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Indeed ? $   !DM elements & their combination !""$ "  $ Motivation for multi-participant DM%%$ %  & Towards Bayesian multi-participant DM''$ '   & Towards Bayesian multi-participant DM''$ '    & related talks$    F & other (non)-addressed problems $$$ $  /  P  ` f3|` 3f3` ___>?" dd@(?Pd@ d " @ `"  n?" dd@   @@``PP    @ ` ` pD @D @ 0 % sk( @    f*?d @? "   6*?r x "   Nv2 @?PP  "    f?'d @?*  "   N0gֳgֳ ?  ;Nadpis  6  H@gֳgֳ ?\U  l Click to edit Master text styles * Second level  5 7   T, gֳgֳ ?  B*f  Nvd޽h @? ? f3| Default Design   % NF0 (     6"?  " f  Nv޽h @? ? f3|0 jb`( 0   ZԴD1 ?P   D \*  X  C    D@  ZܸD1 ? @ D RClick to edit Master text styles Second level Third level Fourth level Fifth level!     S  ZLD1 ?   D ^*     `LD1 ?`P  D \*     `pD1 ?`  D ^*  B  s *޽h ? ̙3380___PPT10.56*8 PX( )pM P P  ftn1 ?"P     B*   P  fp 1 ?"     D*   P # lj1 ?"`P    B*   P # l021 ?"`   D*  H P 0޽h ? ̙3380___PPT10.5O9*L     (     s * # "9 Rj      0# p  O   3 r4%1? ,$D0 y/an introduction to the following block of talks00 0    0<) r,$D 0 d2Towards Bayesian Multi-Participant Decision Making33,B   s *޽h ? f3___PPT10+JDO' = @B D ' = @BA?%,( < +O%,( < +DA' =%(D' =%(D' =A@BBBB0B%(D' =1:Bvisible*o3>+B#style.visibility<* %(+8+0+  +7   >6P(  ~  s *   D    HODgֳgֳ ?W[r$0>6___PPT9 P{ f( a | P ) } Arg min D( f || If ) )   &     C &Acamani"},$D 0z @ @x   p,$D 0  HXDgֳgֳ ?U @5jr$0>6___PPT9  actions a @   B  TD1?@ xx,$D 0z  @   @,$D  0B   HD1? >@,$D 0   Hgֳgֳ ? *@r$ 0>6___PPT9 < innovations D @  (2   c wBC dE$GLI`TQ1? x d`Tx d`T`T`Tx d`T`T`TT/,$D 0x   H_Dgֳgֳ ?*r$D 0>6___PPT9  Environment <       HaDgֳgֳ ?  c r$D 0>6___PPT9 KL divergence D( f || If )Z     HhDgֳgֳ ?p a r$D 0>6___PPT9 Acomparing model f( Q ) describing behavior Q = (P, a, ignorance)TB B   H nDgֳgֳ ?p{ r$D0>6___PPT9 Optimal strategy f( a | P )D   r  HtDgֳgֳ ?_ r$D 0>6___PPT9 expanding experience P:  (2  c T+BC dE$GLI`TQ1? x d`Tx d`T`T`Tx d`T`T`T?  ,$D 0  H}Dgֳgֳ ? u B r$D 0>6___PPT9  Participant H    ,  3 r܁D1?,$D0 Vuses   HDgֳgֳ ?  r$D0>6___PPT9 with ideal model If ( Q )J  U  H\Dgֳgֳ ?( J r$D 0>6___PPT9 c & minimizes(       f،D1? ",$D0 )representing multiple aims & constraints`* P   ) H  0޽h ?/   f3|91___PPT10+qV+D' = @B D' = @BA?%,( < +O%,( < +D' =%(%(D' =%(D' =A@BBBB0B%(D' =1:Bvisible*o3>+B#style.visibility<*H)%(D' =%(D' =A@BBBB0B%(D' =1:Bvisible*o3>+B#style.visibility<*H%(D#' =%(D' =A@BBBB0B%(D' =1:Bvisible*o3>+B#style.visibility<*H%(D' =+4 8?