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PAGI	
  World:	
  A	
  simula1on	
  
Environment	
  to	
  Challenge	
  
Cogni1ve	
  Architectures	
  
John	
  Licato	
  
S...
Last	
  week	
  
•  John	
  Laird	
  talked	
  
about	
  “interac1ve	
  
task	
  learning”	
  
•  Today,	
  we	
  will	
  ...
Developmental	
  AI	
  –	
  Emerging	
  field	
  
aOemp1ng	
  to	
  show	
  how,	
  using	
  an	
  
agent	
  endowed	
  wit...
Guerin	
  (2011)’s	
  requirements	
  
A	
  sufficiently	
  rich	
  environment…	
  
C1	
  –	
  is	
  rich	
  enough	
  to	
...
Our	
  addi1onal	
  requirements	
  
A	
  sufficiently	
  rich	
  environment…	
  
C4	
  –	
  rich	
  enough	
  to	
  provid...
Tailorability	
  Concern	
  –	
  that	
  [cogni1ve	
  
systems]	
  deal	
  almost	
  exclusively	
  with	
  
manually	
  c...
Licato,	
  J.,	
  Bringsjord,	
  S.,	
  &	
  Govindarajulu,	
  N.S.	
  (2014).	
  How	
  models	
  of	
  crea1vity	
  and	...
Drescher	
  (1991):	
  A	
  star1ng	
  point	
  
•  Cell-­‐based	
  world	
  
•  Simple	
  agent	
  which	
  
occupied	
  ...
Drescher	
  (1991):	
  A	
  star1ng	
  point	
  
•  Used	
  to	
  show	
  Piage1an	
  
(construc1vist)	
  boOom-­‐up	
  
c...
C5	
  –	
  allows	
  for	
  tes1ng	
  of	
  a	
  virtually	
  unlimited	
  number	
  of	
  tasks,	
  whether	
  they	
  te...
Controlled by
PAGI-side
!
!
!
!
!
!
!
!
!
!
Reflex and
State
Machine
Controlled by
AI-side
!
!
!
!
!
TCP/
IP
pyPAGI (option...
PAGI	
  World	
  can	
  be	
  run	
  on:	
  
Windows	
  
Mac	
  OS	
  
Linux	
  (through	
  Chrome	
  browser)	
  
Android...
C1	
  –	
  is	
  rich	
  enough	
  to	
  provide	
  knowledge	
  which	
  would	
  bootstrap	
  the	
  understandings	
  o...
C3	
  –	
  can	
  support	
  the	
  crea1on	
  and	
  maintenance	
  of	
  knowledge	
  which	
  the	
  agent	
  can	
  ve...
C4	
  –	
  rich	
  enough	
  to	
  provide	
  much	
  of	
  the	
  sensory-­‐level	
  informa1on	
  accessible	
  to	
  a	...
PAGI	
  World	
  allows	
  super	
  
rapid	
  demonstra1ons	
  of	
  
cogni1ve	
  abili1es	
  
“The%Brilliant%Boardroom”:%Cogni4ve%
Compu4ng%with%the%DCEC*%and%ADR%
John%Licato%%*%%Selmer%Bringsjord%
Konner&Atkin&*&Ma...
Theorem 3:	

There is a way to satisfy both obligations.
From the Licato presentation in IBM’s Cognitive Systems Institute Lecture Series: 	

“PAGI World:A Simulation Environment ...
PAGI	
  World	
  is	
  a	
  challenge	
  to	
  AI	
  and	
  
cogni1ve	
  architecture	
  researchers	
  
Let’s	
  create	
...
How	
  to	
  access	
  PAGI	
  World	
  (beta	
  version)	
  
	
  
Email	
  John	
  Licato	
  at	
  licatj@rpi.edu	
  
Pagi World from RPI Licato and Bringsjord
Pagi World from RPI Licato and Bringsjord
Pagi World from RPI Licato and Bringsjord
Pagi World from RPI Licato and Bringsjord
Pagi World from RPI Licato and Bringsjord
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Pagi World from RPI Licato and Bringsjord

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Cognitive Systems Institute Speaker Series - presentation from RPI John Licato and Selmer Bringsjord.

