Our system, based on a multiagent framework called collaborative understanding of distributed knowledge (CUDK), is designed with the overall goal of balancing agents' conceptu...
PAC-MDP algorithms approach the exploration-exploitation problem of reinforcement learning agents in an effective way which guarantees that with high probability, the algorithm pe...
In reinforcement learning problems, an agent has the task of learning a good or optimal strategy from interaction with his environment. At the start of the learning task, the agent...
Tom Croonenborghs, Kurt Driessens, Maurice Bruynoo...
The ideas of dependency directed backtracking (DDB) and explanation based learning (EBL) have developed independently in constraint satisfaction, planning and problem solving comm...
Many real-world applications of multiagent systems require independently designed (heterogeneous) and operated (autonomous) agents to interoperate. We consider agents who offer bu...
Matteo Baldoni, Cristina Baroglio, Amit K. Chopra,...