\CB#ppt_xBCB#ppt_xB*Y3>B ppt_x<*HD' =+4 8?dCB0-#ppt_h/2BCB#ppt_yB*Y3>B ppt_y<*HD#' =%(D' =A@BBBB0B%(D' =1:Bvisible*o3>+B#style.visibility<* H%(D' =+4 8?\CB#ppt_xBCB#ppt_xB*Y3>B ppt_x<* HD' =+4 8?dCB0-#ppt_h/2BCB#ppt_yB*Y3>B ppt_y<* HD#' =%(D' =A@BBBB0B%(D' =1:Bvisible*o3>+B#style.visibility<*!H%(D' =+4 8?\CB#ppt_xBCB#ppt_xB*Y3>B ppt_x<*!HD' =+4 8?dCB0-#ppt_h/2BCB#ppt_yB*Y3>B ppt_y<*!HD' =%( D' =A@BBBB0B%(D' =1:Bvisible*o3>+B#style.visibility<* H$%(D' =%( D' =A@BBBB0B%(D' =1:Bvisible*o3>+B#style.visibility<*"H"%(D' =%( D' =A@BBBB0B%(D' =1:Bvisible*o3>+B#style.visibility<*'H#%(DA' =%(D' =%(D' =A@BBBB0B%(D' =1:Bvisible*o3>+B#style.visibility<*H%(++0+H7 ++0+H7 ++0+H7 ++0+H7 ++0+ H7 ++0+ H7 ++0+!H7 ++0+"H7 ++0+'H7 +.   ((  ~  s *D  D \  C &Acamani"}a  HXDgֳgֳ ? YC r$D0>6___PPT9 oEnvironment model.    HDgֳgֳ ? r$D0>6___PPT9 ` f( D | a, P) = f( D | a, P, Q) f(Q | P) dQ |1  1    fD1? S,$D0 6,Bayes rule f(Q | D, a, P ) f( D | a, P, Q) f(Q | P) updates f(Q | P) starting from the prior pdf f(Q) describing expert knowledge P  ,4 l  " `  Hgֳgֳ ?z#r$D0>6___PPT9 nOptimal strategy.  &  H$gֳgֳ ?Er$D0>6___PPT9 4bf( a | P ) = If( a | P ) exp{w( a, P)} g ( P )2    2 z    f1? $ ,$D0  uses estimate f(Q | P) of unknown parameter Q of theoretical parameterized model f( D | a, P, Q) y P) y 1   3 r1?  ,$D0 [ predictor   >z :   h,$D00   HD&gֳgֳ ?:r$D 0>6___PPT9 6 g -1( P ) = If( a | P) exp{w( a, P)} da w( a, P) = f( D | a, P) g (D , a, P) d Dy    &&*  H $   3 r-1?K   zIf( D | a, P)$     3 r*1?b n  jf( D | a, P)   B  ZD1?     3 r21? " `ln $    `1?xH  0޽h ? f3|___PPT10+&D' = @B D=' = @BA?%,( < +O%,( < +D' =%(%(D#' =%(D' =A@BBBB0B%(D' =1:Bvisible*o3>+B#style.visibility<* 8%(D' =+4 8?\CB#ppt_xBCB#ppt_xB*Y3>B ppt_x<* 8D' =+4 8?dCB0-#ppt_h/2BCB#ppt_yB*Y3>B ppt_y<* 8D#' =%(D' =A@BBBB0B%(D' =1:Bvisible*o3>+B#style.visibility<*8%(D' =+4 8?\CB#ppt_xBCB#ppt_xB*Y3>B ppt_x<*8D' =+4 8?dCB0-#ppt_h/2BCB#ppt_yB*Y3>B ppt_y<*8D#' =%(D' =A@BBBB0B%(D' =1:Bvisible*o3>+B#style.visibility<*8%(D' =+4 8?\CB#ppt_xBCB#ppt_xB*Y3>B ppt_x<*8D' =+4 8?dCB0-#ppt_h/2BCB#ppt_yB*Y3>B ppt_y<*8D' =%(D' =A@BBBB0B%(D' =1:Bvisible*o3>+B#style.visibility<*86%(DA' =%(D' =%(D' =A@BBBB0B%(D' =1:Bvisible*o3>+B#style.visibility<*8%(DA' =%(D' =%(D' =A@BBBB0B%(D' =1:Bvisible*o3>+B#style.visibility<* 8%(+P+0+8 ++0+ 8 ++0+ 8 ++0+8 ++0+8 ++0+8 +\    W(  ~  s *D  D \  C &Acamani"}5   fDD1?"