In many real world planning scenarios, agents often do not have enough resources to achieve all of their goals. Consequently, they are forced to find plans that satisfy only a sub...
Menkes van den Briel, Romeo Sanchez Nigenda, Minh ...
Despite the recent resurgence of interest in learning methods for planning, most such efforts are still focused exclusively on classical planning problems. In this work, we invest...
We investigate the problem of learning action effects in partially observable STRIPS planning domains. Our approach is based on a voted kernel perceptron learning model, where act...
We present a method for finding optimal partial solutions to overconstrained instances of the Disjunctive Temporal Problems (DTP). The solutions are optimal in that they satisfy ...
It has recently been shown, for the Constraint Satisfaction Problem (CSP), that the state associated with a node of the search tree built by a backtracking algorithm can be exploit...