In this work we assume that there is an agent in an unknown environment (domain). This agent has some predefined actions and it can perceive its current state in the environment c...
— We concern ourselves with the process of making optimized production planning decisions in the face of low frequency, high impact uncertainty, which takes the form of a small n...
Themostefficient planning algorithms recently developed are mainly based on Graphplansystem or on satisfiability approach. In this paper wepresent a new approach to plan generatio...
Marco Baioletti, Stefano Marcugini, Alfredo Milani
We present BL-WoLF, a framework for learnability in repeated zero-sum games where the cost of learning is measured by the losses the learning agent accrues (rather than the number...
Partially observable stochastic games (POSGs) provide a rich mathematical framework for planning under uncertainty by a group of agents. However, this modeling advantage comes wit...