(,$D 0 k Everything is solved !$ P  H  0޽h ? f3|%___PPT10+t D' = @B Dd' = @BA?%,( < +O%,( < +D' =%(%(D#' =%(D' =A@BBBB0B%(D' =1:Bvisible*o3>+B#style.visibility<*<%(D' =+4 8?\CB#ppt_xBCB#ppt_xB*Y3>B ppt_x<*<D' =+4 8?dCB0-#ppt_h/2BCB#ppt_yB*Y3>B ppt_y<*<+8+0+<7 +   S(  ~  s *@~V    \  C &Acamani"}1   f8D1?"(,$D 0 g NO !$ P  H  0޽h ? f3|___PPT10+ɤD ' = @B D ' = @BA?%,( < +O%,( < +D ' =%(%(D#' =%(D' =A@BBBB0B%(D' =1:Bvisible*o3>+B#style.visibility<*@%(D' =+4 8?\CB#ppt_xBCB#ppt_xB*Y3>B ppt_x<*@D' =+4 8?dCB0-#ppt_h/2BCB#ppt_yB*Y3>B ppt_y<*@D#' =%(D' =A@BBBB0B%(D' =1:Bvisible*o3>+B#style.visibility<*@%(D' =+4 8?\CB#ppt_xBCB#ppt_xB*Y3>B ppt_x<*@D' =+4 8?dCB0-#ppt_h/2BCB#ppt_yB*Y3>B ppt_y<*@D#' =%(D' =A@BBBB0B%(D' =1:Bvisible*o3>+B#style.visibility<* @%(D' =+4 8?\CB#ppt_xBCB#ppt_xB*Y3>B ppt_x<* @D' =+4 8?dCB0-#ppt_h/2BCB#ppt_yB*Y3>B ppt_y<* @++0+@7 ++0+@7 ++0+ @7 +!    7(  ~  s *l8   \  C &Acamani"}  H$gֳgֳ ?m H r$D 0>6___PPT9 Model & design validationH    3 r1? ,$D0 +Actuating of actions & getting innovationsj, P  ,     f\1?r.x,$D0 @hardware development, optimized choice & positioning, embedded & external processing, experimental design & .l Pkk   3 rT1?U,$D0 BlTranslating prior knowledge in f(Q) & aims into If(Q) 7  6    f,1?.,$D0 Cforms & values of pdfs, dependence structures, algorithmic support $D PD2  - D   3 rܸ1? $" ,$D0 nModeling*  M   3 rĺ1? Vp ,$D0 w%domain theories, approximation theory&&% x   3 rȻ1? 2 ,$D0 *Estimation & design of optimal strategies <+ * n    fê1? .2 ,$D 0 Dapproximate functional recursions, curse of dimensionality, numericsEE&<      fɪ1?z>,$D 0 Amethodology, choice of alternatives, estimation & design problemsHB P0A H  0޽h ? f3|___PPT10+ɤD ' = @B D ' = @BA?%,( < +O%,( < +D ' =%(%(D#' =%(D' =A@BBBB0B%(D' =1:Bvisible*o3>+B#style.visibility<*@%(D' =+4 8?\CB#ppt_xBCB#ppt_xB*Y3>B ppt_x<*@D' =+4 8?dCB0-#ppt_h/2BCB#ppt_yB*Y3>B ppt_y<*@D#' =%(D' =A@BBBB0B%(D' =1:Bvisible*o3>+B#style.visibility<*@%(D' =+4 8?\CB#ppt_xBCB#ppt_xB*Y3>B ppt_x<*@D' =+4 8?dCB0-#ppt_h/2BCB#ppt_yB*Y3>B ppt_y<*@D#' =%(D' =A@BBBB0B%(D' =1:Bvisible*o3>+B#style.visibility<* @%(D' =+4 8?\CB#ppt_xBCB#ppt_xB*Y3>B ppt_x<* @D' =+4 8?dCB0-#ppt_h/2BCB#ppt_yB*Y3>B ppt_y<* @++0+@7 ++0+@7 ++0+ @7 +$       (  ~  s *t   \  C &Acamani"}  H,gֳgֳ ?~ 8 r$D 0>6___PPT9 1 no unified Bayesian scalable theory available !.21 2   Hgֳgֳ ?f$r$D0>6___PPT9 7 All elements are being elaborated, tailored & improved486 8 T  H|gֳgֳ ?g Z r$D 0>6___PPT9 b but.    