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Pagi World from RPI Licato and Bringsjord

  1. 1. PAGI  World:  A  simula1on   Environment  to  Challenge   Cogni1ve  Architectures   John  Licato   Selmer  Bringsjord   Rensselaer  AI  and  Reasoning  (RAIR)  Lab  
  2. 2. Last  week   •  John  Laird  talked   about  “interac1ve   task  learning”   •  Today,  we  will   present  a  simulator   to  facilitate  such   research  
  3. 3. Developmental  AI  –  Emerging  field   aOemp1ng  to  show  how,  using  an   agent  endowed  with  minimal  innate   capaci1es  embedded  in  a  sufficiently   rich  environment,  higher-­‐level   cogni1ve  abili1es  can  emerge.   What  makes  an  environment  sufficiently  rich?   Guerin,  Frank  (2011).  Learning  like  a  baby:  A  survey  of  ar1ficial  intelligence   approaches.  The  Knowledge  Engineering  Review,  26(2),  209-­‐236.  
  4. 4. Guerin  (2011)’s  requirements   A  sufficiently  rich  environment…   C1  –  is  rich  enough  to  provide  knowledge  which   would  bootstrap  the  understandings  of  concepts   rooted  in  physical  rela1onships,  e.g.:  inside  vs.   outside,  large  vs.  small,  above  vs.  below   C2  –  can  allow  for  the  modeling  and  acquisi1on  of   spa1al  knowledge  (widely  regarded  to  be  a   founda1onal  domain  of  knowledge  acquisi1on)   through  interac1on  with  the  world.   C3  –  can  support  the  crea1on  and  maintenance  of   knowledge  which  the  agent  can  verify  itself.  
  5. 5. Our  addi1onal  requirements   A  sufficiently  rich  environment…   C4  –  rich  enough  to  provide  much  of  the  sensory-­‐level   informa1on  accessible  to  a  real-­‐world  agent.   C5  –  allows  for  tes1ng  of  a  virtually  unlimited  number  of   tasks,  whether  they  test  low-­‐level  implicit  knowledge,   high-­‐level  explicit  knowledge,  or  any  of  the  other  areas   required  by  PAGI,  ideally  allowing  for  the  crea1on  of  new   tasks  without  substan1al  programming  effort.   C6  –  allows  a  wide  variety  of  AI  systems  based  on  vastly   different  theore1cal  approaches  to  aOempt  the  same   tasks,  thus  enabling  these  different  approaches  to  be   directly  compared.   CT  –  can  support  tasks  capable  of  verifying  AI  able  to  pass   the  Tailorability  Concern  
  6. 6. Tailorability  Concern  –  that  [cogni1ve   systems]  deal  almost  exclusively  with   manually  constructed  knowledge   representa1ons,  using  toy  examples  and   source  knowledge  ocen  selected  solely  to   display  some  par1cular  ability.   Gentner,  Dedre  &  Forbus,  Ken  (2011).  Computa1onal  models  of  analogy.  Wiley   Interdisciplinary  Reviews:  Cogni>ve  Science,  2(3),  266-­‐276.  
  7. 7. Licato,  J.,  Bringsjord,  S.,  &  Govindarajulu,  N.S.  (2014).  How  models  of  crea1vity  and  analogy  need  to   answer  the  tailorability  concern.  In  Besold,  T.R.,  Kühnberger,  K.-­‐u.,  Schorlemmer,  M.,  &  Smaill,  A.  (Eds.),   Computa>onal  Crea>vity  Research  :  Towards  Crea>ve  Machines.  Atlan1s  Press.  
  8. 8. Drescher  (1991):  A  star1ng  point   •  Cell-­‐based  world   •  Simple  agent  which   occupied  one  cell   •  Agent  had  a  “hand”   which  could  grasp   objects  in  the  world   •  Visual  field  rela1ve  to   the  agent’s  “body”   Drescher,  Gary  L.  (1991).  Made-­‐Up  Minds:  A  Construc>vist  Approach  to   Ar>ficial  Intelligence.  The  MIT  Press.  
