AI Planning for Semantic Web Service Composition

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AI Planning for Semantic Web Service Composition?

• Survey of current approaches,
• challenges and open questions

presented by Axel Polleres axel.polleres_at_deri.org
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Let's start with a pessimistic view
Jim Hendler's opinion 01/02/2004
public-sws-ig_at_w3.org

• I would point out that the mapping of web
  services to compositions has largely been done in
  the past, even in the best work in
• this area, with some simplifications that
  generally "twist" web services into a planning
  framework -- there's huge parts of the
• web service world that need to be explored before
  we can really say AI planning has shown a success
  in web services other
• than as an evocative idea -- the reason is that
  a real web services engine will need to deal with
  (at least)
• scaling issues way beyond anything we've seen in
  planning to date (there may be thousands of
  services each with multiple ports, optional
  arguments, etc.)
• the issues Dana mentioned (side effects, change
  in the world, etc.) that make Strips-operators
  planning an approximation at best (the assumption
  that change occurs only through the operators
  under the control of this planner is clearly
  wrong)
• issues of interaction with users - web service
  planning better be more mixed-initiative
• issues of preferences v. constraints
• issues of interaction between planning agents out
  there (you buy the book I'm in the process of
  planning to buy)
• knowledge engineering issues (when planners take
  ebXML and WSDL as inputs, instead of requiring
  specialized planning-like languages like
• OWL-S, then we'll see a lot more excitement on
  the outside - OWL-S is an interesting starting
  place, but we fool ourselves if we think it
  really is going to be widely used for process
  specification in its current form)
• In fact, I'll wager that it will be absolutely
  trivial to prove that web services planning, even
  w/simplifications, is inherently
• undecidable, so we'll need to explore a lot of
  the issues from the old "dynamic planning" world
  as well. All this, by the way, I
• see as good news - it means this is a fertile and
  exciting research area for those of us in
  planning, with good heuristic solutions
• being transitionable. That said, in the past I
  have tried to get AI planning people to think
  outside the box and failed miserably,
• and I'll be surprised if the web services
  planning stuff doesn't become an "applied" area
  being ignored by the bulk of the
• research community (who, if you'll apologize my
  saying so, still have their heads up their
  you-know-whats worrying about
• scaling simple problems in all the wrong ways)

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and let's see what still might be achieved

• AI Planning vs. Web Service Composition
• Non-classical Planning
• Planning approaches for WSC
• Open Questions Challenges
• Relations with our current WSMO efforts

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AI Planning

• is more than Blocks World!

B
move(a,table) move(c,a) move(b,c)
A
C
B
C
A

• The Classical Planning Problem
• - Complete Knowledge of the world
• Atomic actions with, deterministic effects
• Set of actions finite and static
• Full observability
• Only sequential plans
• Goals mostly simple conjunctions of propositions
• Domain description uses a fixed terminology,
  mostly a finite set of state variables (often
  called fluents)

"Given a set of actions, their preconditions and
positive and negative effects, a complete
description of the initial state and a user goal
find a sequence of actions achieving the goal".
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Classical Planners

• STRIPS like domains
• Forward-chaining, backward chaining and
  combinations (Graphplan, etc.)
• Successful heuristics, but problems of limited
  real-world value (IPC bi-annual competition,
  combined with AIPS,ICAPS)
• However, Can maybe serve as an approximation at a
  high level of abstraction in semi-automatic
  approaches.

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Semantic Web Services on the other hand

• Semantics descriptions of Web services, where
• The number of available services is huge
• (shallow and broad search space vs. narrow and
  deep search space for which most planning
  algorithms are tailored)
• and results possibly unknown, only the type
• (need for typing, conceptual reasoning over
  types, ontologies, take information gathering
  into account, selection step for sets of
  information)
• Services might expose complex interfaces with
  complex message exchange patterns (choreography,
  multi-agent collaboration rather than
  single-agent planning)
• but without full insight
• (incomplete knowledge, non-determinism)
• Need to compose complex services (i.e.,
  "pre-defined plans" orchestration, cf. OWL-S
  process models)

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Non-classical PlanningLoosen the restrictions
to complete knowledge, and deterministic actions

• Conditional Planning Peot,Smith,92 construct a
  branching plan(-tree) taking all possible
  contingencies into account.
• Conformant Planning Goldman,Boddy,96
• find a plan which works in any initial situation
  even under incomplete knowledge or when
  non-deterministic effects.
• Hierarchical Task Network Planning (HTN) Erol et
  al. 1994 views a plan as a hierarchical network
  of tasks (however, mostly only simple sequences,
  no complex control structures like in OWL-S,
  BPEL4WS, etc.)!
• Dealing with Non-classical Goals Maintainance
  goals, preferences, etc.

