Localizing Planning with Functional Process Models

1. Localizing Planningwith Functional Process Models J. William Murdock Intelligent Decision Aids Group Navy Center for Applied Research in AI Naval Research Laboratory, Code 5515 Washington, DC 20375 bill@murdocks.org http://bill.murdocks.org Ashok K. Goel Artificial Intelligence Laboratory College of Computing Georgia Institute of Technology Atlanta, GA 30332-0280 goel@cc.gatech.edu http://www.cc.gatech.edu/ai/faculty/goel/ International Conference on Automated Planning & SchedulingTrento, Italy: June, 2003

2. Localizing Planning: Models • Problem: Traditional generative planning can be very time consuming • Alternative: Use precompiled knowledge of tasks and methods • Can be very fast for addressing tasks that you know • Requires knowledge engineering • Useless for unknown tasks • Compromise: Use precompiled knowledge for some parts of a task and generative planning for other parts

3. URL’s, servers, documents, etc. Access WWW Remote Local … Request Receive Store TMK(Task-Method-Knowledge) • TMK models encode knowledge about processes (e.g., military operations, computer programs) • TMK encodes: • Tasks: functional specification / requirements and results • Methods: behavioral specification / composition and control • Knowledge: Domain concepts and relations

4. REM Reflective Evolutionary Mind • A shell for adaptive software systems encoded as TMK models • Can retrieve and adapt methods for known tasks • Can also build new methods from scratch • Variety of mechanisms for building/modifying methods • This talk focuses on one: generative planning

5. Done! Task Knowledge … Execute Subtask Subtask Create New Method Task Knowledge or Retrieve Existing Method Adapt Existing Method REM Functional Architecture

6. Illustrative Shipping Domain • Structured shipping domain • Loading crates on to a truck, driving them to a destination, delivering documentation • Loading subproblem is isomorphic to Tower-of-Hanoi • Solution cost grows rapidly with the number of crates • Driving and delivering documentation: 1 action each Destination Pallet Warehouse

7. Key Task Method Knowledge Shipping Model Deliver with Documentation Select Object Move Stack Drive Deliver Object Recipient Documentation Agent Select Move Move Crate Manifest Paper Truck Object Warehouse Destination In Truck Crate Place

8. Iterative method, loops over multiple crates Key Trivial method, only works with one crate Task Method Shipping Model Deliver with Documentation Select Object Move Stack Drive Deliver Object Select Move Move Crate

9. Model Execution • If the given task is non-primitive • Chose a method whose applicability conditions are met • While the state-transition machine encoded in the method is not at an end state: • Execute the subtask for the current state • Chose a transition from the current state whose applicability conditions are met • Else execute the primitive task • Choices are made through weighted random selection; reinforcement learning influences these weights. • In most models, usually only one choice at each decision point • Throughout execution, REM records a trace of the tasks and methods performed and the knowledge used.

10. Key Task Method Running the Shipping Model Deliver with Documentation Select Object Move Stack Drive Deliver Object Select Move Move Crate

11. Key Only the trivial method; no iterative method Task Method Ablated Shipping Model Deliver with Documentation Select Object Move Stack Drive Deliver Object Select Move Move Crate

12. Adaptation UsingGenerative Planning • Requires operators and a set of facts (initial state) • Invokes external planner (i.e., Graphplan) • Operators = Those primitive tasks known to the agent which can be translated into planner’s operator language • Facts = Known assertions which involve relations referred to by the operators • Goal = Makes condition of main task • Translates plan into more general method by turning instances into parameters & propagating bindings • Stores method for later reuse

13. Key Task Method Running the Ablated Model Deliver with Documentation Select Object Move Stack Drive Deliver Object • Halts (no applicable method) • Generative planning adaptation creates a new method for Move Stack Select Move Move Crate • Runs again with the adapted model

14. Key Task Method Ablated model + generative planning Deliver with Documentation Select Object Move Stack Drive Deliver Object Select Move Move Crate New method (from planning)

15. Key Task Method Generative planning only Deliver with Documentation Move Crate Drive Deliver Object New method (from planning)

16. Shipping Example Results

17. # Complete Ablated None 1 6.4 6.6 7.6 2 7.3 10.4 8.2 3 9.3 11.7 10.0 Contrast: Small Problems(overheads dominate) • 1 crate: • Complete and ablated both require no adaptation (very fast) • None requires planning (slower) • 2 & 3 crates: • Complete still requires no adaptation (very fast) • None requires planning (slower) • Ablated requires model-based credit assignment and planning (slowest)

18. # Complete Ablated None 4 12.8s ~1hr ~4.5hr 5 21.1s 6 43.2s Contrast: Larger Problems(knowledge = power) • Complete model much faster than planning with 4 or more crates. • The ablated model took 3.5 hours less than no model. • The plan produced with no model only had 2 extra steps! (Drive, Deliver Object) • All of the Move Crate actions are the same in both conditions.

19. Other Adaptation Strategies • Relation mapping (Murdock & Goel ICCBR’01) • Model-based mechanism for creating a method for a new task • Analogical transfer from existing (retrieved) method • Fixed-value production (Murdock & Goel, AI’01) • Model-based mechanism for repairing a known method • Uses an execution trace to diagnose the source of failure • Situated Learning • Knowledge-lean method for creating a method for a new task • Creates a method that can take any action at any time • Reinforcement learning during execution eventually refines that method into an effective policy

20. Conclusions • Localized planning for a subproblem can be much faster than planning for a complete problem • Even when the subproblem includes almost all of the complexity • I.e., removing minor distractions can greatly speed up planning • Functional models enable localization of reasoning failures that can be addressed by planning • REM enables the creation and modification of methods using generative-planning and other techniques • REM enables efficient and flexible reasoning

21. Current/Future Work • Integration with other planners using PDDL • Currently: STRIPS-style only • Experiments using this mechanism soon • Expect similar results at different scales • Future: More PDDL levels • Testing on more domains • Benchmark domains (e.g., IPC) • Real-world domains • Generalizing methods from individual plans • e.g., inferring a “loop” in the model from many concrete instances of varying lengths • More integration with model-based techniques

22. AHEAD: Explanation of Threats Analogical Hypothesis Elaborator for Activity Detection • Objective: Helping intelligence analysts understand and trust hypotheses about hostile, covert activity • e.g., organized crime, terrorism • Input: Hypothesis about hostile activity & lots of raw data • Output: Arguments for and/or against the hypothesis • Knowledge: HTN’s of hostile activities, annotated with functional (TMK-based) information. • Reasoning: • MAC/FAC (Forbus & Gentner, CogSci 1991) maps the given hypothesis to a structured index for an HTN • Modified SHOP2 (Nau, et al. JAIR 2003) uses that HTN to generate an annotated plan that approximates the hypothesis • Where raw data matches the effects of actions in the plan, AHEAD produces an argument for the hypothesis • Where raw data is missing or contradicts the plan, AHEAD produces an argument against the hypothesis

23. Hypothesis Pilot Study: Subjects with arguments were faster, more confident, and significantly more accurate DETAILS @ ICCBR’03! Arguments AHEAD User Interface