Anytime Planning for Web Service
Composition via Alternative Plan Merging
George Markou & Ioannis Refanidis
Dept. of Applied Informatics, University of Macedonia, Greece
ICTAI 2014 - Session A23. Planning
•
Introduction
•
Background
o
•
Problem formulation
Related Work
Non-Deterministic planning
o WSC
o
•
Alternative plan generation and merging
o
•
Evaluation
o
•
Example
Results
Conclusion
ICTAI 2014 - Session A23. Planning
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Introduction (1/2)
•
Web Service Composition (WSC)  reductions in time / money
required to produce enterprise applications
Number of WSs growing continuously  discovery phase more difficult
o Ever-changing environment: interfaces / usage
o Always possible that their execution is not successful
o
automatic
nondeterministic
•
Solution of non-deterministic WSC problem with complete
information is EXP-hard (Nam, Kil, and Lee 2011)
•
Semantic WSs:
fully
observable
probabilistic
problem
Functionality level: defined in regards to preconditions /effects over
ontological concepts
o Can have alternative outcomes, each with a probability of occurring
attached to it
o
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Introduction (2/2)
•
MAPPPA (anagram of Anytime Probabilistic Planning via
Alternative Plan Merging) planner
Anytime contingent planning framework
o Inspired by FF-Replan (winner of the 2004 IPPC-04)
o Takes the probabilities of the original non-deterministic actions into
consideration while generating the contingent plan
o Specifically targeted for WSC problems
o
ICTAI 2014 - Session A23. Planning
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•
Introduction
•
Background
o
•
Problem formulation
Related Work
Non-Deterministic planning
o WSC
o
•
Alternative plan generation and merging
o
•
Evaluation
o
•
Example
Results
Conclusion
ICTAI 2014 - Session A23. Planning
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Background (1/3)
•
View WSC as “planning for service chaining”
Only take into account semantic WSs at their functionality level (described in
the service profile part of OWL-S)
o Technical details, e.g., data structures or WSDL schemata, are ignored
o WSs defined by name, Inputs/Outputs and Preconditions/Effects (IOPEs)
o
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Background (2/3)
•
Translation from WS domain (OWL-S) to planning one (PPDDL)
Inputs + preconditions (hasInput + hasPrecondition)  action’s preconditions
o Outputs + effects (hasOutput + hasEffect)  action’s add effects
o IOPEs comprise set of ontological concepts  domain’s predicates
o Initial state /problem’s goal : conjunction of literals, i.e., ontological concepts
o Solution: template for execution, necessary WSs and their order of execution
o
•
Current web service repository
o
OWL-S Service Retrieval Test Collection v. 4.0
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Background (3/3) – Problem Formulation
o
Probabilistic planning domain is of the form D = S, A, γ, Pr, Co
o
o
o
o
o
o
S is a finite set of states
A is a finite set of actions
γ ∶ S × A → 2S is the state-transition function
