Multi-agent teamwork is critical in a large number of agent applications, including training, education, virtual enterprises and collective robotics. Tools that can help humans an...
Here we advocate an approach to learning hardware based on induction of finite state machines from temporal logic constraints. The method involves training on examples, constraint...
Marek A. Perkowski, Alan Mishchenko, Anatoli N. Ch...
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...
Recently TRW fielded a prototype system for a government customer. It provides a wide range of capabilities including data collection, hierarchical storage, automated distribution...
We describe the Paraflow system for connecting heterogeneous computing services together into a flexible and efficient data-mining metacomputer. There are three levels of parallel...