2008
NeurIPS
NeurIPS 2008
Skill Characterization Based on Betweenness
Abstract
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 may be used directly to form a set of skills suitable for a given environment. More importantly, it serves as a useful guide for developing online, incremental skill discovery algorithms that do not rely on knowing or representing the environment graph in its entirety.
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Interdisciplinary Bridge
- Artificial Intelligence and Mathematics & Optimization
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Trend Setter
- Reasoning
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Keyword Pioneer
- skill discovery
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Hot Topic Early Bird
- multi-agent system
Authors
Topics
Artificial Intelligence > Core AI > Agent Systems
Machine Learning > Core Methods > Representation Learning
Mathematics & Optimization > Mathematics > Graph Theory
Machine Learning > Learning Types > Reinforcement Learning
Artificial Intelligence > Core AI > Reasoning
Artificial Intelligence > Core AI > Reinforcement Learning