H\gֳgֳ ?!r$D0>6___PPT9 F inherent limited ability of participants to perceive, act & evaluate LG:  G 8:+W0  3 r8 1? e ,$D 0 Z(    3 r|1? # ,$D0 BDM tasks decomposed & solved cooperatively by several participants.C PBB T   Hgֳgֳ ? j r$D 0>6___PPT9 b but.  H  0޽h ? f3|___PPT10+ PNDU' = @B D' = @BA?%,( < +O%,( < +DG' =%(%(D#' =%(D' =A@BBBB0B%(D' =1:Bvisible*o3>+B#style.visibility<*D%(D' =+4 8?\CB#ppt_xBCB#ppt_xB*Y3>B ppt_x<*DD' =+4 8?dCB0-#ppt_h/2BCB#ppt_yB*Y3>B ppt_y<*DD#' =%(D' =A@BBBB0B%(D' =1:Bvisible*o3>+B#style.visibility<* D%(D' =+4 8?\CB#ppt_xBCB#ppt_xB*Y3>B ppt_x<* DD' =+4 8?dCB0-#ppt_h/2BCB#ppt_yB*Y3>B ppt_y<* DD#' =%(D' =A@BBBB0B%(D' =1:Bvisible*o3>+B#style.visibility<* D%(D' =+4 8?\CB#ppt_xBCB#ppt_xB*Y3>B ppt_x<* DD' =+4 8?dCB0-#ppt_h/2BCB#ppt_yB*Y3>B ppt_y<* DD#' =%(D' =A@BBBB0B%(D' =1:Bvisible*o3>+B#style.visibility<* D%(D' =+4 8?\CB#ppt_xBCB#ppt_xB*Y3>B ppt_x<* DD' =+4 8?dCB0-#ppt_h/2BCB#ppt_yB*Y3>B ppt_y<* DD#' =%(D' =A@BBBB0B%(D' =1:Bvisible*o3>+B#style.visibility<* D%(D' =+4 8?\CB#ppt_xBCB#ppt_xB*Y3>B ppt_x<* DD' =+4 8?dCB0-#ppt_h/2BCB#ppt_yB*Y3>B ppt_y<* D++0+D7 ++0+ D7 ++0+ D7 ++0+ D7 ++0+ D7 +,   !(  ~  s *8<   \  C &Acamani"}  H0?gֳgֳ ?;er$D 0>6___PPT9 ?Elaborate individual Bayesian participant as much as possible !.@? @ H  0޽h ? f3|=)5)___PPT10)+q D'' = @B D&' = @BA?%,( < +O%,( < +D%' =%(%(D#' =%(D' =A@BBBB0B%(D' =1:Bvisible*o3>+B#style.visibility<*H%(D' =+4 8?\CB#ppt_xBCB#ppt_xB*Y3>B ppt_x<*HD' =+4 8?dCB0-#ppt_h/2BCB#ppt_yB*Y3>B ppt_y<*HD#' =%(D' =A@BBBB0B%(D' =1:Bvisible*o3>+B#style.visibility<*H%(D' =+4 8?\CB#ppt_xBCB#ppt_xB*Y3>B ppt_x<*HD' =+4 8?dCB0-#ppt_h/2BCB#ppt_yB*Y3>B ppt_y<*HD#' =%(D' =A@BBBB0B%(D' =1:Bvisible*o3>+B#style.visibility<*H%(D' =+4 8?\CB#ppt_xBCB#ppt_xB*Y3>B ppt_x<*HD' =+4 8?dCB0-#ppt_h/2BCB#ppt_yB*Y3>B ppt_y<*HD#' =%(D' =A@BBBB0B%(D' =1:Bvisible*o3>+B#style.visibility<* H%(D' =+4 8?\CB#ppt_xBCB#ppt_xB*Y3>B ppt_x<* HD' =+4 8?dCB0-#ppt_h/2BCB#ppt_yB*Y3>B ppt_y<* HD#' =%(D' =A@BBBB0B%(D' =1:Bvisible*o3>+B#style.visibility<* H%(D' =+4 8?\CB#ppt_xBCB#ppt_xB*Y3>B ppt_x<* HD' =+4 8?dCB0-#ppt_h/2BCB#ppt_yB*Y3>B ppt_y<* HD#' =%( D' =A@BBBB0B%(D' =1:Bvisible*o3>+B#style.visibility<* H%(D' =+4 8?\CB#ppt_xBCB#ppt_xB*Y3>B ppt_x<* HD' =+4 8?dCB0-#ppt_h/2BCB#ppt_yB*Y3>B ppt_y<* HD#' =%( D' =A@BBBB0B%(D' =1:Bvisible*o3>+B#style.visibility<* H%(D' =+4 8?