In many task-planning domains, dynamic assemblies of autonomous agents are replacing hierarchical organisations because they promise more agility. In such assemblies, interdepende...
In our research we study rational agents which learn how to choose the best conditional, partial plan in any situation. The agent uses an incomplete symbolic inference engine, emp...
In this paper we extend dynamic controllability of temporally-flexible plans to temporally-flexible reactive programs. We consider three reactive programming language constructs w...
Robert T. Effinger, Brian C. Williams, Gerard Kell...
This article summarizes research on several interrelated general issues that can arise in the design and development of user modeling systems: the learning and subsequent adaptati...
Thorsten Bohnenberger, Boris Brandherm, Barbara Gr...
Partially Observable Markov Decision Processes (POMDPs) provide a rich framework for sequential decision-making under uncertainty in stochastic domains. However, solving a POMDP i...