1. Knowledge Engineering for Automated Planning
Lee McCluskey
With acknowledgement to
Ron Simpson

2. Abstract
Algorithms for automated planning have become significantly more powerful in recent years, but the industrial take up of the technology has been slow. One reason for this has been the difficulty of modelling problem areas with sufficient rigour to allow for their automated solution.
The tutorial will provide attendees with
an insight into the range of potential applications of the technology
an idea of the level of difficulty in deploying the technology
Information on where to find out more and how to obtain free software
Computer Science Research Centre
University of Huddersfield

3. Abstract The tutorial will cover
a brief introduction to automated planning from an AI perspective (‘AI Planning’)
a review of the scope and power of the currently available automated planning systems.
the languages used for domain problem specification and the associated knowledge engineering issues.
the development of example domain models using dedicated knowledge engineering tools in GIPO.
practical demonstrations!
Computer Science Research Centre
University of Huddersfield

4. Introduction Computer Science Research Centre
University of Huddersfield

5. Introduction: Resources
Mainly because of PLANET (EC F5 NoE in Planning) there are heaps of resources to back up this tutorial:
Applications of AI Planning website:
Free Planners, Schedulers and Domain Models:
http//scom.hud.ac.uk/planet/repository/
General PLANET website containing Summer School Notes on many aspects of AI Planning, and Planning Curriculum
http//
Computer Science Research Centre
University of Huddersfield

6. Introduction: Resources
Free downloadable software – GIPO - to engineer planning domain models
KE for Planning ROADMAP
The notes will be on my website
Computer Science Research Centre
University of Huddersfield

7. Introduction: What is AI Planning?
The scope of ‘AI Planning’ is the synthesis (generation) and execution of PLANS. That is, AI Planners reason with actions and generate plans.
Scheduling is the allocation of resources to plans of action taking into account various constraints – generally considered an ‘easier’ subpart of the Planning process
Planning is considered more of a knowledge-based pursuit than scheduling
Computer Science Research Centre
University of Huddersfield

8. Introduction: MAIN ASSUMPTION
The MAIN ASSUMPTION of virtually all work in AI Planning is that there should be a logical separation between
The Planning Engine
The Domain Model
Planning System
Planning
Engine
APPLICATION
DOMAIN
Domain
Model
Computer Science Research Centre
University of Huddersfield

9. Introduction Implications of assumption:
It tends to made AI Planning a different subject to eg ‘planning and scheduling’ in manufacturing
planning engines AND domain models can be developed, tested, debugged, validated independently - the ‘model’ of the particular application of planning is developed in relative isolation to the planning engine
domain models may be useful for more purposes that simply automated planning functions
Computer Science Research Centre
University of Huddersfield

10. Introduction Negative consequences of assumption ..
Domain model representation is influenced by ‘what planners an handle’
Consequent inefficiency in planning systems - akin to ‘compilation’ rather than ‘hand coding’ in software
Much research work has been carried out in developing planning engines, pushed along by the International Planning Competition (AIPS’98, AIPS’00, AIPS’02, ICAPS’04) BUT methods and tools to help develop the domain models have had relatively little attention
Computer Science Research Centre
University of Huddersfield

11. Introduction– Applications of AI Planning
See web site
Aerospace e.g.
Autonomous control of spacecraft - the NASA Remote Agent experiment
‘Vicar’ planner applied to automated image processing
Satellite mission planning and scheduling (European Meteostat)
Planning and Scheduling of Spacecraft Assembly
Computer Science Research Centre
University of Huddersfield

12. Introduction– Applications of AI Planning
Military e.g.
Planning for military air campaigns
DARPA/Rome Planning Initiative - A military scheduling project
Computer Science Research Centre
University of Huddersfield

13. Introduction– Applications of AI Planning
See web site
Industrial / Government / Control e.g.
Planning in a forest fire simulation system -
.. + disaster recovery in general
Brewery production-line scheduling
Generation of control programmes for industrial plant
Oil Spill Response Planning
Ship Building using shipyard scheduling optimisation systems
Aircraft crew scheduling
Chemical Plant control
Computer Science Research Centre
University of Huddersfield

