We present a novel method for information-theoretic exploration, leveraging recent work on mapping and localization. We describe exploration as the constrained optimization proble...
Most formulations of Reinforcement Learning depend on a single reinforcement reward value to guide the search for the optimal policy solution. If observation of this reward is rar...
The use of several types of structural restrictions within algorithms for learning Bayesian networks is considered. These restrictions may codify expert knowledge in a given domai...
We present a multi-dimensional, multi-step negotiation mechanism for task allocation among cooperative agents based on distributed search. This mechanism uses marginal utility gai...
Xiaoqin Zhang, Victor R. Lesser, Rodion M. Podoroz...
In this research work a large set of the classical numerical functions were taken into account in order to understand both the search capability and the ability to escape from a lo...
Vincenzo Cutello, Giuseppe Nicosia, Mario Pavone, ...