Pr ∶ S × A × S → 0,1 is the probability-transition function
Co ∶ S × A × S → ℕ is a bounded cost-function
Planning problem is a triple P = s0 , sg , D
o s0
∈ S is the initial state
o
o
o sg
o
o
closed world semantics
only contains static propositions: truth value cannot change during the planning process
∈ S is the goal state
FOP problem
Solutions to non-deterministic problems
weak
o strong
o strong cyclic
o
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•
•
Introduction
Background
o
•
Problem formulation
Related Work
Non-Deterministic planning
o WSC
o
•
Alternative plan generation and merging
o
•
Evaluation
o
•
Example
Results
Conclusion
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Related Work (1/2) – WSC
•
Literature review suggests
o
AI planning is the method of choice for WSC
o
o
Gap in the evaluation process of current WSC systems
o
o
OWL-S to PDDL translation : Klusch, Gerber & Schmidt (2005), Hatzi et al. (2011)
Evaluation based on a single case study, without quantitative criteria is common,
e.g., Chen, Xu, and Reiff-Marganiec (2009)
Scarcity of non-deterministic WSC approaches
o
Exceptions, e.g., Hoffmann, et al. (2009)
o
o
application of a web service as a belief update operation
identify two tractable special cases of WSC under uncertainty
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Related Work (2/2) – Non-Deterministic planning
o
FF-Replan
single plan for a deterministic version of the original problem
o determinizations
o
o
o
o
o
single-outcome: select one probabilistic effect as the outcome of each action,
all-outcomes: create new action for each of the outcomes of the probabilistic effect
Ignores the probabilities attached to the probabilistic outcomes
(Jiménez, Coles, and A. Smith 2006)
all-outcomes determinization
o translation of probabilities to associated cost values ≈the risk of failing
o metric planner  minimize the product of the failure probabilities
o
o
(Dearden et al. 2003)
construct seed plan though deterministic planning
o additional (deterministic) branches added incrementally
o best place to insert branch computed based on (approximation of) the amount of
utility gained
o
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•
•
Introduction
Background
o
•
Problem formulation
Related Work
Non-Deterministic planning
o WSC
o
•
Alternative plan generation and merging
o
•
Evaluation
o
•
Example
Results
Conclusion
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Alternative plan generation and merging (1/3)
o
Anytime contingent planning algorithm - Three steps:
creates a determinized version of the problem
o generates multiple solutions to the new deterministic problem
o merges plans in a single decision tree / contingent plan
o
o
Tailor-made for WSC problems. Assumptions:
o
WSs do not produce irreversible results
o
o
once an action has been executed with a specific effect as an outcome, then for the
rest of the particular branch it cannot be executed again with a different outcome
o
o
also holds for undesired effects; If it fails in a particular branch, it will always fail in it
real-world example:
o
o