14. Introduction– Applications of AI Planning
Other Current and Future Application areas
Other areas in ‘Control’:
Workflow/Workforce
Air Traffic
Project planning
Transport Logistics
Web Agents,
Games Software
Physical Robots, Autonomous Vehicles
Computer Science Research Centre
University of Huddersfield

15. Basic Concepts in AI Planning
Computer Science Research Centre
University of Huddersfield

16. Basic Concepts in AI Planning
Basic concepts in AI Planning are:
Operators, Objects, States, Goals, Planning Problem, Plans
To illustrate them we will use a famous benchmark domain – the ‘change tyre’ world
Computer Science Research Centre
University of Huddersfield

17. Basic Concepts in AI Planning
A state represents a world or snapshot within the domain. Objects have States – the conjunction of all objects’ states make up a world state
[wrench_in(wrench1,boot)]
[wheel_in(wheel1,boot),pumped_up(wheel1)]
[wheel_on(wheel2,hub1),flat(wheel2)]
[pump_in(pump1,boot)]
[tight(nuts1,hub0)]
[have_jack(jack1)]
[on_ground(hub1),fastened(hub1)]
[closed(boot)]
Computer Science Research Centre
University of Huddersfield

18. Basic Concepts in AI Planning
An operator represents an action within the application. Operators change state.
operator(putaway_wheel(C,W),
% prevail
[ se(container,C,[open(C)])],
% necessary
[ sc(wheel,W,[have_wheel(W)]=>[wheel_in(W,C)])],
% conditional
[])
A Domain model consists of a set of operators …
Computer Science Research Centre
University of Huddersfield

19. Basic Concepts in AI Planning
Goals are conditions on states
wheel_on(wheel1,hub1)
Computer Science Research Centre
University of Huddersfield

20. Basic Concepts in AI Planning
A Planning Problem is a triplet
(Initial World State, Goal, Domain Model)
Plans are collections of (instantiated, ordered) operators that solve planning problems
Computer Science Research Centre
University of Huddersfield

21. Basic Concepts in AI Planning
Other concepts in AI Planning are:
Tasks, Events, Resources, Time
- Tasks are compound actions to be executed (eg ‘make a cup of tea’)
Events change the states of objects but happen exactly when their pre-conditions are true
Resources are quantities used up by actions - usually represented by numeric variables
Computer Science Research Centre
University of Huddersfield

22. Basic Concepts in AI Planning
How do AI planners generate plans?
… well there are MANY techniques ..
Genarally they SEARCH through some representation of the planning problem!
Computer Science Research Centre
University of Huddersfield

23. Current State of AI Planning
Computer Science Research Centre
University of Huddersfield

24. Current State of AI Planning
Research in AI planning has produced very good results in the last 10 – 15 years – largely due to…
The International Planning Competition
Acceptance of a ‘standard’ communication language called PDDL for domain models and domain problems
More ‘industrial’ involvement – NASA + US military
Computer Science Research Centre
University of Huddersfield

25. OLD State of AI Planning
In the 80’s / early 90’s.. Most planners could solve simple problems with domain models consisting of
Actions modelled as instantaneous, deterministic operators with infinite resources.
Action’s pre-conditions and effects were propositions
States = set of propositions under the CWA
Goal = set of propositions.
Metrics for planners
time to solve problem – ie generate plan
size (no of operators) in sequential plan
Computer Science Research Centre
University of Huddersfield

26. Current State of AI Planning
Now: Planners can solve more complex problems with domain models consisting of ..
Durative operators – time is explicit
Resources
Non-deterministic operators
Operators with complex/conditional effects
Partially observed states
More metrics considered e.g. makespan and multi-objective achievement
MOST IMPORTANT: they are downloadable!
Computer Science Research Centre
University of Huddersfield

27. Current State of AI Planning
PDDL is the common communication language. Main variants of PDDL..
PDDL – first IPC
Added Duration and Numerical Quatities
PDDL 2.1
Added timed initial facts, derived predicates
PDDL 2.2
PDDL+
Added Processes,
Events, cts time
Computer Science Research Centre
University of Huddersfield