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Non-classical planners

• Planning as model checking (Jensen,Veloso,2001,
  Cimatti, et al.,97 and subsequent extensions)
  
• compact representation of belief states in BDDs
  for planning under incomplete knowledge.
• Complex plans (strong and weak plans, branching
  plans, cyclic plans)
• Complex goals (EaGLe)
• Scalability for huge sets of actions? Composing
  of pre-canned plans? Son,McIlraith,2002

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Planning for WS, examples

• McDermott,2002
• suggests extension of PDDL with a polymorphic
  typing system for information gathering actions
  as a representation language for planning
• Sketches how classical goal-regression planner
  can be extended to create conditional plans "on
  demand" (scalability not touched, assume number
  of branches is low).
• Description of interface as a set of planning
  operations instead of a process definition!
  Extension to multiple-agents then of course
  trivial! Does not (yet?) assume but mention that
  ontological differences have to be resolved first
• Pistore, et al.,2004
• Based on planning as model-checking
• Automatic composition of BPEL4WS processes given
  a desired user process
• Also leaves ontological differences out
• Currently reported performance not usable for
  complex protocols
• Wu, et al. 2003
• Translating DAML-S to HTN, and use HTN planner
  SHOP2 for plan generation
• Makes many simplifying assumptions

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Planning for WS, examples cont'd

• Mandell,McIlraith,2003
• approach to integrate SemWeb technologies
  (particularly OWL-S) on top of BPEL4WSBPWS4J to
  enable dynamic binding in BPEL4WS
• First approach to integrate semantic mismatches
• Mentions prototype at http//ksl.stanford.edu/sds
  however not (yet?) provided
• In principle only discovery for dynamic binding,
  but propose a simple recursive search algorithm
  similar to planning for creating chains of
  missing services.
• Heflin,Muñoz-Avila,2002
• Use HTN planning for information gathering
  exploiting LCW information.
• Tackles the problem of unbounded search by trying
  to exploit LCW information
• Not really "planning for Web Services", but
  interesting application of planning techniques
  for SemWeb information gathering.
• Thakkar,Knoblock,Ambite,2003
• primarily on information discovery, extraction
  and integration,
• Query planning for information gathering. Not
  really comparable with classical planning but
  uses similar techniques, services described in
  terms of LAV views, modified inverse rule
  algorithm which allows to consider binding
  patterns and optimize wrt. LCW information.
• Most likely this list is incomplete

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Open Questions Challenges

• compensation in case of failure and dynamic
  replanning not yet tackled
• scaling not proven in real scenarios, large
  testbeds missing (Approach in this direction
  Constantinescu et al.,2004)
• Collaboratively resolving complex communication
  interfaces? (e.g. in the Pistore et al.,2004
  approach.)
• Conjecture only if you size down the number of
  possible services beforehand by intelligent
  discovery mechanisms (see next slide)

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Open Questions Challenges cont'd

• fully automated vs. semi-automated (approximation
  might help for the latter)
• Interleaving planning and execution
• assuming the same context at plan time than et
  execution time might cause problems
• planning neglects exogeneous events
• preconditions vs. outputs.
• Selection step from a set of outputs instead of
  conditions on all outputs (e.g. picking a flight)
• Maybe no negative effects, but non-deterministic
  effects!
• As opposed to planning often many services
  available which basically offer the same, but at
  different costs. This is not the case for the
  usual planning examples.
• Synthesis is ideally less frequent than access
  So, composite services, if stored have special
  necessities on persistence of the service,
  availability needs to be checked and/or HOW LONG
  are the descriptions valid?
• dynamic object generation web services create
  new objects "at run-time", It is unlikely that
  this can be adequately modeled with the current
  planning techniques.
• How to connect SWS and real services?
• Partly own ideas, McIlraith,Son,2002,
  Srivatava,Koehler,2003

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Relations With WSMO

• Current focus in Discovery, define different
  levels of abstraction for discovery WSMO D5.1
  different levels of abstraction shall filter the
  number of relevant (approximately matching)
  services before detailed checking, similar
  applies to composition
• WSMO preconditions/postconditions/effects
  involve complex FOL formulae WSML dialects WSML
  D16.x, WSML D20.x restrictions which allow
  decidable logical reasoning
• DL style
• LP style
• etc.
• Discovery is to be solved before we can compose
  and execute SWS!
• Grounding, Orchestration and Choreography under
  development. Need to have a clear formal
  semantics in order to enable (semi-)automatic
  composition and execution/verification of
  composed services (which e.g. BPEL4WS, OWL-S do
  not provide (yet?)).

