Decentralized partially observable Markov decision processes (Dec-POMDPs) constitute an expressive framework for multiagent planning under uncertainty, but solving them is provabl...
Frans A. Oliehoek, Matthijs T. J. Spaan, Shimon Wh...
Learning, planning, and representing knowledge in large state t multiple levels of temporal abstraction are key, long-standing challenges for building flexible autonomous agents. ...
Learning agents, whether natural or artificial, must update their internal parameters in order to improve their behavior over time. In reinforcement learning, this plasticity is ...
Many years of experimental and computational molecular biology of model organisms such as Escherichia coli and Saccharomyces cerevisiae has elucidated the gene regulatory network i...
This paper describes the application of data farming techniques (Brandstein and Horne 1998) to explore various aspects of coevolutionary dynamics (McKelvey 2002) in organization s...