Countering Murphy's Law: the Use of Anticipation and Improvisation
via an Episodic Memory in Support of Intelligent Robot Behavior
Directed by Dr. Ronald C. Arkin
Recently in robotics, substantial efforts have been invested on critical applications such as military, nursing, and search-and-rescue. These applications are critical in a sense that the robots may directly deal with human lives in life-or-death situations, and they are therefore required to make highly intelligent decisions as rapidly as possible. The intelligence we are looking for in this type of situations is proactiveness: the ability to anticipate as well as improvise.
Anticipation here means that the robot can assess the current situation, predict the future consequence of the situation, and execute an action to have desired outcome based on the determined assessment and prediction. On the other hand, improvisation is performed when the consequence of the situation is not fully known. In other words, it is the ability to deal with a novel situation based on knowledge or skill being acquired before.
In this dissertation, we introduce a biologically inspired computational model
of proactive intelligent behavior for robots. Integrating multiple levels of
machine learning techniques such as temporal difference learning, instance-based
learning, and partially observable Markov decision process, aggregated episodic
memories are processed in order to accomplish anticipation as well as
improvisation. How this model can be implemented within a software architectural
framework and integrated into a physically realized robotic system is also
explained. The experimental results using a real robot and high fidelity 3D
simulators are then presented in order to help us understand how extended
experience of a robot influences its ability to behave proactively.