\CB#ppt_xBCB#ppt_xB*Y3>B ppt_x<* HD' =+4 8?dCB0-#ppt_h/2BCB#ppt_yB*Y3>B ppt_y<* HD#' =%( D' =A@BBBB0B%(D' =1:Bvisible*o3>+B#style.visibility<* H%(D' =+4 8?\CB#ppt_xBCB#ppt_xB*Y3>B ppt_x<* HD' =+4 8?dCB0-#ppt_h/2BCB#ppt_yB*Y3>B ppt_y<* HD#' =%(D' =A@BBBB0B%(D' =1:Bvisible*o3>+B#style.visibility<*H%(D' =+4 8?\CB#ppt_xBCB#ppt_xB*Y3>B ppt_x<*HD' =+4 8?dCB0-#ppt_h/2BCB#ppt_yB*Y3>B ppt_y<*H++0+HH ++0+HH ++0+H7 ++0+ HH ++0+ HH ++0+ HH ++0+ HH ++0+ HH ++0+HH +J   ! 0] (  ~  s *d<   \  C &Acamani"}  HQgֳgֳ ?Br$D  0>6___PPT9 = Merging of pdfs describing learning & decision aims needed !>>;&   . i  H4Rgֳgֳ ?Pr$D 0>6___PPT9 w%Support cooperation of participants !"&& & x  HVgֳgֳ ?XH2r$D0>6___PPT9  Environment <    (2  c wBC dE$GLI`TQ1? x d`Tx d`T`T`Tx d`T`T`T*,$D0 @ # ,$D002   c T+BC dE$GLI`TQ1? x d`Tx d`T`T`Tx d`T`T`T ",$D 0   H _gֳgֳ ? 'r$0>6___PPT9  actions aA P    B   TD1? p,$D 0  @   p @,$D  0B   HD1? >@,$D 0  H_gֳgֳ ? *@r$ 0>6___PPT9 > innovations DA R     H4agֳgֳ ? :r$D 0>6___PPT9  Participant A X     @ # o{-$ ,$D002  c T+BC dE$GLI`TQ1? x d`Tx d`T`T`Tx d`T`T`T ",$D 0  Hhgֳgֳ ? 'r$0>6___PPT9  actions aB@  &    B  TD1? p,$D 0  @  p @,$D  0B  HD1? >@,$D 0  Hrgֳgֳ ? *@r$ 0>6___PPT9  innovations DB@    HLugֳgֳ ? :r$D 0>6___PPT9  Participant B V      Hzgֳgֳ ?0 Z r$D0>6___PPT9 EActing A, B cooperate via overlapping behaviors influenced by designs>F: F   C x1? ,$D0 Intentional cooperation needs knowledge sharing & aims harmonizingDDC zz ~X   X~ ,$D0n   f1? fA , IfAp    4   n   f1?~ *  fB , IfBp    4   ~B  NDo?PX ( ~B  NDo?0 H  0޽h ??0  f3|=)5)___PPT10)+q D'' = @B D&' = @BA?%,( < +O%,( < +D%' =%(%(D#' =%(D' =A@BBBB0B%(D' =1:Bvisible*o3>+B#style.visibility<*H%(D' =+4 8?\CB#ppt_xBCB#ppt_xB*Y3>B ppt_x<*HD' =+4 8?dCB0-#ppt_h/2BCB#ppt_yB*Y3>B ppt_y<*HD#' =%(D' =A@BBBB0B%(D' =1:Bvisible*o3>+B#style.visibility<*H%(D' =+4 8?\CB#ppt_xBCB#ppt_xB*Y3>B ppt_x<*HD' =+4 8?dCB0-#ppt_h/2BCB#ppt_yB*Y3>B ppt_y<*HD#' =%(D' =A@BBBB0B%(D' =1:Bvisible*o3>+B#style.visibility<*H%(D' =+4 8?\CB#ppt_xBCB#ppt_xB*Y3>B ppt_x<*HD' =+4 8?dCB0-#ppt_h/2BCB#ppt_yB*Y3>B ppt_y<*HD#' =%(D' =A@BBBB0B%(D' =1:Bvisible*o3>+B#style.visibility<* H%(D' =+4 8?\CB#ppt_xBCB#ppt_xB*Y3>B ppt_x<* HD' =+4 8?dCB0-#ppt_h/2BCB#ppt_yB*Y3>B ppt_y<* HD#' =%(D' =A@BBBB0B%(D' =1:Bvisible*o3>+B#style.visibility<* H%(D' =+4 8?\CB#ppt_xBCB#ppt_xB*Y3>B ppt_x<* HD' =+4 8?dCB0-#ppt_h/2BCB#ppt_yB*Y3>B ppt_y<* HD#' =%( D' =A@BBBB0B%(D' =1:Bvisible*o3>+B#style.visibility<* H%(D' =+4 8?