always possible, from any state in the problem, to return to the initial one
e.g., network failure, is not probable to be available again in a very short amount of
time.
consequence
o
contingent plan can execute each of the actions at most once in each of its branches 
solutions do not contain infinite branches or cycles
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Alternative plan generation and merging (2/3)
o
MAPPPA: all-outcomes determinization + incorporation of probabilities
o
each action from the original problem is associated with an aversion factor
o
o
o
Not guaranteed to converge to an optimal contingent plan
o
o
probability monotonically increases as each new (deterministic) branch is added
In general, the decision tree is a weak plan
o
o
maintain the original probabilities and costs from the probabilistic domain
combined into a single metric  how much the planner should try to avoid using this
action due to its high probability of failing or its high cost in case it succeeds
If all the decision tree branches achieve the goals, the DT is a strong plan
Planning can be based on any search algorithm that returns multiple
deterministic plans
we use a variation of A* algorithm: continues finding solutions after the first one
o aversion metric can be integrated both as the past path-cost function of A* during
planning, or as a sorting metric of the plans afterwards
o
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Alternative plan generation and merging (3/3) - Example
States represented by a circle
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Alternative plan generation and merging (3/3) - Example
Deterministic actions
denoted by a straight line
(𝑎2 and 𝑎7 )
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Alternative plan generation and merging (3/3) - Example
Probabilistic actions denoted
by a dotted line
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Alternative plan generation and merging (3/3) - Example
Some probabilistic actions
have a single effect executed
with a probability 𝑝𝑟𝑜𝑏𝛼𝑖𝑗 &
with a probability of
1 − 𝑝𝑟𝑜𝑏𝛼𝑖𝑗 they fail
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Alternative plan generation and merging (3/3) - Example
Other probabilistic actions
have two different effects,
each having a different
probability of being produced
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Alternative plan generation and merging (3/3) - Example
Cost associated with each
action is shown opposite it,
e.g., 𝑐𝑜𝑠𝑡𝛼11 = 4,𝑐𝑜𝑠𝑡𝛼7 = 2
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Alternative plan generation and merging (3/3) - Example
For this example, we use as an
aversion metric,
1
𝑔 = 𝑐𝑜𝑠𝑡𝑎𝑖𝑗 +
𝑝𝑟𝑜𝑏𝛼𝑖𝑗 +1
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Alternative plan generation and merging (3/3) - Example
Original
probabilistic
actions
All-outcomes
determinization
a11
a12
a2
a1 example, we useaas
For this
31 an
*
a2 metric,
aversion
a32
1
𝑔 =a3 𝑐𝑜𝑠𝑡𝑎𝑖𝑗 +
𝑝𝑟𝑜𝑏
a𝛼41𝑖𝑗 +1
a4
a42
a5
a6
a51
a7 *
a
*(deterministic)
52
a61
a62
a7
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Alternative plan generation and merging (3/3) - Example
