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Huy Pham & Deborah Stacey

Practical Goal-based Reasoning in Ontology-Driven Applications. School of Computer Science University of Guelph Guelph, Ontario, Canada. Huy Pham & Deborah Stacey. Quick Overview. A more practical way to do planning in ontology-driven applications

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Huy Pham & Deborah Stacey

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  1. Practical Goal-based Reasoning in Ontology-Driven Applications School of Computer Science University of Guelph Guelph, Ontario, Canada Huy Pham & Deborah Stacey

  2. Quick Overview A more practical way to do planning in ontology-driven applications Some interesting challenges, and some (hopefully) interesting ideas Result: A reusable integration framework for bringing planning into onto-driven apps Slide 2 of 15 Knowledge Engineering and Ontology Development 2011

  3. Motivation Today's intelligent systems are knowledge-intensive And would benefit from an onto-driven approach Standardized semantics  Reusable KB built for one app is understood by many others Expressive  Rich models Reasoning services  Modular models Problem: Inadequate reasoning support Slide 3 of 15 Knowledge Engineering and Ontology Development 2011

  4. Onto Reasoning vs Goal-based Reasoning Reasoning about structure vs reasoning about actions “Is this class a subclass of that class?” vs “Is there a way to get to the goal state?” Static vs Dynamic Tableaux (DL, Open-world) vs Resolution (LP, Closed-world) Slide 4 of 15 Knowledge Engineering and Ontology Development 2011

  5. Existing Approaches Language-based Approaches Idea: Modify/Extend/Restrict DL to provide rule-based support SWRL, DLP, etc. Very challenging Theoretical: Decidability, Boundary, etc. Practical: Tooling support, User acceptance, etc. Awaiting more case studies Slide 5 of 15 Knowledge Engineering and Ontology Development 2011

  6. Existing Approaches Parallel Modeling Approaches Idea Model application knowledge in ontologies Model planning-related knowledge in a planning language Have planning programs query the ontologies at runtime Challenges KBs in two languages System developers have to be well-versed in both Integration is more likely to be app-specific Slide 6 of 15 Knowledge Engineering and Ontology Development 2011

  7. How about? A translation approach Model planning-related knowledge in ontology (alongside with other app knowledge) Have it translated it into executable rule-based programs (under the hood) Slide 7 of 15 Knowledge Engineering and Ontology Development 2011

  8. Crazy Ideas? Perhaps! But planning KBs are now ontology-based Universally understood/reusable by other apps Smaller risk of being “stuck” in a non-mainstream language Make use of existing and mature tool and frameworks Total independence from the underlying planning framework Also, user does not need to learn/worry about the underlying planning formalism Partially investigated by Rajpathak et al and Gil et al Slide 8 of 15 Knowledge Engineering and Ontology Development 2011

  9. Two interesting challenges Representability Can we describe planning problems in ontology? HL is not a proper subset of HL Closed world vs open world Translatability How can we ensure the user does not produce non-translatable problem descriptions? DL is also a non-proper subset of DL Slide 9 of 15 Knowledge Engineering and Ontology Development 2011

  10. Observation 1 DL can describe rules (given a proper set of ontological constructs) Triangle(x,y,z) ← Point(x) Ʌ Point(y) Ʌ Point(z) Ʌ x ≠ y Ʌ y ≠ z Ʌ z ≠ x can be modeled as: Slide 10 of 15 Knowledge Engineering and Ontology Development 2011

  11. Observation 2 • As such, we do have some control on what the user can produce • By carefully control the language constructs in the planning ontology • In a transparent and non-intrusive ways! An ontology can be viewed as a language Concepts constitute a vocabulary Roles dictates how the terms can be combined to form statements Slide 11 of 15

  12. Proposed Architecture Knowledge Engineering and Ontology Development 2011 Slide 12 of 15

  13. Illustrative Example (Simple) TripPlanning Arrive at UPEC campus from Guelph campus, awake, and properly rested! By taking a combination of actions: flights, bus, train, rest, buy or drink coffee Preconditions and Effects Planning Heuristics If at hub airport  Find direct flight to destination Find bus or train route to destination Find flight to another hub airport Slide 13 of 15 Knowledge Engineering and Ontology Development 2011

  14. Discussions Contributions An integration framework for bringing planning into Onto-driven apps Plus 2 interesting challenges/observations What worked? Demonstrated feasibility with a toy problem Demonstrated effectiveness with a real-world problem What didn't? Tooling support Debugging Usability Language is still a bit technical for an average modeler Slide 14 of 15 Knowledge Engineering and Ontology Development 2011

  15. Questions and Suggestions Hope you will read our paper! More details available at: http://ontology.socs.uoguelph.ca Thank you! Slide 15 of 15 Knowledge Engineering and Ontology Development 2011

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