Softbot Planning

1. Softbot Planning Christophe Bisciglia 5/16/2003

2. Agenda • Issues with Softbot Planning • Representation (KPC & SADL) • PUCCINI (Observation/Verification Links) • What’s Next?

3. Challenges to Softbots • Can traditional planners be used? • How do you represent information? • Is it complete? • Uncertainty • Can it be modeled Probabilistically? • Should it be? • What are “actions” and “goals” for Softbots? • How do you ensure sanity (reasonable plans)?

4. Motivating Examples • Personal Agents • Find me a few recent articles on “The Matrix Reloaded” • Find all .java files under my home directory, compile them, and make a JAR • Larger Scale Agents • Comparison Shopping Agents • Intelligent Spiders

5. A Softbot’s World • Can you make Open/Closed world assumptions? • How can you describe information? • Bounded? • Complete? • Correct?

7. Given unbounded, incomplete information • How do you figure out “the truth”? • What assumptions can be made about quality? • Is assuming truth reasonable? • How does assuming incomplete, but correct information limit domains? • Good/Bad examples

8. Local Closed Word Information(LCW) • Make the Closed Word Assumption about local areas of the world • How can this idea be used to determine “Ф is F” • When is something still unknown?

9. LCW - Formally • Set of Ground Literals DM • LCW Formulas DF • Of the form LCW(Ф) • IE: LCW(parent.dir(f,/tex)) • Means DM contains all files in /tex • Ф() is a LCW formula Ф with the set of variables  substituted • Ф()  DM  Truth-Value(Ф)  {T,F} • Ф  DM  Truth-Value(Ф() )  {F,U} • LCW(Ф)  DF  Ф() is F • LCW(Ф)  DF  Ф() is U

10. LCW Example • Consider the following • DM = {aips.tex=(parent.dir(aips.tex,/papers), 241 b)} • DF = {} • What do we know about the world? • What happens of the planner executes: ls –a /papers • Results: 241b aips.tex, 187 b TPSreport.tex • New state: • DM = {aips.tex=(parent.dir(aips.tex,/papers), 241 b), TPSreport.tex=(parent.dir(TPSreport.tex, /papers), 187 b)} • DF = {parent.dir(f, /papers) /\ length(f,l)}

11. LCW Example Continued • State: • DM = {aips.tex=(parent.dir(aips.tex,/papers), 241 b), TPSreport.tex=(parent.dir(TPSreport.tex, /papers), 187 b)} • DF = {parent.dir(f, /papers) /\ length(f,l)} • How do we conclude: • paret.dir(aips.tex, /papers) /\ length(aips.tex, 241 b) • parent.dir(AAAI.tex, /papers) /\ length(AAAI.tex, 921 b) • paent.dir(memo.tex, /memos) /\ length(memo.tex, 71 b)

12. LCW and Universal Quantification (briefly) • How could LCW be used for universally quantified effects? • Example: compress all files in /papers • !f parent.dir(!f, /papers) satisfy(compressed(!f)) • Plan: • Obtain LCW(parent.dir(f, /papers)) • For each f in Dm where parent.dir(f, /papers) is true, compress f • How do we know this works?

13. Allows Agent to make local conclusions Prevents Redundant Sensing – how? Universal Quantification Others? What about “mv aips.tex foo” when we have LCW on foo – Do we need to re-sense? Bookkeeping Others? LCW Pros & Cons Conclusion: Overall, LCW is great for Softbot Planning

14. Knowledge Representation • Classical Knowledge Pre-Conditions • Requires a priori knowledge that an action causes some effect • Can’t really “check if X is the case” – Consider the safe combination example • Problems with KPCs • “Representational Handcuffs” for sensing • How do you represent “I may or may not see X” – and then plan accordingly? • Why not just build contingent plans? • Would POMDPs work?

15. SADL = UWL + ADL • Designed to represent sensing actions and information goals • Eliminates Knowledge Pre-Conditions • Generalizes causal links • Categorizes effects and goals • Runtime variables (preceded by !)

16. SADL Actions • Causal Actions • Actions that change the world • IE: ? • Observational Actions • Actions that report the state of the world • IE: ?

17. SADL Goals • Satisfy Goals • Traditional Goals • Satisfy my any means possible • Initially Goals • Similar, but refers to when goal was given to agent, not when the goal is achieved • Initially (p,!tv) means by the time the plan is complete, the agents should know whether it was true when it started • What do initially goals allow?

18. SADL Goals continued… • Hands-Off Goals • Prohibits agent from modifying fluents involved • What does this do for us? • Consider this example • Goal: Delete core file • Plan: • mv TPS-report.tex core • rm core • Remember, agents are very resourceful 

19. General Causal Links • Two types discussed in PUCCINI paper • Observational Links: Ae-e,p->Ap • The effect e from A1is an observe effect needed by p for A2 • Verification Links: Ap<-p,e-Ae • Action Ap needs p to be verified by the effect e from Ae • What’s the difference? • What happens if we remove the ordering constraint as the paper suggests? • What does this do to the search space?

20. What next? • This planner is a few years old, what new technologies might be used? • What assumptions could we relax?

21. In Conclusion… • LCW is a compromise between open and closed world assumptions. • LCW prevents redundant sensing • LCW facilitates universal quantification • SADL is great when you need to describe sensing and ensure reasonable plans • Generalizing causal links gives planner more options without greatly increasing complexity