28. Current State of AI Planning
Now many very effective techniques in use in plan generation..
(see http//scom.hud.ac.uk/planet/repository/ )
Generate and search through a plan graph (Graphplan, STAN)
Do best-first, forward, state space search with very good weak heuristics (FF, HSP)
Compile planning problem into a large set of clauses and solve with a satisfiability engine (Blackbox)
Compile planning problem into a compact storage form such as Binary Decision Diagrams (MIPS)
Computer Science Research Centre
University of Huddersfield

29. Current State of AI Planning
Summary - Good News:
Plan generation algorithms are much more efficient than 10 years ago, and can work efficiently in more expressive problems domains
But there is Bad News:
Technology transfer: there is much to do in making the technology generally available and usable
Model development: domain model authors use planners themselves to try to develop and debug a domain model. Planners have not generally been designed for this purpose..
Computer Science Research Centre
University of Huddersfield

30. Knowledge Engineering and Domain Model Capture
Computer Science Research Centre
University of Huddersfield

31. Knowledge Engineering
Knowledge Acquisition / Engineering is a huge area in AI related to Knowledge-based Systems (KBS)
Old idea of 'knowledge transfer', where constructing a KBS amounted to extracting the knowledge from experts and encoding it within an expert system 'shell‘ (20 years ago!)
Application
expertise
transfer
Procedural expert knowledge
Computer Science Research Centre
University of Huddersfield

32. Knowledge Engineering
Now KBS emphasises the building of a deep causal model prior to an operational system.
This ‘domain’ model has to embody not just the procedural expert knowledge but the environment in which this knowledge was utilised.
Several modelling frameworks have been developed (e.g. CommonKads which is based on the use of a series of models during domain capture, each dealing with different aspects of the domain.)
These support the process of model acquisition and validation, and are underpinned by an overall method of development.
Computer Science Research Centre
University of Huddersfield

33. Knowledge Engineering for AI Planning: Definition
Knowledge Engineering (KE) in AI Planning is the process that deals with
acquisition,validation and maintenance of planning domain models, and
the selection and optimization of appropriate planning machinery to work on them.
Hence, knowledge engineering processes support the planning process – they comprise all of the off-line, knowledge-based aspects of planning that are to do with the application being built.
(definition of Roadmap –
Computer Science Research Centre
University of Huddersfield

34. Knowledge Engineering for AI Planning
Computer Science Research Centre
University of Huddersfield

35. Knowledge Engineering for AI Planning
KE for KBS Is generally not the same as KE for planning..
The knowledge elicited in planning is largely knowledge about actions and how objects are effected by actions. This knowledge has to be adequate to allow efficient automated reasoning and plan construction.
The ultimate use of the planning domain model is to be part of a system involved in the ``synthetic'' task of plan construction (not for solving diagnostic or classification problems as in typical KBS)
Computer Science Research Centre
University of Huddersfield

36. Knowledge Engineering for AI Planning: Terminology
Domain is the application area
Domain model is a formal model (theory) of the application area
Acquisition is the process of producing a domain model of the application area
Modelling is the area of using the model to predict behaviour in the application area
Computer Science Research Centre
University of Huddersfield

37. Knowledge Engineering for AI Planning: Terminology
Symbolic World
DOMAIN = APPLICATION AREA
Acquisition
Domain Model
Domain Model Language
Predict
Modelling
Computer Science Research Centre
University of Huddersfield

38. Knowledge Engineering for AI Planning: Validation
Validation of a model is the process that promotes its quality in terms of internal and external criteria by the identification and removal of errors in the model.
Internal criteria includes properties such as syntactic correctness and logical consistency; in general these properties can be proved formally and are not too problematic.
External criteria includes properties such as accuracy, correctness and completeness. Given that the sources of the model will not often be a mathematical object, these properties can never be proved correct (in the same sense that a requirements specification can never be proved correct).
Note the distinction between validation of a domain model and validation of a planning system. The former supports the latter, and occurs at a much earlier stage in system development.
Computer Science Research Centre
University of Huddersfield

39. Domain Model Languages for AI Planning
A DML should:
Be associated with a method.
Be tool supported
Be expressive and customizable
Support the operational aspects of the model
Have a clear syntax and semantics
Be structured
Computer Science Research Centre
University of Huddersfield

