An algorithm for self-motivated hierarchical sequence learning

Olivier L. Georgeon, Jonathan H. Morgan, Frank E. Ritter

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

4 Scopus citations

Abstract

This work demonstrates a mechanism that autonomously organizes an agent's sequential behavior. The behavior organization is driven by pre-defined values associated with primitive behavioral patterns. The agent learns increasingly elaborated behaviors through its interactions with its environment. These learned behaviors are gradually organized in a hierarchy that reflects how the agent exploits the hierarchical regularities afforded by the environment. To an observer, the agent thus appears to exhibit basic selfmotivated, sensible, and learning behavior to fulfill its inborn predilections. As such, this work illustrates Piaget's theories of early-stage developmental learning.

Original languageEnglish (US)
Title of host publicationProceedings of the 10th International Conference on Cognitive Modeling, ICCM 2010
Pages73-78
Number of pages6
StatePublished - 2010
Event10th International Conference on Cognitive Modeling, ICCM 2010 - Philadelphia, PA, United States
Duration: Aug 5 2010Aug 8 2010

Other

Other10th International Conference on Cognitive Modeling, ICCM 2010
CountryUnited States
CityPhiladelphia, PA
Period8/5/108/8/10

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Modeling and Simulation

Fingerprint Dive into the research topics of 'An algorithm for self-motivated hierarchical sequence learning'. Together they form a unique fingerprint.

Cite this