Recent advancements in model-based reinforcement learning have shown that the dynamics of many structured domains (e.g. DBNs) can be learned with tractable sample complexity, desp...
Thomas J. Walsh, Sergiu Goschin, Michael L. Littma...
Interactive multi-agent system improves reusability of agents by separating application design from agent design. However, it remains difficult for application designers (usually n...
Dynamically providing students with clear explanations of complex spatial concepts is critical for a broad range of knowledge-based educational and training systems. This calls fo...
Stuart G. Towns, Charles B. Callaway, James C. Les...
In this paper we incorporate autonomous agents' capability to perform parallel interactions into the cooperative search model, resulting in a new method which outperforms the...
Computer programming of complex systems is a time consuming effort. Results are often brittle and inflexible. Evolving, self-learning flexible multi-agent systems remain a distant ...