Starting from a heuristic learning scheme for N -person games, we derive a new class of continuous-time learning dynamics consisting of a replicator-like drift adjusted by a penalty term that renders the boundary of the game’s strategy space repelling. These penalty-regulated dynamics are equivalent to players keeping an exponentially discounted aggregate of their on-going payoffs and then usin...

This paper reports on the use of machine learning systems for modeling existing engineering decision procedures. In this activity, various models of an existing decision procedure are constructed by using diierent machine learning systems as well as by changing their operational parameters and input. Individual models serve to focus on diierent aspects of the decision procedure and their combin...

Problem-solving procedures have been typically aimed at achieving well-defined goals or satisfying straightforward preferences. However, learners and solvers may often generate rich multiattribute results with procedures guided by sets of controls that define different dimensions of quality. We explore methods that enable people to explore and express preferences about the operation of classifi...

Existing systems are able to learn information agents through demonstration that provide programmatic access to web-based information. However it is still difficult for end users to combine these information agents in procedures that are customized to their particular needs. We combine learning by demonstration with learning by instruction to build a system to learn such procedures with a small...

The research results presented here are extensions of previous work on Iterative Learning Control (ILC) using projection-based update techniques. These updates were mainly developed for Quasi-Newton optimization and also for solving systems of simultaneous equations. Recently, such updates have been used by the authors to estimate the dynamics of a control system which is asked to perform a per...