\CB#ppt_xBCB#ppt_xB*Y3>B ppt_x<* HD' =+4 8?dCB0-#ppt_h/2BCB#ppt_yB*Y3>B ppt_y<* HD#' =%( D' =A@BBBB0B%(D' =1:Bvisible*o3>+B#style.visibility<* H%(D' =+4 8?\CB#ppt_xBCB#ppt_xB*Y3>B ppt_x<* HD' =+4 8?dCB0-#ppt_h/2BCB#ppt_yB*Y3>B ppt_y<* HD#' =%( D' =A@BBBB0B%(D' =1:Bvisible*o3>+B#style.visibility<* H%(D' =+4 8?\CB#ppt_xBCB#ppt_xB*Y3>B ppt_x<* HD' =+4 8?dCB0-#ppt_h/2BCB#ppt_yB*Y3>B ppt_y<* HD#' =%(D' =A@BBBB0B%(D' =1:Bvisible*o3>+B#style.visibility<*H%(D' =+4 8?\CB#ppt_xBCB#ppt_xB*Y3>B ppt_x<*HD' =+4 8?dCB0-#ppt_h/2BCB#ppt_yB*Y3>B ppt_y<*H++0+HH ++0+HH ++0+H7 ++0+ HH ++0+ HH ++0+ HH ++0+ HH ++0+ HH ++0+HH +6     P (  ~  s *쳫   \  C &Acamani"}  Hgֳgֳ ? r$D 0>6___PPT9 The considered normative design directly usable in artificial systems like urban traffic control problem characterized by J. XehkDL"$~  D  H0gֳgֳ ?:r$D 0>6___PPT9 RJ. Krack provides current merging methodology for participants with restricted abilities (it suits also to knowledge elicitation & negotiation) PVL  $ d   H\īgֳgֳ ?3 :U r$D 0>6___PPT9 kThe description of the current state of its solution given by J. Homolov reflects the problem complexity >l G2B  !   H˫gֳgֳ ?  r$D 0>6___PPT9  V. `mdl & J. PYikryl outline a vision how to address the traffic problem as multi-participant decision making.Lt L@    ^ #  H ҫgֳgֳ ?{r$D 0>6___PPT9 1g J. Andrsek improves estimation of dynamic mixtures, universal parameterized model of DM participantLh2Z   ' 1 H  0޽h ? f3|=)5)___PPT10)+q D'' = @B D&' = @BA?%,( < +O%,( < +D%' =%(%(D#' =%(D' =A@BBBB0B%(D' =1:Bvisible*o3>+B#style.visibility<*H%(D' =+4 8?\CB#ppt_xBCB#ppt_xB*Y3>B ppt_x<*HD' =+4 8?dCB0-#ppt_h/2BCB#ppt_yB*Y3>B ppt_y<*HD#' =%(D' =A@BBBB0B%(D' =1:Bvisible*o3>+B#style.visibility<*H%(D' =+4 8?\CB#ppt_xBCB#ppt_xB*Y3>B ppt_x<*HD' =+4 8?dCB0-#ppt_h/2BCB#ppt_yB*Y3>B ppt_y<*HD#' =%(D' =A@BBBB0B%(D' =1:Bvisible*o3>+B#style.visibility<*H%(D' =+4 8?\CB#ppt_xBCB#ppt_xB*Y3>B ppt_x<*HD' =+4 8?dCB0-#ppt_h/2BCB#ppt_yB*Y3>B ppt_y<*HD#' =%(D' =A@BBBB0B%(D' =1:Bvisible*o3>+B#style.visibility<* H%(D' =+4 8?\CB#ppt_xBCB#ppt_xB*Y3>B ppt_x<* HD' =+4 8?dCB0-#ppt_h/2BCB#ppt_yB*Y3>B ppt_y<* HD#' =%(D' =A@BBBB0B%(D' =1:Bvisible*o3>+B#style.visibility<* H%(D' =+4 8?\CB#ppt_xBCB#ppt_xB*Y3>B ppt_x<* HD' =+4 8?dCB0-#ppt_h/2BCB#ppt_yB*Y3>B ppt_y<* HD#' =%( D' =A@BBBB0B%(D' =1:Bvisible*o3>+B#style.visibility<* H%(D' =+4 8?