Web Intelligence (WI) Web Intelligence (WI) Some Research - PDF document

Web Intelligence (WI) Web Intelligence (WI) Some Research Challenges Some Research Challenges [IJ [IJ CAI CAI’03 Invited Talk] 03 Invited Talk] Jiming Liu Liu Jiming Web Intelligence Consortium (WIC) Web Intelligence Consortium (WIC) & Department of Computer Science Department of Computer Science Hong Kong Baptist University Hong Kong Baptist University j i m j i m i ng i ng@ @ c om c om p. p. hkbu hkbu. edu edu. hk hk I JCAI I JCAI ’ 03 8/ 15/ 2003 03 8/ 15/ 2003 Acknowledgements and Credits Acknowledgements and Credits Profs. Profs. Ning Zhong Ning Zhong, , Yiyu Yao Yiyu Yao, , � Edward A. Feigenbaum Edward A. Feigenbaum, , Setsuo Ohsuga Setsuo Ohsuga, , Benjamin Benjamin Wah Wah, Philip Yu, , Philip Yu, Lotfi Lotfi A. A. Zadeh Zadeh, and , and Xindong Wu, etc. Xindong Wu, etc. WIC Technical Committee WIC Technical Committee � WIC Research Centers in Australia, Canada, India WIC Research Centers in Australia, Canada, India � Japan, and Spain, among others Japan, and Spain, among others Students and Post Students and Post-doc/Visitors at doc/Visitors at � A.M.D. Lab (HKBU) A.M.D. Lab (HKBU) � AAMAS/AOC Group (HKBU) AAMAS/AOC Group (HKBU) � 2003-8-29 2

The preparation has benefited from a peaceful The preparation has benefited from a peaceful � break in break in Grand Canyon, Grand Canyon, on the way to IJCAI on the way to IJCAI’03 03 … 2003-8-29 3 Outline Outline � Background Background � Challenges Challenges � Semantic Web + Planning Semantic Web + Planning � Distributed Agents + Coordination Distributed Agents + Coordination � Social Networks + Self Social Networks + Self-Organization Organization � Issues and Directions Issues and Directions 2003-8-29 4

What is Web Intelligence (WI)? What is Web Intelligence (WI)? WI WI explores the fundamental roles as well explores the fundamental roles as well as practical impacts of as practical impacts of Artificial Intelligence (AI) Artificial Intelligence (AI) � (e.g., knowledge representation, (e.g., knowledge representation, planning, knowledge discovery, agents, planning, knowledge discovery, agents, and social intelligence) and and social intelligence and Advanced Information Technology (IT) Advanced Information Technology (IT) � (e.g., wireless networks, ubiquitous e.g., wireless networks, ubiquitous devices, social networks, and devices, social networks, and data/knowledge grids) data/knowledge grids) on the next generation of Web on the next generation of Web-empowered empowered systems, environments, and activities systems, environments, and activities Zhong, N , Liu, J , and Zhong , N , Liu, J , and Yao Yao, Y Y (eds.) , Y Y (eds.) Web Intelligence Web Intelligence , Springer, 2003 , Springer, 2003 2003-8-29 6

2003-8-29 7 Four Levels of WI Support Four Levels of WI Support Application-level ubiquitous computing Level-4 and social intelligence utilities Knowledge-level information processing Level-3 and management tools support Interface-level multi-media presentation Level-2 standards Internet-level communication, infrastructure, Level-1 and security protocols 2003-8-29 8

WI Challenge #1 WI Challenge #1 Semantic Web + Planning Semantic Web + Planning Semantic Web Semantic Web Ontology: Ontology: Define meanings and relationships of Define meanings and relationships of vocabularies vocabularies � (in terms of classes and properties) (in terms of classes and properties) � Semantic Web: Semantic Web: Add semantic meanings to Web information Add semantic meanings to Web information based on pre based on pre-defined ontology defined ontology Benefits: Enable better human Benefits: Enable better human-computer communications as computer communications as � well as software agents access well as software agents access Example: Example: DARPA Agent Markup Language (DAML) DARPA Agent Markup Language (DAML) � Web Content Representation SHOE OWL HTML SGML RDF OIL DAML+OIL XML DAML Content Definition Ontology/Agent Markup 2003-8-29 10