Plan
𝑎11
𝑎2, 𝑎31
Prob
Cost
Aversion
0.8
4
4.55
0.8
5
6.05
= 1 ∗ 0.8
= 0.8
1
1 +11
=4+
0.81+ 1
+ 3+
0.8 + 1
= 2+
𝑔=
𝑐𝑜𝑠𝑡𝑎𝑖𝑗 +
1
𝑝𝑟𝑜𝑏𝛼𝑖𝑗 +1
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Alternative plan generation and merging (3/3) - Example
#
1
2
3
Plan
Aversion
𝑎11
𝑎2, 𝑎31
𝑎41 , 𝑎51 , 𝑎61 , 𝑎7
4.55
6.05
13.13
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Alternative plan generation and merging (3/3) - Example
#
1
2
3
4
Plan
Aversion
𝑎11
𝑎2, 𝑎31
𝑎41 , 𝑎51 , 𝑎61 , 𝑎7
𝑎42 , 𝑎61 , 𝑎7
4.55
6.05
13.13
17.96
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Alternative plan generation and merging (3/3) - Example
#
1
2
3
4
5
Plan
Aversion
𝑎11
𝑎2, 𝑎31
𝑎41 , 𝑎51 , 𝑎61 , 𝑎7
𝑎42 , 𝑎61 , 𝑎7
𝑎41 , 𝑎51 , 𝑎62
4.55
6.05
13.13
17.96
19.91
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Alternative plan generation and merging (3/3) - Example
#
1
2
3
4
5
6
Plan
Aversion
𝑎11
𝑎2, 𝑎31
𝑎41 , 𝑎51 , 𝑎61 , 𝑎7
𝑎42 , 𝑎61 , 𝑎7
𝑎41 , 𝑎51 , 𝑎62
𝑎42 , 𝑎62
4.55
6.05
13.13
17.96
19.91
31.74
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Alternative plan generation and merging (3/3) - Example
#
1
2
3
4
5
6
Plan
Aversion
𝑎11
𝑎2, 𝑎31
𝑎41 , 𝑎51 , 𝑎61 , 𝑎7
𝑎42 , 𝑎61 , 𝑎7
𝑎41 , 𝑎51 , 𝑎62
𝑎42 , 𝑎62
4.55
6.05
13.13
17.96
19.91
31.74
Circular nodes
= Chance nodes
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Alternative plan generation and merging (3/3) - Example
#
1
2
3
4
5
6
Plan
Aversion
𝑎11
𝑎2, 𝑎31
𝑎41 , 𝑎51 , 𝑎61 , 𝑎7
𝑎42 , 𝑎61 , 𝑎7
𝑎41 , 𝑎51 , 𝑎62
𝑎42 , 𝑎62
4.55
6.05
13.13
17.96
19.91
31.74
Grey nodes
potentially lead
to the goal
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Alternative plan generation and merging (3/3) - Example
#
1
2
3
4
5
6
Plan
Aversion
𝑎11
𝑎2, 𝑎31
𝑎41 , 𝑎51 , 𝑎61 , 𝑎7
𝑎42 , 𝑎61 , 𝑎7
𝑎41 , 𝑎51 , 𝑎62
𝑎42 , 𝑎62
4.55
6.05
13.13
17.96
19.91
31.74
White nodes
don’t lead to the
goal
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Alternative plan generation and merging (3/3) - Example
#
1
2
3
4
5
6
Plan
Aversion
𝑎11
𝑎2, 𝑎31
𝑎41 , 𝑎51 , 𝑎61 , 𝑎7
𝑎42 , 𝑎61 , 𝑎7
𝑎41 , 𝑎51 , 𝑎62
𝑎42 , 𝑎62
4.55
6.05
13.13
17.96
19.91
31.74
Triangular nodes
= end nodes
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Alternative plan generation and merging (3/3) - Example
#
1
2
3
4
5
6
Plan
Aversion
𝑎11
𝑎2, 𝑎31
𝑎41 , 𝑎51 , 𝑎61 , 𝑎7
𝑎42 , 𝑎61 , 𝑎7
𝑎41 , 𝑎51 , 𝑎62
𝑎42 , 𝑎62
4.55
6.05
13.13
17.96
19.91
31.74
Goal node
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Alternative plan generation and merging (3/3) - Example
#
1
2
3
4
5
6
Plan
Aversion
𝑎11
𝑎2, 𝑎31
𝑎41 , 𝑎51 , 𝑎61 , 𝑎7
𝑎42 , 𝑎61 , 𝑎7
𝑎41 , 𝑎51 , 𝑎62
𝑎42 , 𝑎62
4.55
6.05
13.13
17.96
19.91
31.74
Dead-end node
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Alternative plan generation and merging (3/3) - Example
#
1
2
3
4
5
6
Plan
Aversion
𝑎11
𝑎2, 𝑎31
𝑎41 , 𝑎51 , 𝑎61 , 𝑎7
𝑎42 , 𝑎61 , 𝑎7
𝑎41 , 𝑎51 , 𝑎62
𝑎42 , 𝑎62
Best plan = a11
First action = a11
Deterministic = a11  Probabilistic = a1
4.55
6.05
13.13
17.96
19.91
31.74
𝑓𝑎𝑖𝑙𝑒𝑑 𝑒𝑥𝑒𝑐𝑢𝑡𝑖𝑜𝑛 𝑜𝑓 𝑎𝑐𝑡𝑖𝑜𝑛
𝑠𝑢𝑐𝑐𝑒𝑠𝑠𝑓𝑢𝑙 𝑒𝑥𝑒𝑐𝑢𝑡𝑖𝑜𝑛 𝑜𝑓 𝑎𝑐𝑡𝑖𝑜𝑛
𝑁𝑒𝑥𝑡?
𝐺𝑜𝑎𝑙!
Current plan = a11 does not contain any
more actions
Compute possible valid
plans
