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Htn Planning In A Tool Supported
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Htn Planning In A Tool Supported



Htn Planning In A Tool Supported

Htn Planning In A Tool Supported



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Htn Planning In A Tool Supported Htn Planning In A Tool Supported Presentation Transcript

  • GIPO II: HTN Planning in a Tool-supported Knowledge Engineering Environment
    • Lee McCluskey
    • Donghong Liu
    • Ron Simpson
    • Department of Computing and
    • Mathematical Sciences,
    • The University of Huddersfield
  • Summary
    • In this paper/talk, we postulate a “domain independent” method supported by
      • static/dynamic tools
      • dual structuring of the model via object hierarchies and HTN operators
    • to acquire, develop and validate a hierarchical planning domain model .
  • Contents
    • Contents:
    • Background+ Development Method
    • Modelling
    • The Transparency Tool
    • The HyHTN planner / algorithm
    • Conclusions
  • 1`. Background + Development Method
    • Our main interest is in Knowledge Engineering - the acquisition, validation and modelling of AI Planning knowledge.
    • We are creating experimental research platforms for investigating
      • the integration of planning tools
      • methods of planning knowledge acquisition
  • Advertisements
    • GIPO-I and GIPO-II software can be obtained freely for Linux, Solaris and Windows via our website:
    • http://scom.hud.ac.uk/planform/gipo/
    • There is a demonstration of GIPO II at the Demo session later on this evening
    • Also please note that there is a NEW comprehensive web site for planners and schedulers, planning tools, domain models - on
    • http://scom.hud.ac.uk/planet/repository/
    • sponsored by the EU PLANET Network
  • Recap on GIPO (related papers in ECP’01, ISMIS’02,AIPS’02) :
    • An ‘open’ tools environment supporting domain acquisition and domain modelling integrated via a GUI around the Object Centred language OCL h and associated method.
    • Our approach has been to stick to a ‘classical’ planning foundation and concentrate on the integration/acquisition/modelling aspects
  • GIPO - I :
    • GIPO version 1 contains within a GUI:
      • domain model editors
      • static analysis checking tools
      • a plan stepper
      • an exporter to PDDL
      • planners + API for 3rd party planners
      • a solution animator
      • a random task generator
      • (new in ‘02) an induction tool to help in operator acquisition
  • Domain Model Development with GIPO-II : Method Outline
    • GIPO-II is designed for writing HTN models. GIPO II contains
      • domain model editors
      • basic static analysis checking tools
      • is object class hierarchy consistent?
      • do object state descriptions satisfy invariants?
      • are predicate structures and operator schema mutually consistent?
      • are task specifications consistent with the domain model?
      • a plan stepper
      • a solution visualiser
      • PLUS:
      • Transparency Tool + HyHTN Planner
      • TO ADD:
      • Induction and Pattern tools
    Acquisition of Objects/ Object State Behaviour -use GIPO-II GUI or (next release) use generic paterns Operator Acquisition Using GIPO-II GUI or (next release) induction Static Analysis Tools Static Analysis including Transparency Tool Solving simple tasks using The Plan Stepper Solving more complex Tasks with HyHTN Solution visualier
  • 2. Modelling
    • GIPO’s modelling based on the idea of engineering a planning domain so that the universe of potential states of objects are defined first:
    • objects grouped within classes under a class hierarchy.
    • Each class in the hierarchy may have a ``behaviour'' in the sense that objects of that class have changeable properties and relations.
    • An object may inherit behaviour from each class above it in the hierarchy.
    • This proceeds before operator definition and makes it possible to accurately induce operator schema (as shown in our AIPS’02 paper..)
    GIPO Example from a TransLog Domain
  • Object hierarchy example physical_obj package vehicle railv train traincar waiting certified delivered moveable available attached unattached at
  • Operator representation
    • The basis of both primitive and hierarchical operators are transitions of objects LHS => RHS
    • Depending on how precisely specified the LHS/RHS of transitions are, this abstraction uniformly encompasses goal conditions, pre-conditions, necessary and conditional effects, deterministic and non-deterministic actions..
  • Method Representation
    • Primitive operators are composed of transitions and constraints. But hierarchical domain models contain methods - HTN-like operators
    • Methods are defined using
      • transitions
      • constraints
      • a task network (of methods and achieve goals)
  • Task Representation
    • Tasks are defined using
      • an initial state
      • a task network (of methods and achieve goals)
      • constraints on variables/orders in the task network
      • EXAMPLE (without the initial state data):
      • ( [ achieve(ss(traincar,traincar1,
      • [at(traincar1,city1-ts1)])),
      • transport(pk-5-z,city3-cl1-z,city2-cl1),
      • achieve(ss(package,pk-5,
      • [at(pk-5,X),delivered(pk-5)] )) ],
      • [before(1,3)],
      • [serves(X,city3-x)] )
  • 3. The Transparency Tool
      • A Method’s transitions can be viewed as (or translated to) pre- and post conditions for that method’s task network
    • For Example:
  • The Transparency Property
      • Methods are regulated by the semantic property of transparency -- this ensures they are structured in a coherent manner.
    • A method is sound if its task network necessarily achieves its POST-condition with respect to the objects in the method’s transitions.
    • The transparency property is then as follows:
    • A method m is transparent if it and every expansion of m, consistent with its static constraints, is sound.
  • The Transparency Tool
      • The tool is executed after each method has been created or updated.
    • An error shows that the method’s task network cannot perform the method’s transitions.
    • Its use can uncover subtle errors in methods .. But it cannot spot all errors!
  • 4. The HyHTN planner / algorithm
    • A m ethods’ task networks can be composed of all achieve goals, all methods, or somewhere in between. The idea is to use HyHTN at any point in this development space.
    • It inputs an OCL h domain model and task as shown above.
    • It outputs a plan that is input to the plan visualiser , displaying the decompositions making up the solution to the task.
  • HyHTN Algorithm:
    • HYHTN is a forward state advancing HTN planner. This has the advantage that heuristic state-space search can be used to establish `achieve-goal' conditions .
    • Thus the performance of eg SHOP-like algorithms in HTN planning, and the performance of fast forward algorithms in pre-condition planning have been combined into a flexible, efficient hybrid system.
    • Also the transparency property reduces the possibility of choosing methods that lead to dead - ends, as every task network decomposition that satisfies its static constraints is guaranteed to achieve its post-conditions.
  • Experiments with HyHTN
    • In the paper we include a comparison of HyHTN vs SHOP using the Translog Domain with object classes such as cities, regions, packages, trucks, trains, planes, cranes, ramps etc.
    • The domain model contains 34 parameterised methods and 58 parameterised primitive operator structures. The SHOP model is of a similar size.
    • The specific problems concern the transport of up to 10 packages, with 5 connected cities, 15 locations, 15 cranes to maintain one crane at each location, and 11 trucks in one location in the initial state.The packages were of different types: bulky, liquid, granular, and mail.
  • 5. Summary
    • We have introduced a method which includes two powerful tools for HTN planning and domain development:
    • Transparency – to ensure that hierarchical operators do what their transitions say they do. This is static validation.
    • HyHTN – a planner that can be used to help develop hierarchical operators in that initially it can be run with `achieve goals’ and these can be replaced by canned plans as the domain model is refined.
  • 6. Future
    • GIPO-II is still only usable by experts… we want to make it more accessible :-
    • We intend to incorporate the transparency property into an induction and theory revision tool we are creating for inducing HTN models from examples.
    • We intend to transfer GIPO into a Web Service and eventually make it into an autonomous knowledge acquisition agent for planning problems.