Using multi-agent systems for learning optimal policies for complex problems
Andreas Lommatzsch, Şahin Albayrak · 2007
The automatic computation of an optimal solution for a complex problem is a challenging task if no additional knowledge is available. For bounded sized problems there are universally applicable algorithms (e.g. genetic algorithms, branch and bound, reinforcement learning). The disadvantage of these algorithms is their high computational complexity so that real world problems can only be solved efficiently, if the search space is reduced dramatically.