Hill-climbing has been shown to be more effective than exhaustive search in solving satisfiability problems.Also, it has been used either by itself or in combination with other ...
In supervised learning, we commonly assume that training and test data are sampled from the same distribution. However, this assumption can be violated in practice and then standa...
This paper investigates the problem of policy learning in multiagent environments using the stochastic game framework, which we briefly overview. We introduce two properties as de...