Design a Collaborative Online Planning Service based on Markov Process in Command and Control Domain

Although existing Planning methods can plan under uncertainty and decentralize situation, most of them malfunction in some complicated conditions of command and control scenarios such as real time decision making, needing more accurate planning, bounded communication between agents, dynamic worlds and partially observable environments. Among suitable models for these situations, extended models of DEC-POMDPs such as MAOP-COMM can be considered for handing  these conditions. It is possible to improve MAOP-COMM model to do planning for agents with double precision. In this paper, the algorithm of MAOP-COMM model has been improved by upgrading value function heuristic and using "two steps look ahead" in the strategy of finding best policy and making correct decision. In the next step, the improved method was implemented in a web service frame work. The web service gets the desired bounded scenario from the architect and performs online planning to generate plans and sequence of actions regarding to environment dynamicity. The obtained results, show preference of the improved algorithm, So that the designed service with improved algorithm can perform online planning for agents in a decentralized multi-agent system in uncertain condition with better performance and high percent of correct decision making.