The PKS Planner

1. The PKS Planner Andrew Courter Texas Tech University CS5331

2. Overview of Presentation: • PKS • Why PKS? • STRIPS • The Databases • Inference Algorithm • Extended Features • PKS Examples • Conclusion and Future Work • Questions CS5331

3. PKS Planning with Knowledge and Sensing System Goal: To come up with natural plans with an incomplete set of knowledge Implement new features in PKS that will be able to solve a wider range of problems CS5331

4. STRIPS • (Stanford Research Institute Problem Solver) is the framework that PKS is based on • The known world is represented in a database and actions are represented as updates to the database • PKS uses multiple databases and stores knowledge instead of the state of the world CS5331

5. The Databases • The first database is like a STRIPS database(containing ground literals) except that both positive and negative facts are allowed • The second database is used for plan time reasoning about sensing actions • The third database stores information about function values that will be known at execution time CS5331

6. The Databases (cont’d) The fourth database contains disjunctive knowledge If one ground literal is found to be true the rest of the literals become false or if all but one literals are false the remaining one is true CS5331

7. Inference Algorithm Examines database contents to determine if an actions preconditions hold true Also determines what the effects of an action should be activated and whether or not a plan achieved its goal Four different rules are used to determine conclusions about the effects CS5331

8. Critical Assumptions The PKS has a complete knowledge of action effects and non-effects The agent’s actions are the only source of change in the world CS5331

9. Extended Features Knowing that a final conclusion relates to the initial state and all other states Numeric expressions used in evaluating numbers(evaluated down to a number at plan time) Finite valued functions in the exclusive-or knowledge database CS5331

10. Poisonous Liquid Example CS5331

11. Painted Door Example CS5331

12. Unix Domain Example CS5331

13. Conclusions The PKS system was fine tuned and can handle a wider range of planning problems using the new inference algorithm In the problems a know-whether state was achieved CS5331

14. Future Work Develop extensions to handle unknown numeric quantities Current system is unable to treat unknown file sizes in a general way in the Unix example CS5331

15. Questions?? On page 2, they discuss the Kv section how, can you guarantee the value will become known? Do you have an example? Why can’t they make the numeric evaluation work with numbers not known at run time? Can you explain the painted door problem, I am confused? What happens if the algorithm cannot deduce a plan given the current inputs? Does it stop or does it try the plan that considered a "best fit"? What is STRIPS? CS5331

16. Questions?? Does the PKS guarantee optimal solutions (plans)? When a human does not know exactly how something works or to do, they try something that they think of right on the spot, do you think this can ever be accomplished with these techniques? Can you give us some examples to explain the application of PKS? CS5331