Abstract- Decomposing a complex computational problem into sub-problems, which are computationally simpler to solve individually and which can be combined to produce a solution to ...
Vineet R. Khare, Xin Yao, Bernhard Sendhoff, Yaoch...
Improving logic capacity by time-sharing, dynamically reconfigurable FPGAs are employed to handle designs of high complexity and functionality. In this paper, we model each task ...
In this paper we report on using a relational state space in multi-agent reinforcement learning. There is growing evidence in the Reinforcement Learning research community that a r...
Tom Croonenborghs, Karl Tuyls, Jan Ramon, Maurice ...
Transfer learning is the ability of an agent to apply knowledge learned in previous tasks to new problems or domains. We approach this problem by focusing on model formulation, i....
The computation of meaning similarity as operationalized by vector-based models has found widespread use in many tasks ranging from the acquisition of synonyms and paraphrases to ...