  9. 9. Drescher  (1991):  A  star1ng  point   •  Used  to  show  Piage1an   (construc1vist)  boOom-­‐up   crea1on  of  knowledge   •  Simula1on  environment   was  1ghtly  coupled  with  his   schema  mechanism   •  No  realis1c  mo1on  or   physics   •  World  did  not  provide  rich   source  analogs  for  e.g.   inside  vs.  outside   Drescher,  Gary  L.  (1991).  Made-­‐Up  Minds:  A  Construc>vist  Approach  to   Ar>ficial  Intelligence.  The  MIT  Press.  
  10. 10. C5  –  allows  for  tes1ng  of  a  virtually  unlimited  number  of  tasks,  whether  they  test  low-­‐level   implicit  knowledge,  high-­‐level  explicit  knowledge,  or  any  of  the  other  areas  required  by  PAGI,   ideally  allowing  for  the  crea1on  of  new  tasks  without  substan1al  programming  effort.  
  11. 11. Controlled by PAGI-side ! ! ! ! ! ! ! ! ! ! Reflex and State Machine Controlled by AI-side ! ! ! ! ! TCP/ IP pyPAGI (optional) ! ! ! DCEC* extractor/ convertor Physics Engine Task Editor Configurable by external user Can  be  wriOen  in  almost  any  language!  
  12. 12. PAGI  World  can  be  run  on:   Windows   Mac  OS   Linux  (through  Chrome  browser)   Android  and  Iphone  (in  theory)     AI  can  be  wriCen  in:   ANY  programming  language  which   supports  TCP/IP  
  13. 13. C1  –  is  rich  enough  to  provide  knowledge  which  would  bootstrap  the  understandings  of   concepts  rooted  in  physical  rela1onships,  e.g.:  inside  vs.  outside,  large  vs.  strong   C2  –  can  allow  for  the  modeling  and  acquisi1on  of  spa1al  knowledge  (widely  regarded  to  be  a   founda1onal  domain  of  knowledge  acquisi1on)  through  interac1on  with  the  world.  
  14. 14. C3  –  can  support  the  crea1on  and  maintenance  of  knowledge  which  the  agent  can  verify  itself.   Warning:  DCEC*  is  a  highly  expressive  computa1onal  logic  and  therefore  the  cogni1on  which   it  enables  may  or  may  not  be  within  reach  of  a  given  cogni1ve  architecture.     But  PAGI  World  allows  us  to  test  and  see!  
  15. 15. C4  –  rich  enough  to  provide  much  of  the  sensory-­‐level  informa1on  accessible  to  a  real-­‐world   agent.     C6  –  allows  a  wide  variety  of  AI  systems  based  on  vastly  different  theore1cal  approaches  to   aOempt  the  same  tasks,  thus  enabling  these  different  approaches  to  be  directly  compared.   Controlled by PAGI-side ! ! ! ! ! ! ! ! ! ! Reflex and State Machine Controlled by AI-side ! ! ! ! ! TCP/ IP pyPAGI (optional) ! ! ! DCEC* extractor/ convertor Physics Engine Task Editor Configurable by external user
  16. 16. PAGI  World  allows  super   rapid  demonstra1ons  of   cogni1ve  abili1es  
  17. 17. “The%Brilliant%Boardroom”:%Cogni4ve% Compu4ng%with%the%DCEC*%and%ADR% John%Licato%%*%%Selmer%Bringsjord% Konner&Atkin&*&Maggie&Borkowski&*&Jack&Cusick&*&Kainoa&Eastlack&*&Nick&Marton&*&James&Pane;Joyce&*&Spencer&Whitehead& Abstract% This%poster%reports%on%research%and%development%done%by%the%Rensselaer%AI%and% Reasoning%(RAIR)%Lab’s%team,%in%collabora4on%with%IBM,%on%crea4ng%framework% technologies%that%can%be%used%in%many%areas%of%cogni4ve%compu4ng.%We%here%focus%on% one%such%areaMMMthe%Brilliant%Boardroom%(BB),%in%which%a%robot%or%set%of%robots,% augmented%with%mul4modal%inputs%such%as%speech%recogni4on,%synthesis,%and%basic% vision%processing,%react%and%produc4vely%add%to%a%mee4ng%of%corporate%execu4ves.