40. PDDL
PDDL is the ‘standard’ communication language for domain models but…
has no associated method for building models
has little ‘structure’ for helping in model building
has little in the way of static tools to help in de-bugging
Computer Science Research Centre
University of Huddersfield

41. OCLh
OCLh is a language developed precisely for helping with the BUILDING of domain models
It is similar in some respects to PDDL but-
It has structure using object classes and state abstractions
It has a tools environment called GIPO which supports a model building method
Computer Science Research Centre
University of Huddersfield

42. Planning Domain Engineering with GIPO
Computer Science Research Centre
University of Huddersfield

43. GIPO - rationale
Planning Domain Models are hard to design, write, debug, maintain - even for experts. The process of encoding is laborious. Bugs are of various types can lurk in models for a long time.
As planners and planning applications become larger, the problems of engineering planning domain models become more acute. There is a need to research into engineering environments and explore their synergy with general purpose planners.
Application
Domain Model
Acquisition is
Very hard!!
Computer Science Research Centre
University of Huddersfield

44. GIPO - rationale The two main planning systems used in anger are:
O-Plan (Edinburgh University)
SIPE (Stanford Research Institute)
Both have very expressive domain models languages.
To make an application efficient, the user must encode appropriate heuristics.
To be able to use them one has to be a planning expert, modelling expert and an application expert.
Even then, developing models is a painstaking process.
Computer Science Research Centre
University of Huddersfield

45. GIPO – what is it?
GIPO (Graphical Interface for Planning with Objects) is an experimental GUI and tools environment for building planning domain models.
It is written mainly in Java, with some embedded tools in Prolog, and is under continuous development.
It is a product of PLANFORM, a UK EPSRC-funded research project, written at The University of Huddersfield UK. Website:
Our long term aim is make the technology more usable and available!
Computer Science Research Centre
University of Huddersfield

46. GIPO – versions GIPO 1.1 Generally available
For ‘Flat’ models (ECP’01)
GIPO+
GIPO 1.2
Not on release
For models with
cts time, events
and processes
(PlanSig’03)
Not on release
Incorporating
automated
induction of
Operators (AIPS’02)
GIPO 2
Generally available
For hierarchical models
(ICAPS’03)
Computer Science Research Centre
University of Huddersfield

47. GIPO -functions
GIPO allows a user to create new domain models or import and change old ones via a GUI. It features
on-line tutorial and OCL manual
Tools for initial model acquisition
Tools for model validation
Planning engines
3rd party Planning engines can be easily ‘bolted on’ to GIPO 1 as it outputs PDDL 1.2, and can accept generated plans from them.
Computer Science Research Centre
University of Huddersfield

48. GIPO – main tools
syntax and semantic checks for individual components, and between components, of a model
From: Trivial checks on names and sorts
To: complex check’s on the structure of hierarchically defined operators
a plan stepper
a plan animator
a random task generator (GIPO 1 only)
an operator induction method (GIPO 1.2 only)
Computer Science Research Centre
University of Huddersfield

49. GIPO – simple method Identify objects and object classes (sorts)
Define predicates
Define typical object states
Define operators
Debug and validate using plan stepper, planner and animator
Method in more detail was given in:
T.L. McCluskey and J.M. Porteous Engineering and Compiling Planning Domain Models to Promote Validity and Efficiency. Artificial Intelligence Vol. 95(1), pages , 1997.
Computer Science Research Centre
University of Huddersfield

50. Future releases – GIPO+
GIPO+ supports models that contain
Actions
Events
Processes
Continuously varying values (Time)
Computer Science Research Centre
University of Huddersfield

51. GIPO+: Air Traffic Control Example
All Symbols are ‘clickable’ and give object information
plane flying through a block represented by a process
Computer Science Research Centre
University of Huddersfield

52. Air Traffic Control Example
Safety Violation
Event
Computer Science Research Centre
University of Huddersfield

53. Conclusion AI Planning is a maturing technology
AI Planning technology needs to made more usable and available
The main assumption in AI Planning is that there should be a logical separation between the planning engine and the domain model. Particularly important is the engineering of the domain model – if this has bugs then the application is doomed
GIPO is an experimental GUI for building and validating planning domain models. It is a first step in making planning technology more usable.
Computer Science Research Centre
University of Huddersfield