In this paper we describe a class of a problem of goal satisfaction in mutual exclusion network that can be solved in polynomial time. This problem provides a common basis for rea...
Reinforcement Learning (RL) is a simulation-based technique useful in solving Markov decision processes if their transition probabilities are not easily obtainable or if the probl...
We consider discrete stochastic optimization problems where the objective function can only be estimated by a simulation oracle; the oracle is defined only at the discrete points....
We present an extension of the Dynamics Based Control (DBC) paradigm to environment models based on Predictive State Representations (PSRs). We show an approximate greedy version ...
Ariel Adam, Zinovi Rabinovich, Jeffrey S. Rosensch...
Distributed constraint optimization problems (DCOPs) are a popular way of formulating and solving agent-coordination problems. It is often desirable to solve DCOPs optimally with m...