%We% infuse%the%Brilliant%Boardroom%with%two%RAIRMlabMdeveloped%technologies:%the%Deon4c% Cogni4ve%Event%Calculus%(DCEC*),%a%highly%expressive%computa4onal%framework% intended%to%formally%model%and%mechanize%humanMlevel%reasoning,%decisionMmaking,% problemMsolving,%and%natural%language%communica4on;%and%AnalogicoMDeduc4ve% Reasoning%(ADR),%a%type%of%reasoning%which%is%central%to%higher%level%humanMlike% cogni4on.% Syntax S ::= Object | Agent | Self @ Agent | ActionType | Action v Event | Moment | Boolean | Fluent | Numeric f ::= action : Agent ⇥ ActionType ! Action initially : Fluent ! Boolean holds : Fluent ⇥ Moment ! Boolean happens : Event ⇥ Moment ! Boolean clipped : Moment ⇥ Fluent ⇥ Moment ! Boolean initiates : Event ⇥ Fluent ⇥ Moment ! Boolean terminates : Event ⇥ Fluent ⇥ Moment ! Boolean prior : Moment ⇥ Moment ! Boolean interval : Moment ⇥ Boolean ⇤ : Agent ! Self payoff : Agent ⇥ ActionType ⇥ Moment ! Numeric t ::= x : S | c : S | f (t1,...,tn) f ::= t : Boolean | ¬f | f ^ y | f _ y | 8x : S. f | 9x : S. f P(a,t,f) | K(a,t,f) | C(t,f) | S(a,b,t,f) | S(a,t,f) B(a,t,f) | D(a,t,holds( f ,t0)) | I(a,t,happens(action(a⇤,a),t0)) O(a,t,f,happens(action(a⇤,a),t0)) Rules of Inference C(t,P(a,t,f) ! K(a,t,f)) [R1] C(t,K(a,t,f) ! B(a,t,f)) [R2] C(t,f) t  t1 ...t  tn K(a1,t1,...K(an,tn,f)...) [R3] K(a,t,f) f [R4] C(t,K(a,t1,f1 ! f2) ! K(a,t2,f1) ! K(a,t3,f3)) [R5] C(t,B(a,t1,f1 ! f2) ! B(a,t2,f1) ! B(a,t3,f3)) [R6] C(t,C(t1,f1 ! f2) ! C(t2,f1) ! C(t3,f3)) [R7] C(t,8x. f ! f[x 7! t]) [R8] C(t,f1 $ f2 ! ¬f2 ! ¬f1) [R9] C(t,[f1 ^ ... ^ fn ! f] ! [f1 ! ... ! fn ! y]) [R10] B(a,t,f) B(a,t,f ! y) B(a,t,y) [R11a] B(a,t,f) B(a,t,y) B(a,t,y ^ f) [R11b] S(s,h,t,f) B(h,t,B(s,t,f)) [R12] I(a,t,happens(action(a⇤,a),t0)) P(a,t,happens(action(a⇤,a),t)) [R13] B(a,t,f) B(a,t,O(a⇤,t,f,happens(action(a⇤,a),t0))) O(a,t,f,happens(action(a⇤,a),t0)) K(a,t,I(a⇤,t,happens(action(a⇤,a),t0))) [R14] f $ y O(a,t,f,g) $ O(a,t,y,g) [R15] 1 DCEC*:%The%Deon4c%Cogni4ve% Event%Calculus% % The%DCEC*,%pictured%in%Figure%1,%is%a%highly%expressive%framework%that%has%been% used%for%the%mechaniza4on%of%humanMlevel%reasoning,%automated%decisionMmaking,% natural%language%parsing%and%genera4on,%and%many%other%applica4ons.%Because%it% allows%ar4ficial%agents%to%represent%arbitrarily%nested%beliefs%and%knowledge%(e.g.% that%the%execu4ve%in%chair%1%believes%that%the%execu4ve%2%in%chair%believes%that%the% execu4ve%in%chair%1%is%lying),%it%can%perform%reasoning%far%beyond%that%of%many% other%formalisms%proposed%to%represent%commonsense%knowledge.%This%sort%of% ability%is%extremely%important%in%situa4ons%where%an%ar4ficial%agent%is%asked%to%exist% in%a%complex%social%environment,%much%less%one%that%may%require%the%agent%to% provide%jus4fica4ons%for%its%conclusions%(as%the%robo4c%agent%in%our%demonstra4on% was%made%to%do).% % The%DCEC*%also%lends%itself%to%social%environments%because%of%its%inherent%capturing% of%deon4c%no4ons.%It%has%operators%such%as%O%(for%obliga4on),%which%is%treated% carefully%by%a%set%of%inference%rules%(see%Figure%1),%themselves%chosen%to%help% ensure%that%commonsense%no4ons%of%what%it%means%to%be%obliged%to%do%something% can%be%captured%through%straighWorward%applica4ons%of%deduc4ve%reasoning.% These%inference%rules%are%constantly%being%refined%and%explored%through%RAIR%lab% R&D.% % Of%course,%deduc4ve%reasoning%alone%may%be%insufficient%to%capture%the%sort%of% reasoning%expected%by%an%ar4ficial%agent%in%a%boardroom;%therefore%we%augment% our%system%with%ADR,%which%is%another%major%research%focus%of%our%lab.