ijcai-tutorial

Informations

• Publié par: Ormya
• Nombre de lectures: 17
• Langue: English

Extrait

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[e-mail]
http://rakaposhi.eas.asu.edu/ijcai99 tutorial
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Planning is hot...
26% of the papers in AAAI 99. 20% of papers in IJCAI-99.
New People. Conferences. Workshops. Competitions.
Inter planetary explorations. Why the increased interest?
* Significant scale up in the * Significant strides in our
last 4 5 years understanding
– Before we could – Rich connections between
synthesize about 5 6 planning and CSP(SAT)
action plans in minutes OR (ILP)
– Now, we can synthesize » Vanishing separation
100 action plans in between planning &
minutes Scheduling
Further scale up with New ideas for heuristic
» –
domain specific control of planners
control
– Wide array of approaches
for customizing planners
with domain specific
knowledge
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Overview
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Planning : The bi g picture
* Synthesizing goal directed behavior
Planning involves
*
– Action selection; Handling causal dependencies
– Action sequencing and handling resource
allocation (aka SCHEDULING)
Depending on the problem, plans can be
*
– action sequences
– or “policies” (action trees, state action mappings
etc.)
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Planning & (Classical Plannin g)
(Static)
Environment
(Observable)
Goals
perception
action
(perfect)
(deterministic)
What action next?
I = initial state G = goa l state
(prec) O (effects)
i
[ ] [ ]
I O O O O G
i j k m
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Why care about classical Plannin g?
* Many domains are approximatel y classical
– Stabilized environments
* It is possible to handle near classical domains
through replannin g and execution monitorign
* Classical planning techniques often shed li ght on
effective ways of handlin g non classical plannign
worlds
– Currently, most of the efficient techniques for handling
non classical scenarios are still based on
ideas/advances in classical planning
* Classical planning poses man y interesting
computational challen ges
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The (too) many brands of classical planners
Planning as Theorem Provin g
Planning as Search
(Green’s planner)
Search in the space of States
(pro gression, regression, MEA)
(STRIPS, PRODIGY, TOPI)
Search in the space of Plans
(total order, partial order,
Search in the space of
protections, MTC)
Task networks (reduction
(Interplan,SNLP,TOCL,
of non primitive tasks)
UCPOP,TWEAK)
(NOAH, NONLIN,
O Plan, SIPE)
Planning as (constraint) Satisfaction
(Graphplan, IPP, STAN, SATPLAN, BLackBOX)
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Advantages of the Unified View
To the extent possible, this tutorial shuns brand names
and reconstructs important ideas underlying those
brand names in a rational fashion
* Better understanding of existing planners
Normalized comparisons between planners
–
– Evaluation of trade offs provided by various
design choices
* Design of novel planning algorithms
Hybrid planners using multiple refinements
–
– Explication of the connections between planning,
CSP, SAT and ILP
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