Autonomous learning of domain models using two-dimensional probability distributions

Witold Slowinski, Frank Guerin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

An autonomous agent placed without any prior knowledge in an environment without goals or a reward function will need to develop a model of that environment using an unguided approach by discovering patters occurring in its observations. We expand on a prior algorithm which allows an agent to achieve that by learning clusters in probability distributions of one-dimensional sensory variables and propose a novel quadtree-based algorithm for two dimensions. We then evaluate it in a dynamic continuous domain involving a ball being thrown onto uneven terrain, simulated using a physics engine. Finally, we put forward criteria which can be used to evaluate a domain model without requiring goals and apply them to our work. We show that adding two-dimensional rules to the algorithm improves the model and that such models can be transferred to similar but previously-unseen environments.
Original languageEnglish
Title of host publicationInternational Conference on Development and Learning and Epigenetic Robotics 2013
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1-6
Number of pages6
DOIs
Publication statusPublished - 20 Aug 2013
EventInternational Conference on Development and Learning and Epigenetic Robotics 2013 - Osaka, Japan., United Kingdom
Duration: 19 Aug 201321 Aug 2013

Conference

ConferenceInternational Conference on Development and Learning and Epigenetic Robotics 2013
CountryUnited Kingdom
CityOsaka, Japan.
Period19/08/1321/08/13

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  • Cite this

    Slowinski, W., & Guerin, F. (2013). Autonomous learning of domain models using two-dimensional probability distributions. In International Conference on Development and Learning and Epigenetic Robotics 2013 (pp. 1-6). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/DevLrn.2013.6652524