Skill Characterization Based on Betweenness

We present a characterization of a useful class of skills based on a graphical representation of an agent's interaction with its environment. Our characterization uses betweenness, a measure of centrality on graphs. It captures and generalizes (at least intuitively) the bottleneck concept, which has inspired many of the existing skill-discovery algorithms. Our characterization may be used directly to form a set of skills suitable for a given task. More importantly, it serves as a useful guide for developing incremental skill-discovery algorithms that do not rely on knowing or representing the interaction graph in its entirety.

• Type: Conference
• Year: 2008
• Where: NIPS
• Authors: Özgür Simsek, Andrew G. Barto
• Keywords: Agent's Interaction, Characterization, Information Technology, NIPS 2008, Skill-discovery Algorithms