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Remember
assumption:
Alternative plan generation and merging
(3/3)
Example
• If an action was executed with a
#
1
2
3
4
5
6
Plan
particular result it has this result for its
entire branch.
• No delete effects or negated
Compute possible
valid  output effects still hold;
preconditions
plans
no need for re- execution
Aversion
𝑎11
𝑎2, 𝑎31
𝑎41 , 𝑎51 , 𝑎61 , 𝑎7
𝑎42 , 𝑎61 , 𝑎7
𝑎41 , 𝑎51 , 𝑎62
𝑎42 , 𝑎62
4.55
6.05
13.13
17.96
19.91
31.74
If a plan in the set of valid plans contains at any point actions that have
already been executed in the current branch
1) If executed action had the same outcome as the one in the plan  insert
plan into branch without the particular action
2) If executed action had a different outcome as the one in the plan  the
entire plan is rejected for this particular branch
Set of valid plans may not be the same as the original one
• some actions may have been removed from the plans due to (1)
• Action 𝑎1 is the only one
• their cost and probability of successful execution have also changed
contained in the current branch
• plans are sorted again by their ascending aversion factors
• only present in 𝑃𝑙𝑎𝑛• 1 that
some plans may have been removed from the set due to (2)
has already been inserted
• All other plans
• can be inserted intact
• retain their aversion factor
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Alternative plan generation and merging (3/3) - Example
#
2
3
4
5
6
Plan
Aversion
𝑎2, 𝑎31
𝑎41 , 𝑎51 , 𝑎61 , 𝑎7
𝑎42 , 𝑎61 , 𝑎7
𝑎41 , 𝑎51 , 𝑎62
𝑎42 , 𝑎62
Best plan = 𝑎2, 𝑎31
First action = a2
6.05
13.13
17.96
19.91
31.74
𝑠𝑢𝑐𝑐𝑒𝑠𝑠𝑓𝑢𝑙 𝑒𝑥𝑒𝑐𝑢𝑡𝑖𝑜𝑛 𝑜𝑓 𝑎𝑐𝑡𝑖𝑜𝑛
𝐷𝑒𝑡𝑒𝑟𝑚𝑖𝑛𝑖𝑠𝑡𝑖𝑐
𝑐𝑎𝑛𝑛𝑜𝑡 𝑓𝑎𝑖𝑙
Next action in plan = a31
Deterministic = a31  Probabilistic = a3
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Alternative plan generation and merging (3/3) - Example
#
Plan
Aversion
2
3
4
5
6
𝑎2, 𝑎31
𝑎41 , 𝑎51 , 𝑎61 , 𝑎7
𝑎42 , 𝑎61 , 𝑎7
𝑎41 , 𝑎51 , 𝑎62
𝑎42 , 𝑎62
6.05
13.13
17.96
19.91
31.74
𝑓𝑎𝑖𝑙𝑒𝑑 𝑒𝑥𝑒𝑐𝑢𝑡𝑖𝑜𝑛 𝑜𝑓 𝑎𝑐𝑡𝑖𝑜𝑛
𝑠𝑢𝑐𝑐𝑒𝑠𝑠𝑓𝑢𝑙 𝑒𝑥𝑒𝑐𝑢𝑡𝑖𝑜𝑛 𝑜𝑓 𝑎𝑐𝑡𝑖𝑜𝑛
𝑁𝑒𝑥𝑡?
𝐺𝑜𝑎𝑙!
Current plan does not contain any more
actions
Compute possible valid
plans
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Alternative plan generation and merging (3/3) - Example
#
Plan
Aversion
1
2
3
4
5
6
𝑎11
𝑎2, 𝑎31
𝑎41 , 𝑎51 , 𝑎61 , 𝑎7
𝑎42 , 𝑎61 , 𝑎7
𝑎41 , 𝑎51 , 𝑎62
𝑎42 , 𝑎62
4.55
6.05
13.13
17.96
19.91
31.74
Compute possible valid
plans
•
•
•
Current branch contains actions 𝑎12 , 𝑎2 , 𝑎32
All actions are only present in 𝑃𝑙𝑎𝑛1 and 𝑃𝑙𝑎𝑛2 ;
• 𝑎2 has already been executed with the same result  removed
• 𝑎1 and 𝑎3 have already been inserted with different results
• 𝑃𝑙𝑎𝑛1 and 𝑃𝑙𝑎𝑛2 are rejected
All other plans
• can be inserted intact
• retain their aversion factor
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Alternative plan generation and merging (3/3) - Example
#
3
4
5
6
Plan
Aversion
𝑎41 , 𝑎51 , 𝑎61 , 𝑎7
𝑎42 , 𝑎61 , 𝑎7
𝑎41 , 𝑎51 , 𝑎62
𝑎42 , 𝑎62
13.13
17.96
19.91
31.74
𝑠𝑢𝑐𝑐𝑒𝑠𝑠
𝑓𝑎𝑖𝑙𝑢𝑟𝑒
𝑁𝑒𝑥𝑡?
𝑓𝑎𝑖𝑙𝑢𝑟𝑒
𝑑𝑒𝑎𝑑 − 𝑒𝑛𝑑!
Current plan does not contain any more
actions
𝑠𝑢𝑐𝑐𝑒𝑠𝑠
𝑠𝑢𝑐𝑐𝑒𝑠𝑠
𝑓𝑎𝑖𝑙𝑢𝑟𝑒
𝑑𝑒𝑎𝑑 − 𝑒𝑛𝑑!
Compute possible valid
plans
ICTAI 2014 - Session A23. Planning
𝑠𝑢𝑐𝑐𝑒𝑠𝑠
𝐺𝑜𝑎𝑙!