<?xm l ve r s i on="1. 0"?> <r df : RDF xm l ns: rdf ="ht t p: / / www. w3. or g/ 1999/ 02/ 22 - r df - s ynt ax- ns #" xm l ns: r df s ="ht t p: / / www. w3. or g/ 2000/ 01/ r df - s c he m a#" xm l ns: dam l ="ht t p: / / www. dam l . or g/ 2001/ 03/ dam l +oi l #" xm l : bas e ="ht t p: / / nt s e r ve r . hom e i p. ne t / Honor Pr oj e c t / DAM L/ l ogi c - ont o#"> <dam l : Ont ol ogy dam l : ve r s i onI nf o="1. 0"> <dam l : c om m e nt >An e xam pl e f or l ogi c ont ol ogy</ dam l : c om m e nt > </ dam l : Ont ol ogy> <! - - Te r m - - > <r df s: Cl as s rdf : I D="Te r m"/ > <! - - Var i abl e - - > <r df s: Cl as s rdf : I D="Var i abl e "> <dam l : s ubCl as s Of r df : r e s our c e ="#Te r m "/ > </ r df s : Cl as s > <! - - Cons t ant - - > <r df s: Cl as s rdf : I D="Cons t ant "> <dam l : s ubCl as s Of r df : r e s our c e ="#Te r m "/ > </ r df s : Cl as s > <dam l : Dat at ype Pr ope r t y r df : I D="val ue "> <dam l : dom ai n rdf : r e s our c e ="#Cons t ant "/ > <dam l : r ange r df : r e s our c e ="ht t p: / / www. w3. or g/ 2000/ 10/ XM LSc he m a #s t r i ng"/ > </ dam l : Dat at ype Pr ope r t y> <! - - Pr e di c at e - - > <r df s: Cl as s rdf : I D="Pr e di c at e "> <dam l : s ubCl as s Of r df : r e s our c e ="#Te r m "/ > </ r df s : Cl as s > <dam l : O bj e c t Pr ope r t y r df : I D="ar gum e nt s "> <dam l : dom ai n rdf : r e s our c e ="#Pr e di c at e "/ > <dam l : r ange r df : r e s our c e ="#Te r m "/ > </ dam l : O bj e c t Pr ope r t y > <! - - Com m on Pr ope r t y - - > <dam l : Dat at ype Pr ope r t y r df : I D="nam e "> <dam l : dom ai n rdf : r e s our c e ="#Var i abl e "/ > <dam l : dom ai n rdf : r e s our c e ="#Pr e di c at e "/ > <dam l : r ange r df : r e s our c e ="ht t p: / / www. w3. or g/ 2000/ 10/ XM LSc he m a #s t r i ng"/ > </ dam l : Dat at ype Pr ope r t y> </ rdf : RDF> 2003-8-29 11 Planning Planning � Planning example: Planning example: STRIPS STRIPS � States: States: conjunctions of ground literals conjunctions of ground literals At ( H om e ) ^ Se l l ( Supe r m ar ke t , Banana) ^ Se l l ( Supe r m ar ke t , M i l k) ^ Se l l ( H ar dwar e St or e , Dr i l l ) � Goals: oals: conjunctions of literals conjunctions of literals (possibly (possibly containing variables) containing variables) H ave ( Dr i l l ) ^ H ave ( M i l k) ^ H ave ( Banana) ^ At ( H om e ) At ( ?x) ^ Se l l s ( ?x, M i l k) 2003-8-29 12

Operators in STRIPS Operators in STRIPS At ( ?s t or e ) , Se l l ( ?s t or e , ?x) Buy( ?x) Ac t i on: Buy( ?x) + H ave ( ?x) Pr e c ondi t i ons : At ( ?s t or e ) ^ Se l l ( ?s t or e , ?x) Ef f e c t s : addi t i ons : H ave ( ?x) de l e t i ons : At ( ?he r e ) Ac t i on: G o( ?t he r e ) Pr e c ondi t i ons : At ( ?he r e ) G o( ?t he r e ) Ef f e c t s : addi t i ons : At ( ?t he r e ) + At ( ?t he r e ) de l e t i ons : At ( ?he r e ) - At ( ?he r e ) 2003-8-29 13 POP Algorithm POP Algorithm � Ordering constraint Ordering constraint S i < S j Step Step S i occurs occurs � before step before step S j � Casual link Casual link c S i S j S i achieves the achieves the � precondition c of precondition of S j � Open condition Open condition Precondition that is Precondition that is � not causally linked not causally linked Source: Russell & Norvig 2003-8-29 14