In this paper, we present a novel multi-agent learning paradigm called team-partitioned, opaque-transition reinforcement learning (TPOT-RL). TPOT-RL introduces the concept of usin...
After two decades of research on automated discovery, many principles are shaping up as a foundation of discovery science. In this paper we view discovery science as automation of ...
To avoid the curse of dimensionality, function approximators are used in reinforcement learning to learn value functions for individual states. In order to make better use of comp...
: Although avatars may resemble communicative interface agents, they have for the most part not profited from recent research into autonomous embodied conversational systems. In pa...
: Context is the challenge for the coming years in Artificial Intelligence (AI). In the companion paper [6], we present a view of how context is considered through the literature i...