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Alternative plan generation and merging (3/3) - Example
#
Plan
1
2
3
4
5
6
𝑎11
𝑎2, 𝑎31
𝑎41 , 𝑎51 , 𝑎61 , 𝑎7
𝑎42 , 𝑎61 , 𝑎7
𝑎41 , 𝑎51 , 𝑎62
𝑎42 , 𝑎62
Compute possible valid
plans
Aversion
17.96 7.05
•
•
31.74 20.83
•
Current branch contains actions 𝑎12 , 𝑎2 , 𝑎32 , 𝑎42
All plans contain one of those actions
• 𝑃𝑙𝑎𝑛1 and 𝑃𝑙𝑎𝑛2 contain 𝑎1 and 𝑎3 with different results
• 𝑃𝑙𝑎𝑛1 and 𝑃𝑙𝑎𝑛2 are rejected
• 𝑃𝑙𝑎𝑛3 and 𝑃𝑙𝑎𝑛5 contain 𝑎4 with a different result
• 𝑃𝑙𝑎𝑛3 and 𝑃𝑙𝑎𝑛5 are rejected
𝑃𝑙𝑎𝑛3 and 𝑃𝑙𝑎𝑛5 contain 𝑎4 with the same result
• 𝑎42 is removed, as it has already been executed
• Since they now comprise different actions
• new (smaller, as they are more probable) aversion factor
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Alternative plan generation and merging (3/3) - Example
#
4
6
Plan
Aversion
𝑎42 , 𝑎61 , 𝑎7
𝑎42 , 𝑎62
7.05
20.83
𝑠𝑢𝑐𝑐𝑒𝑠𝑠
𝐺𝑜𝑎𝑙!
𝑠𝑢𝑐𝑐𝑒𝑠𝑠
𝑠𝑢𝑐𝑐𝑒𝑠𝑠
𝐺𝑜𝑎𝑙!
Current plan does not contain any more
actions
Compute possible valid
plans
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•
•
Introduction
Background
o
•
Problem formulation
Related Work
Non-Deterministic planning
o WSC
o
•
Alternative plan generation and merging
o
•
Evaluation
o
•
Example
Results
Conclusion
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Evaluation (1/4)
•
Evaluation based on two domains
o Variation of the one presented as an example
o Modified version of the evaluation domain from Hatzi et al. (2011)
o
A user desires to purchase a book through an electronic bookstore
o
o
knows book title / author, credit card information, and shipping address
result: book’s purchase, shipping date, and customs cost for the item
o Both domains have two versions
uniform costs for the web services (Domuni
x )
o variant cost for each web service (Domvar
x )
o
o Domain costs
o
start from 1
o
taken from exponential probability density function, f 𝑥 = 𝑒 −
ICTAI 2014 - Session A23. Planning
𝑥−1
, 𝑥≥1
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Evaluation (2/4)
•
Different setup versions for A* (search algorithm)
o combinations of path-cost function /
o
heuristic estimates
o
max heuristic (hmax ) (not admissible in all settings)
additive heuristic (hadd ), with the extra assumption that the action costs are unary
h=0
o
h𝑠𝑖𝑚𝑖 = 𝑝𝑟𝑒𝑑𝑖𝑐𝑎𝑡𝑒𝑠 ∈ sg /s𝑖 , i.e., number of goals that not yet achieved
o
Bonet and Geffner (2001)
o
o
heuristic estimates
path-cost functions / aversion metrics for A*:
o
𝑔𝑎 =
o
𝑔𝑏 =
o
𝑔𝑐 =
o
𝑔𝑑 =
o
𝑔𝑒 = 𝑎
cost aij +
1
,
probαij +1
costaij
probaij +1
costaij
,
probaij +1
cost aij −
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probaij
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Evaluation (3/4) - Results
o
In all cases, the algorithm outputted all possible solutions
o
o
the resulting decision trees varied according to the selected path-cost function
Heuristics
o
In (the simpler) 𝐷𝑜𝑚1 all heuristics, in any combination with a path-cost function,
produce solutions quickly
o
o
the simpler heuristics ℎ = 0 and hsim produce the plans in less time than the others
ℎ𝑎𝑑𝑑 and ℎ𝑚𝑎𝑥 are more informed heuristics. Worse performance probably due to:
o
combination of faster computation of simpler heuristics and hardness of domains
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Evaluation (4/4) - Results
o
Path-cost functions
In regard to time and amount
In (the simpler) 𝐷𝑜𝑚1 the choice of path-cost function is
as important
