Towards a Solution to Bongard Problems: A Causal Approach

Autor:	Youssef, Salahedine, Zečević, Matej, Dhami, Devendra Singh, Kersting, Kristian
Rok vydání:	2022
Předmět:	Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Even though AI has advanced rapidly in recent years displaying success in solving highly complex problems, the class of Bongard Problems (BPs) yet remain largely unsolved by modern ML techniques. In this paper, we propose a new approach in an attempt to not only solve BPs but also extract meaning out of learned representations. This includes the reformulation of the classical BP into a reinforcement learning (RL) setting which will allow the model to gain access to counterfactuals to guide its decisions but also explain its decisions. Since learning meaningful representations in BPs is an essential sub-problem, we further make use of contrastive learning for the extraction of low level features from pixel data. Several experiments have been conducted for analyzing the general BP-RL setup, feature extraction methods and using the best combination for the feature space analysis and its interpretation. Comment: Main paper: 12 pages, References: 2 pages, Supplement: 3 pages. Main paper: 9 figures, Supplement: 3 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2206.07196 Zobrazit plný text záznamu View this record from Arxiv