Single-agent reinforcement learners in time-extended domains and multi-agent systems share a common dilemma known as the credit assignment problem. Multi-agent systems have the st...
Hierarchical state decompositions address the curse-ofdimensionality in Q-learning methods for reinforcement learning (RL) but can suffer from suboptimality. In addressing this, w...
Erik G. Schultink, Ruggiero Cavallo, David C. Park...
A collective of agents often needs to maximize a “world utility” function which rates the performance of an entire system, while subject to communication restrictions among th...
In many approaches for qualitative spatial reasoning, navigation of an agent in a more or less static environment is considered (e.g. in the double-cross calculus [12]). However, i...
Consumers use service selection mechanisms to decide on a service provider to interact with. Although there are various service selection mechanisms, each mechanism has different s...