Reinforcement Learning (RL) is analyzed here as a tool for control system optimization. State and action spaces are assumed to be continuous. Time is assumed to be discrete, yet th...
We present a framework of cognitive network management by means of an autonomic reconfiguration scheme. We propose a network architecture that enables intelligent services to meet ...
Minsoo Lee, Dan Marconett, Xiaohui Ye, S. J. Ben Y...
Abstract. Reasoning plays a central role in intelligent systems that operate in complex situations that involve time constraints. In this paper, we present the Adaptive Logic Inter...
Nima Asgharbeygi, Negin Nejati, Pat Langley, Sachi...
Research on coalition formation usually assumes the values of potential coalitions to be known with certainty. Furthermore, settings in which agents lack sufficient knowledge of ...
This paper presents CBRetaliate, an agent that combines Case-Based Reasoning (CBR) and Reinforcement Learning (RL) algorithms. Unlike most previous work where RL is used to improve...
Bryan Auslander, Stephen Lee-Urban, Chad Hogg, H&e...