text_combined: kurt driessens gdisrcswaikatoacnz apologies multiple postings call papers workshop rich representations reinforcement learning date august 7th 2005 conjunction icml05 bonn germany web site httpwwwcswaikatoacnzkurtdrrfrl overview reinforcement learning rl developed primary approach learning control strategies autonomous agents majority rl work focused propositional attributevalue representations states actions simple temporal models action memoryless policy representations many problem domains however easily represented assumptions led recent work studies use richer representations rl overcome traditional limitations includes example relational reinforcement learning states actions learned policies relational representations richer temporal representations action options richer policy representations incorporate internal state maxq hierarchies recently introduced predictive state representations state system represented terms predictions future observations main topic workshop application possibly rich representational formats relationships among benefits drawbacks reinforcement learning number previous workshops focus individual representational items noted goal workshop mainly promote interaction researchers various representational aspects rl high diversity rich representations possible approaches many may mutually benefit one another workshop give researchers chance consider benefits highlight key challenges remain given colocation icml ilp year expect attendees conferences participate workshop topic intersects interests particular incorporation relational logical representations rl example topicsissues could addressed include new algorithms exploiting rich representations fullest possible design algorithms rich representations reduction traditional techniques reinforcement learning benefit rich representations specific realworld successes failures particular interest influence rich representations reusability reinforcement learning results transfer learning example goal parameterization introduction rich representations reinforcement learning accompanied different learning goals policyoptimality keep learning problems feasible evaluate new algorithms rich representations specific benchmarks exhibit weaknesses benefits various representational features particular interest rl benefit fromcontribute existing models techniques used decisiontheoretic planning agents already use richer representations lack learning submissions format potential speakers submit paper maximum 6 pages icml paper format encourage smaller contributions summaries ongoing work one page abstracts position papers topics relevant workshop supply panel planned end workshop discussion topics ask potential presenter participant propose advance provocative question claim emphasis provocative use resulting pool questions possibly anonymously stimulate discussion needed papers provocative questions claims sent email kurtdcswaikatoacnz assume questions attributed unless request anonymity important dates april 1 paper submission deadline april 22 notification acceptance may 13 final paper deadline august 7 workshop date organizing committee kurt driessens university waikato hamilton new zealand alan fern oregon state university corvallis usa martijn van otterlo university twente netherlands program committee robert givan purdue university usa roni khardon tufts university usa ron parr duke university usa sridhar mahadevanuniversity massachusetts usa satinder singh university michigan usa prasad tadepalli oregon state university usa kurt driessens department computer science university waikato private bag 3105 phone 64 7 838 4791 hamilton new zealand probability proposing say proposing thing rather high jan ramon recent emaildiscussion wed 06 aug 2008 161222 1300 uai workshop rich representations reinforcement learning