Subbarao Kambhampati Arizona State University

1. Model-Lite Planningfor the Web Age Masses:(The challenges of Planning with Incomplete and Evolving Domain Models) Subbarao Kambhampati Arizona State University

2. Has gray hair (..and doesn’t color it..) • “No need to code or prove” “Spicy” What is a Senior Member Paper? • According to the conference homepage: • Senior Member Papers • Seasoned experts give thoughtful critiques on trends in the field.

3. Model-Lite Planningfor the Web Age Masses:(The challenges of Planning with Incomplete and Evolving Domain Models) Subbarao Kambhampati Arizona State University

4. Before, planning algorithms could synthesize about 6 – 10 action plans in minutes Significant scale-up in the last 6-7 years Now, we can synthesize 100 action plans in seconds. Realistic encodings of Munich airport! We have figured out how to scale synthesis.. Problem is Search Control!!! The primary revolution in planning in the recent years has been methods to scale up plan synthesis

5. …and we are all busy extending this success to increasingly expressive models • Now that we can make mince-meat of classical problems, we turned our attention to • Temporal planning • Over-subscription planning • Hierarchical task network planning • Planning under uncertainty & partial observability • Successively increasing model expressiveness • Implicit in this trajectory is the assumption: The way to get more applications is to tackle more and more expressive domains

6. (Gently) Questioning the Assumption • The way to get more applications is to tackle more and more expressive domains • There are many scenarios where domain modeling is the biggest obstacle • Web Service Composition • Most services have very little formal models attached • Workflow management • Most workflows are provided with little information about underlying causal models • Learning to plan from demonstrations • We will have to contend with incomplete and evolving domain models.. • ..but our applications assume complete and correct models..

7. Model-lite Planning • We need (frame)work for planning that can get by with incompleteand evolving domain models. • I want to convince you that there are interesting research challenges in doing this. • Disclaimers • I am not arguing against model-intensive planning • We won’t push NASA to send a Rover up to Mars without doing our best to get as good a model as possible

8. Model-lite is in the Bible.. • Interest in model-lite planning is quite old (but has been subverted..) • Originally, HTN planning (a la NOAH) was supposed to allow incomplete models of lower-level actions.. • Originally, Case-based planning was supposed to be a theory of slapping together plans without knowing their full causal models

9. Personal motivations • My attempts to apply planning techniques to Autonomic Planning (ICAC 2005) • Interested in developing automatic patching scripts (but the difficulty was modeling..) • My attempts to get a snap shot of public domain web services (SIGMOD Record 2005) • Very few of them had any formal specification (beyond some disjointed “english descriptions”) • My experience with data/information integration problems (AAAI 2007 tutorial) • Where the competing pulls from • model-poor Information retrieval • Model-rich data/knowledge based approaches have lead to interest in reasoning with semi-structured (or any sturctured) data.

11. Model-Lite Planning is Planning with incomplete models • ..“incomplete”  “not enough domain knowledge to verify correctness/optimality” • How incomplete is incomplete? • Knowing no more than I/O types? • Missing a couple of preconditions/effects?

12. Challenges in Realizing Model-Lite Planning • Planning support for shallow domain models • Plan creation with approximate domain models • Learning to improve completeness of domain models

13. Challenge: Planning Support for Shallow Domain Models • Provide planning support that exploits the shallow model available • Idea: Explore wider variety of domain knowledge that can either be easily specified interactively or learned/mined. E.g. • I/O type specifications (e.g. Woogle) • Task Dependencies (e.g. workflow specifications) • Qn: Can these be compiled down to a common substrate? • Types of planning support that can be provided with such knowledge • Critiquing plans in mixed-initiative scenarios • Detecting incorrectness (as against verifying correctness)

14. Challenge: Plan Creation with Approximate Domain Models • Support plan creation despite missing details in the model. The missing details may be (1) action models (2) cost/utility models • Example: Generate robust “line” plans in the face of incompleteness of action description • View model incompleteness as a form of uncertainty (e.g. work by Amir et. al.) • Example: Generate Diverse/Multi-option plans in the face of incompleteness of cost model • Our IJCAI-2007 work can be viewed as being motivated this way.. Note: Model-lite planning aims to reduce the modeling burden; the planning itself may actually be harder

15. Challenge: Learning to Improve Completeness of Domain Models • In traditional “model-intensive” planning learning is mostly motivated for speedup • ..and it has gradually become less and less important with the advent of fast heuristic planners • In model-lite planning, learning (also) helps in model acquisition and model refinement. • Learning from a variety of sources • Textual descriptions; plan traces; expert demonstrations • Learning in the presence of background knowledge • The current model serves as background knowledge for additional refinements for learning • Example efforts • Much of DARPA IL program (including our LSP system); PLOW etc. • Stochastic Explanation-based Learning (ICAPS 2007 wkhop) Make planning Model-lite  Make learning knowledge (model) rich

16. From “Any Time” to “Any Model” Planning http://rakaposhi.eas.asu.edu/model-lite Summary • While model-intensive planning continues to have a place (e.g. NASA), we should also look at model-lite planning • Applications include workflows, web services, desktop automation, collaborative learning/planning • The aim is to reduce modeling burden. • Either by reducing planning support (shallow domain models) • or by increasing the plan creation cost (approximate domain models) • The challenges in each are different.. • Learning goes hand-in-hand with planning in model-lite planning scenarios.

17. …It pains me to admit that a few minutes ago I withdrew a paper from <conference> on planning for data processing that deals with some of these issues

18. From “Any Time” to “Any Model” Planning http://rakaposhi.eas.asu.edu/model-lite Summary • While model-intensive planning continues to have a place (e.g. NASA), we should also look at model-lite planning • Applications include workflows, web services, desktop automation, collaborative learning/planning • The aim is to reduce modeling burden. • Either by reducing planning support (shallow domain models) • or by increasing the plan creation cost (approximate domain models) • The challenges in each are different.. • Learning goes hand-in-hand with planning in model-lite planning scenarios.