This chapter presents a generic internal reward system that drives an agent to increase the complexity of its behavior. This reward system does not reinforce a predefined task. It...
Abstract. We study through simulation the performance of two MANET routing algorithms in a realistic urban environment. The two algorithms, AODV and AntHocNet, are representative o...
Gianni Di Caro, Frederick Ducatelle, Luca Maria Ga...
Ambient Intelligent (AmI) environments are supposed to act proactively anticipating the user's needs and preferences, therefore the capability of an AmI system to learn those ...
Asier Aztiria, Juan Carlos Augusto, Alberto Izagui...
Reasoning about agents that we observe in the world is challenging. Our available information is often limited to observations of the agent’s external behavior in the past and p...
H. Van Dyke Parunak, Sven Brueckner, Robert S. Mat...
Animated pedagogical agents oer great promise for knowledge-based learning environments. In addition to coupling feedback capabilities with a strong visual presence, these agents...