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Discovery in WSMO

• What does Service Discovery mean in terms of
  WSMO?
• 1) Match goals against WS capabilities
• 2) Enable Executability (semi-automatically)
  WSMX!
• Aim
• More accurate discovery by semantic annotiations!

Discovery
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DiscoveryGoal-Capability Matching

• Different approaches
• Level 1 Keyword-based search (similar UDDI)
• extract keywords intelligently from WSMO
  descriptions
• Level 2 Using controlled vocabulary and
  conceptual
• descriptions of goals and capabilities, using
  goal ontologies!
• Level 3 Logical Level Reasoning on the
  declarative
• descriptions of goals and capabilities!
• Necessity for matching Different levels of
  abstraction within the description are necessary
  on different levels of discovery!
• Further abstraction looses information but
• Decrease complexity
• ? Trade-Off! Semi-automatic!
• Further steps contracting, etc. see Kifer etal.
  2004

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Key aspect Goal discovery vs.Service Discovery
Abstracted Service
Abstracted goal
matching
Goal Discovery
Service discovery
Service
Goal Input
Figure 1
The three major processes of heuristic
classification.

• In first place
• - Services have to provide the abstract
  descriptions to
• be discovered, annotation tool support
  necessary!)
• The user can subscribe to a defined goal (tool
  support, goal browser necessary!)
• For discovery abstract from concrete input
• ? Semi-automatic!

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Level 2

• Could be a simple hierarchy of action-object
  pairs
• Can be conceptual description od goals and
  capabilities

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Level3 Logical Descriptions of Services

• Currently as general as possible FOL
• (e.g. in the SWF Project FOL reasoner used for
  goal
• resolution)
• Different restrictions which allow decidable
  logical reasoning
• DL style
• LP style
• etc.

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WSMO Logical Descriptions of Services, by example

• just an example of what we want to express.
• - This simple example does not include
  terminological mismatches yet.
• We are interested in use case requirements input
  from this project here
• to find useful restrictions to allow
• decidable reasoning, etc.

Conjecture what we need is something in between
theconceptual matching And rules describing
input/output relation
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References

• Cimatti,E.Guinchiglia,F.Giunchiglia,Traverso,1997
  Planning via Model-Checking A Decision
  Procedure for AR, ECP-97.
• Constantinescu,Faltings,Binder,2004 Large scale
  testbed for type compatible service composition,
  ICAPS Workshop on Planning for Web Services, 2004
• Erol,Hendler,Nau,94 UMCP A Sound and Complete
  procedure for Hierarchical Task-Network Planning,
  AIPS 1994.
• Goldman,Boddy,96 Expressive Planning and
  Explicit Knowledge. AIPS 1996.
• Jensen,Veloso,Bowling,2001 OBBD-Based
  optimistic and strong cyclic adversarial
  planning, ECP-01.
• Heflin,Muñoz-Avila,2002 LCW-Based Agent
  Planning for the Semantic Web, AAAI, 2002.
• McDermott,2002 Estimated-Regression Planning
  for Interactions with Web Services.
• McIlraith,Son,2002 Adapting Golog for
  Composition of Semantic Web Services, KR 2002.
• Peot,Smith,92 Conditional Nonlinear Planning.
  AIPS 1992.
• Pistore,Barbon,Bertoli,Traverso,2004 Planning
  and Monitoring Web Service Composition, ICAPS
  Workshop on Planning for Web Services, 2004.
• Srivatava,Koehler,2003 Web Service Composition
  - Current Solutions and Open Problems. ICAPS 2003
  Workshop on Planning for Web Services
• Knoblock,Ambite,2004 Tutorial Planning on the
  Web, ICAPS 2004, con be found on C.Knoblock's
  Webpage.
• Thakkar,Knoblock,Ambite,2003 A view Integration
  Approach to Dynamic Composition of Web Services,
  ICAPS 2003 Workshop on Planning for Web Services.
• Wu,Parsia,Sirin, Hendler,Nau,2003 Automating
  DAML-S Web Services Composition using SHOP-2,
  ISWC 2003.
• WSMO D5.1 Discovery in WSMO. Draft available at
  http//www.wsmo.org
• WSML D16.x WSML.Drafts available at
  http//www.wsmo.org
• WSML D20.x OWL Lite-, OWL Flight. Drafts
  available at http//www.wsmo.org