Investigation and Formal Modeling of Human Guidance Behavior
- Rapid proliferation of unmanned vehicles in military and civilian applications.
- Higher levels of autonomy requires cognitive-like functions.
Figure: UAV flight hours (source: unmanned systems integrated roadmap: FY2011-36).
Figure: Collision at 2007 DARPA Urban Challenge.
- Poorly understood areas:
- Interaction between agent and environment.
- Integration of components operating based on different principles, e.g., perception, cognition and action.
- Agent physically and informationally interacts with environment.
- Learn from human demonstrations.
- Bayesian perspective of learning:
Figure: Bayesian perspective of learning.
- Proper structure (prior knowledge) improves learning efficiency
- For guidance, inherent symmetries in behavior as priori knowledge
See information about:
- Guidance Behavior
- Symmetries in Guidance Interactions
- Agile Guidance Experiments
- Analysis Results
This material is based upon work supported by the National Science Foundation under Grant No. 1002298.
Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
Last Modified: 2012-07-09 at 10:21:32 -- this is in International Standard Date and Time Notation