Efficient Source Discovery and Service Composition for Ubiquitous Computing Environments

To be truly pervasive the devices in a ubiquitous computing environment have to be able to form a “coalition” without human intervention. The Semantic Web provides the infrastructure for discovery and composition of device functionalities. AI planning has been a popular technology for automatic service discovery and composition in the Semantic Web. However, because the Web is so vast and changes so rapidly, a planning agent cannot make a closed-world assumption. This condition makes it difficult for an agent to know when it has gathered all relevant information or when additional searches may be redundant. To avoid redundancy we incorporate Local Closed World reasoning with HTN planning to compose Semantic Web services. In addition, when performing information gathering tasks on the Semantic Web, we use Local Closed World reasoning and a concept of “source relevance” to control the search process. We also describe a prototype agent that we have developed.

[1]  James A. Hendler,et al.  UMCP: A Sound and Complete Procedure for Hierarchical Task-network Planning , 1994, AIPS.

[2]  Fahiem Bacchus,et al.  AIPS 2000 Planning Competition: The Fifth International Conference on Artificial Intelligence Planning and Scheduling Systems , 2001 .

[3]  James A. Hendler,et al.  Automating DAML-S Web Services Composition Using SHOP2 , 2003, SEMWEB.

[4]  Tran Cao Son,et al.  Semantic Web Services , 2001, IEEE Intell. Syst..

[5]  Hector Muñoz-Avila,et al.  SHOP: Simple Hierarchical Ordered Planner , 1999, IJCAI.

[6]  Jeff Heflin,et al.  LCW-Based Agent Planning for the Semantic Web , 2002 .

[7]  Ora Lassila,et al.  Semantic Gadgets: Ubiquitous Computing Meets the Semantic Web , 2003, Spinning the Semantic Web.

[8]  Mark Weiser,et al.  Some Computer Science Problems in Ubiquitous Computing , 1993 .

[9]  Alon Y. Halevy,et al.  Obtaining Complete Answers from Incomplete Databases , 1996, VLDB.

[10]  Dana S. Nau,et al.  Computer Bridge - A Big Win for AI Planning , 1998, AI Mag..

[11]  Bijan Parsia,et al.  Composition-driven Filtering and Selection of Semantic Web Services , 2004 .

[12]  Ismailcem Budak Arpinar,et al.  Automatic Composition of Semantic Web Services , 2003, ICWS.

[13]  Sasikumar Mukundan,et al.  Spinning the Semantic Web , 2004 .

[14]  Craig A. Knoblock,et al.  Information Gathering Plans With Sensing Actions , 1997, ECP.

[15]  Joachim Peer,et al.  Bringing Together Semantic Web and Web Services , 2002, SEMWEB.

[16]  Fahiem Bacchus,et al.  The AIPS '00 Planning Competition , 2001, AI Mag..

[17]  Jeff Heflin,et al.  Reading Between the Lines: Using SHOE to Discover Implicit Knowledge from the Web , 1998 .

[18]  Yue Cao,et al.  Total-Order Planning with Partially Ordered Subtasks , 2001, IJCAI.

[19]  James A. Hendler,et al.  Is there an intelligent agent in your future? Nature , 1999 .

[20]  Austin Tate,et al.  O-Plan: The open Planning Architecture , 1991, Artif. Intell..

[21]  Onn Shehory,et al.  A Planning Component for RETSINA Agents , 1999, ATAL.

[22]  Oliver M. Duschka Query Optimization Using Local Completeness , 1997, AAAI/IAAI.

[23]  Subbarao Kambhampati,et al.  Efficiently Executing Information Gathering Plans , 1998 .

[24]  David E. Wilkins,et al.  Can AI planners solve practical problems? , 1990, Comput. Intell..