Mitchell, T.M.


Learning Problem-Solving Heuristics by Experimentation

Classics

In R.S. Michalski,T.M. Mitchell, and J.G. Carbonell (Eds.), Machine Learning. Palo Alto, Calif.: Tioga, pp. 163-190


Learning Problem-Solving Heuristics by Experimentation

Classics

In R.S. Michalski,T.M. Mitchell, and J.G. Carbonell (Eds.), Machine Learning. Palo Alto, Calif.: Tioga, pp. 163-190


Becoming increasingly reactive mobile robots

Classics

"We describe a robot control architecture which combines a stimulus-response subsystem for rapid reaction, with a search-based planner for handling unanticipated situations. The robot agent continually chooses which action it is to perform, using the stimulusresponse subsystem when possible, and falling back on the planning subsystem when necessary. Whenever it is forced to plan, it applies an explanation-based learning mechanism to formulate a new stimulus-response rule to cover this new situation and others similar to it. With experience, the agent becomes increasingly reactive as its learning component acquires new stimulus-response rules that eliminate the need for planning in similar subsequent situations. This Theo-Agent architecture is described, and results are presented demonstrating its ability to reduce routine reaction time for a simple mobile robot from minutes to under a second."In AAAI-90, Vol. 2, pp. 1051– 1058


Models of learning systems

Classics

"The terms adaptation, learning, concept-formation, induction, self-organization, and self-repair have all been used in the context of learning system (LS) research. The research has been conducted within many different scientific communities, however, and these terms have come to have a variety of meanings. It is therefore often difficult to recognize that problems which are described differently may in fact be identical. Learning system models as well are often tuned to the require- ments of a particular discipline and are not suitable for application in related disciplines."In Encyclopedia of Computer Science and Technology, Vol. 11. Dekker


Models of learning systems

Classics

"The terms adaptation, learning, concept-formation, induction, self-organization, and self-repair have all been used in the context of learning system (LS) research. The research has been conducted within many different scientific communities, however, and these terms have come to have a variety of meanings. It is therefore often difficult to recognize that problems which are described differently may in fact be identical. Learning system models as well are often tuned to the require- ments of a particular discipline and are not suitable for application in related disciplines."In Encyclopedia of Computer Science and Technology, Vol. 11. Dekker