Transfer learning is the ability of an agent to apply knowledge learned in previous tasks to new problems or domains. We approach this problem by focusing on model formulation, i....
New approaches to solving constraint satisfaction problems using iterative improvement techniques have been found to be successful on certain, very large problems such as the mill...
Andrew J. Davenport, Edward P. K. Tsang, Chang J. ...
It is well known that many hard tasks considered in machine learning and data mining can be solved in a rather simple and robust way with an instanceand distance-based approach. In...
In this paper we describe ICARUS, an adaptive architecture for intelligent physical agents. We contrast the framework’s assumptions with those of earlier architectures, taking e...
We evaluate a new hybrid language processing approach designed for interactive applications that maintain an interaction with users over multiple turns. Specifically, we describe ...