. We also outline a more formal description of the challenge and present a preliminary analysis of its curriculum, based on ideas from computational mechanics. We believe, that such formalism will allow for a more principled approach towards investigating tasks in the challenge, building new curricula and for potentially improving consequent challenge rounds.

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