bram bakker uqgoscienceuvanl call papers haamas hierarchical autonomous agents multiagent systems halfday workshop aamas06 conference may 9 afternoon 2006 httpwwwscienceuvanlbramhaamas submission notification submissions must sent bram scienceuvanl later february 1 2006 notification acceptance rejection sent later february 19 2006 workshop description variety fields related autonomous agents multiagent systems hierarchical approaches beginning emerge one premier ways dealing scale complexity interesting realworld problems fields include reinforcement learning evolutionary algorithms multiagent learning mapping planning robotics markov processes networked sensor information systems general strategy methods divide conquer large complex problem decomposed possibly recursively smaller simpler subproblems hierarchical methods generally represent solve tasks multiple spatial andor temporal resolutions higher levels layers sense abstractions details lower levels however precise formal relationships hierarchical methods different fields virtually unknown presumably hierarchical methods one field may profit greatly advances made hierarchical methods another field purpose workshop bring together researchers different fields discuss similarities various hierarchical methods inspire crossfertilization prevent superfluous reinventions hierarchical wheels identify important questions research hierarchical methods call papers invite papers hierarchical methods particular related following limited following issues mathematical frameworks provide solid theoretical underpinnings hierarchical methods hierarchical methods developed single agent systems extended multiagent systems results complexity hierarchical problem solving evolutionary algorithms generalize results fields hierarchy important relationship hierarchical methods flat monolithic methods exploit hierarchical structure terms type solutions terms savings obtained hierarchical methods exist mathematically sound methods learning policies given hierarchical structure example reinforcement learning provide similarly sound methods learning hierarchical structure hierarchical solutions sometimes perform suboptimally losses result hierarchy formalized solutions guaranteed optimal given hierarchical structure important questions future research hierarchical methods address applications hierarchical methods complexrealworld problems workshop organizers dr bram bakker intelligent autonomous systems informatics institute university amsterdam netherlands bram scienceuvanl dr leon kester integrated systems tno defense safety security netherlands leonkester tnonl dr nikos vlassis intelligent autonomous systems informatics institute university amsterdam netherlands vlassis scienceuvanl dr edwin de jong department information computing sciences university utrecht netherlands dejong csuunl wed 06 aug 2008 232208 0100 uai cfp haamas workshop