% ADR:%AnalogicoMDeduc4ve%Reasoning% % Although%analogical%and%deduc4ve%reasoning%can%interact%in%a%myriad%of%different% combina4ons,%the%par4cular%intersec4on%between%hypothe4coMdeduc4ve%and% analogical%reasoning,%which%we%call%ADR,%has%been%shown%to%be%par4cularly%useful%to% human%reasoners%from%young%children%performing%Piage4an%experiments%to% groundbreaking%mathema4cal%logicians%like%Gödel.%In%its%simplest%form,%ADR%involves% using%analogical%processes%to%select%poten4ally%relevant%source%analogs,%match%them% to%the%target%domain,%and%produce%hypotheses%about%the%target%domain.%However,% because%these%hypotheses%are%prone%to%error,%deduc4ve%reasoning%is%invoked%to% verify,%support,%or%refute%the%hypotheses%before%they%are%incorporated%into%a% knowledge%base.%% % In%our%demonstra4on,%the%BB%(personified%by%the%Aldebaran%NAO%Bot%pictured%in% Figure%2)%u4lized%ADR%to%answer%a%ques4on%about%how%one%of%the%boardroom% mee4ng’s%par4cipants%might%get%access%to%some%sales%dataMMMthe%correct%answer%was% to%ask%Mr.%Smith,%which%is%knowledge%that%the%robot%did%not%previously%have.%It% inferred%this%by%drawing%an%analogy%to%a%previous%instance%in%which%a%mee4ng% par4cipant%obtained%similar%sales%data%by%asking%Mr.%Johnson,%who%at%the%4me%held% the%office%currently%held%by%Mr.%Smith.%The%deduc4ve%step%did%not%find%any% contradic4ons,%and%so%the%robot%reported%its%findings.% Rensselaer*AI*and*Reasoning*(RAIR)*Lab* Rensselaer*Polytechnic*Ins9tute,*Troy,*NY* Conclusion%/%Future%Work% % The%coming%of%Cogni4ve%Compu4ng%raises%many%interes4ng%ques4ons%about%what%it% means%to%be%cogni4ve%in%the%first%place.%But%we%must%also%ask%what%we%want%our% ar4ficial%cogni4ve%companions%to%do,%even%when%those%things%may%not%be%cogni4vely% plausible.%Here%we%will%see%at%least%two%concerns:%First,%that%cogni4ve%agents%should%be% able%to%reason%ethically;%and%second,%that%these%agents%should%be%able%to%provide% jus4fica4ons%for%their%ac4ons%(in%part%to%ensure%that%the%first%concern%is%met).%Again,% the%DCEC*%and%ADR%offer%results%in%this%direc4on.%Although%it%may%turn%out%that%this% pair%of%technologies%is%not%all%that%is%needed%to%ensure%that%our%cogni4ve%companions% behave%correctly,%they%represent%a%line%of%research%that%takes%the%concerns%we%have% raised%here%seriously%and%cons4tute%a%larger%effort%that%con4nues%to%be%a%focus%of%RAIR% lab%R&D.% Figure%1.%The%Deon4c%Cogni4ve%Event%Calculus%(DCEC*).% Figure%2.%The%robot%used%as%the%personifica4on%of%the%BB.% RPI%Sugges4on%and% Jus4fica4on%Service% User% ADR% Module% Local% KB% DCEC*% Reasoner% DBPedia% Figure%3.%DCEC*%and%ADR%was%recently%used%in%a%demonstra4on% of%another%service,%hosted%in%RPI’s%“red%zone,”%accessed%from% services%hosted%on%IBM’s%“blue%zone.”%%
  18. 18. Theorem 3: There is a way to satisfy both obligations.
  19. 19. From the Licato presentation in IBM’s Cognitive Systems Institute Lecture Series: “PAGI World:A Simulation Environment to Challenge Cognitive Architectures". For more information, visit https://www.linkedin.com/groups/Cognitive-Systems-Institute-6729452
  20. 20. PAGI  World  is  a  challenge  to  AI  and   cogni1ve  architecture  researchers   Let’s  create  tasks,  AI   systems  to  solve  them,   compare  the  approaches,   and  repeat-­‐-­‐-­‐and  keep  this   field  moving  forward!  
  21. 21. How  to  access  PAGI  World  (beta  version)     Email  John  Licato  at  licatj@rpi.edu  

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