of not
expanded
states as the
heuristic in regard to the required time
o However, using the number of actions (𝑔 = 𝑎 ) as the path-cost function
regardless of the heuristic  most effective
o
o
Overall
the approach is efficient in the evaluation domains tested
o simple, non-time-consuming heuristics, are the method of choice
o
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•
•
Introduction
Background
o
•
Problem formulation
Related Work
Non-Deterministic planning
o WSC
o
•
Alternative plan generation and merging
o
•
Evaluation
o
•
Example
Results
Conclusion
o
Future work
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Conclusion
• MAPPPA:
o Anytime probabilistic planner
o Produces a contingent plan by integrating alternative
deterministic solutions to a determinized probabilistic problem
determinized problem does not disregard WSs reliability or cost
o promising results in our evaluation
o integrated into our prototype WSC platform, MADSWAN*
o
*(alpha
version available currently only contains deterministic WSC algorithm)
gmarkou@uom.gr
http://goo.gl/mqyEOX
http://goo.gl/IBgax3
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Future work
o Planning:
Use an existing deterministic planner to generate the alternative plans
o Optimized existing implementation of A*
o
o
o
prune paths subsumed by others  check if all previously added actions in path already form
another, shorter, solution
prune paths trying to add a deterministic action 𝑎𝑥𝑖, , if 𝑎𝑥𝑗 is already in the same path
o OWL-S
Consider ontological concepts’ matches covering plug-in or subsumes
relationships
same non-deterministic
with a different
o Support OWL-S extensions that allows description of WS QoSaction
elements,
e.g., OWL-Q
o
alternative outcome
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Thank you for your attention!
Questions?
References:
•
•
•
•
•
•
•
•
B. Bonet, and H. Geffner, “Planning as heuristic search,” Artif. Intell., vol. 129, no. 1, pp. 5-33, 2001.
K. Chen, J. Xu, and S. Reiff-Marganiec, “Markov-HTN planning approach to enhance flexibility of automatic web
service composition”, Proc. IEEE International Conference on Web Services (ICWS'09), July 2009, pp. 9-16.
R. Dearden, N. Meuleau, S. Ramakrishnan, D.E. Smith, and Washington, R. “Incremental contingency planning,” in
Proc. of the Thirteenth ICAPS Workshop on Planning under Uncertainty, 2003.
O. Hatzi, D. Vrakas, M. Nikolaidou, et al., “An integrated approach to automated semantic web service composition
through planning”, IEEE Trans. Service Computing, April 2011, pp. 301-308.
J. Hoffmann, P. Bertoli, M. Helmert, and M. Pistore, “Message-based web service composition, integrity
constraints, and planning under uncertainty: a new connection”, J. Artif. Intell. Res, vol. 35, May 2009, pp.49-117.
S. Jiménez, A. Coles, and A. Smith, “Planning in probabilistic domains using a deterministic numeric planner, in
Proc. of the Twenty-fifth Workshop of the UK Planning and Scheduling Special Interest Group (PlanSig), 2006.
M. Klusch, Α. Gerber, and M. Schmidt, “Semantic web service composition planning with OWLS-Xplan”, Proc. 1st
International AAAI Fall Symposium on Agents and the Semantic Web, Nov. 2005.
W. Nam, H. Kil, and D. Lee 2011, “On the computational complexity of behavioral description-based web service
composition,” Theor. Comput. Sci., vol. 412, no. 48, pp. 6736-6749, 2011.
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