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Solving partially observable Markov decision processes (POMDPs) is a complex task that is often intractable. This paper examines the problem of finding an optimal policy for POMDPs. While a lot of effort has been made to develop algorithms to solve POMDPs, the question of automatically finding good low-dimensional spaces in multi-agent co-operative learning domains has not been explored thoroughly. To identify this question, an online algorithm CMEAS is presented to improve the POMDP model. This algorithm is based on a look-ahead search to find the best action to execute at each cycle. Thus the overwhelming complexity of computing a policy for each possible situation is avoided. A series of simulations demonstrate this good strategy and performance of the proposed algorithm when multiple agents co-operate to find an optimal policy for POMDPs. [ABSTRACT FROM AUTHOR]