Challenges in Adapting Automated
Planning for Autonomic Computing
Biplav Srivastava
IBM India Research Lab
sbiplav@in.ibm.com
Subbarao Kambhampati
Arizona State University
rao@asu.edu
ICAPS 2005, Monterey, CA, USA
(Also being presented at 2nd Intl. Conference on
Autonomic Computing)
The Case for Automated Planning
in Autonomic Computing
Biplav Srivastava
Subbarao Kambhampati
IBM India Research Lab
sbiplav@in.ibm.com
Arizona State University
rao@asu.edu
ICAC 2005, Seattle, USA
Presented by: Hemal Khatri
Planning in Autonomic Computing (AC)


The ‘P’ of the M-A-P-E loop in an Autonomic
Manager
Planning provides the policy engine for goaltype policies


Synthesis, Analysis & Maintenance of plans of
action is a vital aspect of Autonomic Computing

Autonomic Manager
Analyze
Given expected system behavior (goals),
determine actions to satisfy them
Plan
Example 1: Taking high-level behavioral
specifications from humans, and control the
system behavior in such a way as to satisfy the
specifications

Monitor
Knowledge
S E
Managed Element
Execute

Change requests (e.g., INSTALL, UPDATE, REMOVE)
from administrator in managing software on a
machine (Solution Install scenarios)
Example 2: Managing/propagating changes
caused by installations and component changes in
a networked environment

Remediation in the presence of failure
Information Expected to be Available
while Planning in AC Scenarios

Planning is <P, I, G, A>
 P is a set of predicates
 I and G are initial and goal states drawn from P
 A is a set of actions, Ai with
 Aipre (preconditions) Aipost (postconditions)
drawn from P
Scenario
I
G
S
Constraints
(initial
state)
(goal
state)
A
Self-configuring
Yes
Yes
-
-
Yes
Self-healing
Yes
Yes
Yes
-
Yes
Self-optimizing
-
-
-
Yes
Yes
Self-protecting
-
Yes
-
Yes
Yes
(actions)
(existing
plans)
(domain constraints)
Comparing Current Status of Automated
Planning and the Needs of AC planning

Highly scalable
planners exist for
synthesizing plans of
actions. However:


They expect
complete domain
theories
They focus on plan
generation rather
than plan
management
Early systems in AC:
a) CHAMPS: Domain-dependent planner
for self-configuration
b) ABLE-Planner4J: Domain-independent
planning for self-* but expects
complete I,G, A.

Planning technology is
relevant for AC
computing, but we
also need:



Ability to handle
incomplete domain
theories
Focus on plan
management rather
than just plan
synthesis
Support mixed
initiative continual
(re)planning
Planning with Incomplete Domain
Theories

In Autonomic computing
(as well as web-service
composition, scientific
workflow handling), the
planner doesn’t have
access to complete and
correct specification



Action specifications may
be incomplete
Domain theory may be in
terms of dependencies
The planner can’t always
verify correctness
 ..but can certainly
look for errors in a
plan


Domain theory is partial if
correctness cannot be
causally explained
 Domain theory
 Explanation
 Modification
 HTNs provide natural
support
Explainability: Event vs.
State constraints



EVENT: If you do a, then do b
before c (don’t ask why!)
STATE: The condition p is
required by a and is given by b
State constraints can be
compiled to event
constraints. But the
reverse?
Prescriptions
AC Practioners

Leverage current planning solutions
in convenient scenarios – very
efficient and will answer qns such
as:



Planning Researchers

In AC, we can at most expect
incomplete specification

What interactions will occur if a new
operation is introduced into the plan
What high-level goals will go
unsupported if an action is removed

Expend time in effect-based
modeling

Complete specifications make it easy
to provide the causal dependency
structure of the plan. This in turn
helps in plan-management by
allowing us to answer questions such
as:


What interactions will occur if a
new operation is introduced into
the plan
What high-level goals will go
unsupported if an action is
removed

Ordering constraints may be
provided without an
explanation of why they are
needed
Some information about
incompatibility of actions
may be provided
Managing such plans poses
two technical challenges:


Deriving additional
dependencies between
workflow operations
Adapting planning techniques
to deal with partial causal
information
Summary

We developed an understanding of the “planning”
needs of AC computing



Connections with 2 other very close applications—Web
Services, and Scientific Workflow management
Evaluated the match between existing planning
technology and AC computing needs, and identified
specific needed extensions
Currently focusing on plan synthesis and management
with incomplete domain theories (such as are present
in AC computing scenarios)


Impact will be measured in terms of availability of
information sought about the domain and improvement in
the quality of plans handled (analyzed/ generated/
managed).
Benchmarking will be in the software installation and
problem determination scenarios.