A major challenge for traditional approaches to multiagent learning is to train teams that easily scale to include additional agents. The problem is that such approaches typically...
David B. D'Ambrosio, Joel Lehman, Sebastian Risi, ...
This paper describes an algorithm, called CQ-learning, which learns to adapt the state representation for multi-agent systems in order to coordinate with other agents. We propose ...
Studies of interactions between protein domains and ligands are important in many aspects such as cellular signaling and regulation. In this work, we applied a three-stage knowledg...
Haiyun Lu, Shamima Banu Bte Sm Rashid, Hao Li, Wee...
Many natural problems in computer science concern structures like graphs where elements are not inherently ordered. In contrast, Turing machines and other common models of computa...
Abstract: In this paper we present our ideas to apply constraint satisfaction on business processes. We propose a multi-level constraint satisfaction approach to handle t levels of...