\CB#ppt_xBCB#ppt_xB*Y3>B ppt_x<* HD' =+4 8?dCB0-#ppt_h/2BCB#ppt_yB*Y3>B ppt_y<* HD#' =%( D' =A@BBBB0B%(D' =1:Bvisible*o3>+B#style.visibility<* H%(D' =+4 8?\CB#ppt_xBCB#ppt_xB*Y3>B ppt_x<* HD' =+4 8?dCB0-#ppt_h/2BCB#ppt_yB*Y3>B ppt_y<* HD#' =%( D' =A@BBBB0B%(D' =1:Bvisible*o3>+B#style.visibility<* H%(D' =+4 8?\CB#ppt_xBCB#ppt_xB*Y3>B ppt_x<* HD' =+4 8?dCB0-#ppt_h/2BCB#ppt_yB*Y3>B ppt_y<* HD#' =%(D' =A@BBBB0B%(D' =1:Bvisible*o3>+B#style.visibility<*H%(D' =+4 8?\CB#ppt_xBCB#ppt_xB*Y3>B ppt_x<*HD' =+4 8?dCB0-#ppt_h/2BCB#ppt_yB*Y3>B ppt_y<*H++0+HH ++0+HH ++0+H7 ++0+ HH ++0+ HH ++0+ HH ++0+ HH ++0+ HH ++0+HH +     p % (  ~  s *h   \  C &Acamani"}d  H4gֳgֳ ?Pr$D0>6___PPT9 rSP non-closure of mixtures with respect to conditioning, non-linear filtering, models of dependence of discrete variables on continuous ones, dual control, quantification of knowledge and aims MP communication and cooperation strategies, approximations D;  M  Hgֳgֳ ?  r$D 0>6___PPT9 [ ( 3  3 r1?< ,$D 0 ]Conceptual problems    3 r1? ,$D0 pSystematic inclusion of approximations into Bayesian DM Theoretical analysis Optimal design within limited time Bq! q   3 r1?M7,$D 0 h& no danger of unemployment at least for DAR members55 5 #   3 r1? t0 ,$D 0 MUse     3 r21?K k,$D0 `Rolling mills, medicine, e-democracy, economy, & &10 1 H  0޽h ? f3|___PPT10+--D ' = @B D ' = @BA?%,( < +O%,( < +D ' =%(%(D#' =%(D' =A@BBBB0B%(D' =1:Bvisible*o3>+B#style.visibility<*P%(D' =+4 8?\CB#ppt_xBCB#ppt_xB*Y3>B ppt_x<*PD' =+4 8?dCB0-#ppt_h/2BCB#ppt_yB*Y3>B ppt_y<*PD#' =%(D' =A@BBBB0B%(D' =1:Bvisible*o3>+B#style.visibility<*P%(D' =+4 8?\CB#ppt_xBCB#ppt_xB*Y3>B ppt_x<*PD' =+4 8?dCB0-#ppt_h/2BCB#ppt_yB*Y3>B ppt_y<*PD#' =%(D' =A@BBBB0B%(D' =1:Bvisible*o3>+B#style.visibility<*P%(D' =+4 8?\CB#ppt_xBCB#ppt_xB*Y3>B ppt_x<*PD' =+4 8?dCB0-#ppt_h/2BCB#ppt_yB*Y3>B ppt_y<*P++0+PH ++0+PH ++0+PH +0 p,(  ^ S    D c $D @  D "H  0޽h ? ̙3380___PPT10.Q7O0 ,(  ^ S     c $dZ @   "H  0޽h ? ̙3380___PPT10.PP0 ,(  ^ S     c $x @   "H  0޽h ? ̙3380___PPT10.R70 ,(  ^ S    D c $D @  D "H  0޽h ? ̙3380___PPT10.R30 ,(  ^ S     c $ @   "H  0޽h ? ̙3380___PPT10.R30  ,(  ^ S     c $PE @   "H  0޽h ? ̙3380___PPT10.Uȋ;.0 @>(  ^ S     c $ @   4 Underlined  bad formulation, no sense. Not introduced notions Active and passive, learning & design phase. I know all around you already know that, but not everybody in auditorium Comments: I would make the slogans more precise and change the sequence to more natural.H  0޽h ? ̙3380___PPT10.Uȋ;0 `,(  ^ S     c $dݫ @   "H  0޽h ? ̙3380___PPT10.Uȋ;p0 (  ^ S    2 c $2 @  2 vb Practical tests are somehow out of the rest.. H  0޽h ? ̙3380___PPT10.Vlrt@q.",c5:0Bz p 6M4X|B HOh+'0(' px    ,4PowerPoint PresentationoweoweMiroslav Karnye2232lavMicrosoft PowerPoint 7.0i@`)դ@@@i!G%;  N&@ &&#TNPP2OMit & TNPP &&TNPP   @ &@&--/^- $/_- $0a- $1c- $ 2e- $  ((4h- $((005k- $00887n- $88@@9r- $@@HH;v- $HHPP=z- $PPXX?~- $XX``A- $``hhD- $hhppF- $ppxxI- $xxK- $N- $P- $R- $U- $W- $Y- $[- $\- $^- $_- $`- $a- $b- $c- $d- $d- $e- $e- $f- $   $  ((e- $((00e- $0088d- $88@@d- $@@HHc- $HHPPb- $PPXXa- $XX```- $``hh_- $hhpp^- $ppxx\- $xx[- $Y- $W- $U- $R- $P- $N- $K- $I- $F- $D- $A- $?~- $=z- $;v- $9r- $7n- $5k- $4h- $2e- $  1c- $  ((0a- $((00/_- $0088/^- $88@@---&&&&3&--&&- $&&33&&&- & $&33&&&-&& &&-B( UUUU-&&4&&&- $&&33&- --&&w@Z Qww 0wf- --r;x-- @Times New Romanww 0wf- .2 a Miroslav  . .2 `Miroslav  . . 2 aK. . 2 `K. . 2 a. . 2 `. . 2 arn . . 2 `rn . . 2 an. . 2 `n.@Times New Romanww 0wf- .2 school@v . 3.2 school@v . . 2 utia  . 3. 2 utia  . . 2 B.n. 3. 2 A.n. . 2 Icasa . 3. 2 Hcasa . . 2 j.n. 3. 2 i.n. . 2 pcz . 3. 2 ocz .--3- .2  http://as.   . .2  http://as.   . . 2 utia  . . 2 utia  . . 2  .z. . 2  .z. . 2 cz . . 2 cz .--!:-- ---- @Times ww 0wf- .N2  /an introduction to the following block of talks              . .N2 /an introduction to the following block of talks              .--!-- @Times New Romanww 0wf- .(2 +Towards Bayesian Multi"   *  . .(2 *Towards Bayesian Multi"   *  . . 2 -z. . 2 -z. .2  Participant    . .2  Participant    . .2 Decision Making"   * . .2 Decision Making"   * .-- "Systemf !-&TNPP &՜.+,D՜.+,|8     Pedvdn na obrazovceedd   Times New RomanSymbolArialTimes WingdingsDefault Design Addressed task and its elementsSolution of the addressed task!Discussion of the addressed task Indeed ?"DM elements & their combination !% Motivation for multi-participant DM' Towards Bayesian multi-participant DM' Towards Bayesian multi-participant DM related talks$ other (non)-addressed problems Pouit psmaablona nvrhuNadpisy snmk  8@ _PID_HLINKSAlmailto:school@utia.cas.cz&_6*Miroslav KarnyMiroslav Karny  !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstuvwxyz{|}~      !"#$%*Root EntrydO)PicturesCurrent UserSummaryInformation(X'PowerPoint Document( ZDocumentSummaryInformation8Root EntrydO)PV.PicturesCurrent UserDSummaryInformation(X'     *$_6 prednasejiciprednasejici