Model-Lite Planning for the Web Age Masses: (The challenges of Planning with Incomplete and Evolving Domain Models)

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Model-Lite Planningfor the Web Age Masses(The
challenges of Planning with Incomplete and
Evolving Domain Models)

• Subbarao Kambhampati
• Arizona State University

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What is a Senior Member Paper?

• According to the conference homepage
• Senior Member Papers
• Seasoned experts give thoughtful critiques on
  trends in the field.

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Model-Lite Planningfor the Web Age Masses(The
challenges of Planning with Incomplete and
Evolving Domain Models)

• Subbarao Kambhampati
• Arizona State University

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We have figured out how to scale synthesis..
Problem is Search Control!!!

• 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.

The primary revolution in planning in the recent
years has been methods to scale up plan synthesis
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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

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(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..

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Model-lite Planning

• We need (frame)work for planning that can get by
  with incomplete and 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 wont push NASA to send a Rover up to Mars
  without doing our best to get as good a model as
  possible

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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

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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.

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Model-Lite Planning is Planning with incomplete
models

• ..incomplete ? not enough domain knowledge to
  verify correctness/optimality
• How incomplete is incomplete?

• Missing a couple of preconditions/effects?

• Knowing no more than I/O types?

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Challenges in Realizing Model-Lite Planning

1. Planning support for shallow domain models
2. Plan creation with approximate domain models
3. Learning to improve completeness of domain models

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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)

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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
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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
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Summary
From Any Time to Any Model Planning
http//rakaposhi.eas.asu.edu/model-lite

• 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.

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It pains me to admit that a few minutes ago I
withdrew a paper from ltconferencegt on planning
for data processing that deals with some of these
issues
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Summary
From Any Time to Any Model Planning
http//rakaposhi.eas.asu.edu/model-lite

• 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.