1. ICAPS Summer School June 2006 Knowledge Engineering for Automated Planning Lee McCluskey, Dept of Informatics, University of Huddersfiield, UK

2. ICAPS Summer School June 2006Informatics Research Group University of Huddersfield Abstract -1 Domain-independent planners rely on the input of formal, accurate and complete domain ‘descriptions’ before they can be used. …If a description contain bugs, or fails to contain relevant knowledge, then the planner will give incorrect results or no results are all. “If your PhD thesis is vacuous, turn it all into Predicate Calculus…” etc

3. ICAPS Summer School June 2006Informatics Research Group University of Huddersfield Abstract -2 But how can domain ‘descriptions’ be accurately and efficiently built up? Is it possible for descriptions to be arrived at by non-planning-experts, or by automated means? This tutorial is concerned with the acquisition, formulation, validation and maintenance of planning domain descriptions.

4. ICAPS Summer School June 2006Informatics Research Group University of Huddersfield Abstract -2 We will scratch the surface of: the general area of knowledge engineering (KE), the area of KE for AI Planning, existing KE tools for planning, including some entered in the ICAPS-05 KE for planning competition. We will also do some practical work with GIPO, a tools environment used as a research tool for exploring knowledge formulation methods for classical planners.

5. ICAPS Summer School June 2006Informatics Research Group University of Huddersfield Introduction

6. ICAPS Summer School June 2006Informatics Research Group University of Huddersfield A I Planning and Scheduling has moved on in the last 10 – 20 years… B A A B THEN NOW

7. ICAPS Summer School June 2006Informatics Research Group University of Huddersfield Introduction: MAIN ASSUMPTION The scope of ‘AI Planning’ is the synthesis / generation and execution of PLANS. 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 Conceptualisation Of APPLICATION DOMAIN Domain Model - A rich Ontology Planning Engine Planning System

8. ICAPS Summer School June 2006Informatics Research Group University of Huddersfield Introduction Some implications of this assumption: 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 than simply automated planning functions Planning engines can be re-used It tends to made AI Planning very different from applied areas such as planning and scheduling in manufacturing

9. ICAPS Summer School June 2006Informatics Research Group University of Huddersfield Introduction Some negative consequences of assumption.. How and what knowledge is encoded is influenced by ‘what planners can handle’ Potentially we could have gross inefficiencies in systems akin to ‘compilation’ rather than ‘hand coding’ in software. Systems hand coded for particular applications are likely to be much more efficient.

10. ICAPS Summer School June 2006Informatics Research Group University of Huddersfield Introduction Much 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 A B B A Conceptualisation ?

11. ICAPS Summer School June 2006Informatics Research Group University of Huddersfield Basic Concepts in Knowledge Engineering

12. ICAPS Summer School June 2006Informatics Research Group University of Huddersfield Knowledge Engineering – a holistic definition In most general terms, Knowledge Engineering is the engineering of (those parts of) a system which contains and reasons with some explicit representation of knowledge OR (for fans of sub-symbolic AI) the engineering of (those parts of) a system whose behaviour indicates that it contains and reasons with knowledge.

13. ICAPS Summer School June 2006Informatics Research Group University of Huddersfield Knowledge Engineering – sum-of-parts definition KE contains sub-processes such as Acquisition – the process of finding and collecting adequate amounts of relevant knowledge, conceptualising it, and formulating it into knowledge structures in a KBS Verification and Validation – validation: are we building the right product? verification: are we building the product right? These processes take the form of identifying and removing bugs in the KBS either because knowledge is inaccurate/incorrect (validation) or knowledge transformations are incorrect (verification) Maintenance – updating and incrementally debugging a KBS

14. ICAPS Summer School June 2006Informatics Research Group University of Huddersfield Knowledge Engineering – historical angle Knowledge Acquisition / Engineering is a huge area in AI related to Knowledge-based Systems (KBS) Initially KA was focused on the idea of 'knowledge transfer', where constructing a KBS amounted to extracting the ‘knowledge’ from experts and encoding it within an expert system 'shell‘ (20 – 30 years ago..) Application expertise Knowledge transfer Procedural expert knowledge Expert System

15. ICAPS Summer School June 2006Informatics Research Group University of Huddersfield Knowledge Engineering – historical angle The old ideas of KA lead to the famous knowledge acquisition ‘bottleneck’ of the 70’s/80’s Expert System Knowledge brittle shallow

16. ICAPS Summer School June 2006Informatics Research Group University of Huddersfield Knowledge Engineering – current wisdon: building deep, reusable models Now KBS wisdom emphasises the building of –a deep causal model prior to the building of an operational system. This domain model has to embody not just the procedural expert knowledge but enough environmental and ‘first principle’ knowledge to justify the procedural knowledge. –Re-usable, globally exchangeable knowledge structures such as ontologies, so that KBS need not be constructed from scratch Rules Causal model Globally Accessible Ontologies

17. ICAPS Summer School June 2006Informatics Research Group University of Huddersfield Knowledge Engineering – Methodological and Tool Support In the last 20 yrs modelling frameworks with tool support have been developed – most notably CommonKads “Knowledge has come to be recognized and handled as a valuable entity in itself.” “there is a strong need to convert the art and craft of knowledge engineering into a real scientific discipline” Quotes from the web site: http://www.commonkads.uva.nl/ Tools such as this support the process of knowledge acquisition and validation, and are underpinned by an overall method of development. CommonKads advocates the use of a series of models during domain capture, each dealing with different aspects of the domain.

18. ICAPS Summer School June 2006Informatics Research Group University of Huddersfield Knowledge Engineering – increase in scope In the last 10 yrs KE is now much broader in scope than simply crafting knowledge for expert systems. This is because of the rise of The Knowledge-Driven Economy The main artifact emerging from this is the “ontology”. KE now encompasses ‘ontological engineering’ and ontology building tools such as Protégé have been built specifically to encode Ontologies in common forms of description logics. Knowledg e =

19. ICAPS Summer School June 2006Informatics Research Group University of Huddersfield Knowledge Engineering of the Semantic Web Inference Logic RDF XML Trust (part of) the Layer-Cake Model of the Semantic Web The Semantic Web – making internet resources globally understandable by internet programs – demands that these ‘resources’ have to be acquired in an explicit form (like a knowledge base).

20. ICAPS Summer School June 2006Informatics Research Group University of Huddersfield My KE history: Aircraft Separation Criteria Shanwick Oceanic Area segment1 segment2

21. ICAPS Summer School June 2006Informatics Research Group University of Huddersfield And the formulation.. [(one_or_both_of Segment1 and Segment2 are_flown_at_subsonic_speed) & ( the_Aircraft_on(Segment1) and the_Aircraft_on(Segment2) meet_mnps) & ( the_Aircraft_on(Segment1) and the_Aircraft_on(Segment2) are_jets & (the_Profile_containing(Segment1) & the_Profile_containing(Segment2) are_wholly_or_partly_in_the_ mnps_airspace) ] => [(the_basic_min_longitudinal_sep _Val_in_mins_required_for Segment1 and Segment2) = 10 …. ETC the_basic_min_longitudinal_sep_Val_in_min s_required_for(Segment1,Segment2,10):- are_subject_to_oceanic_cpr(Segment1,Se gment2), both_are_flown_at_supersonic_speed(Seg ment1,Segment2), (both_are_flown_at_the_same_mach_number_i n_level_flight(Segment1,Segment2) ; the_Aircraft_on_segment(Segment1,Aircr aft1), the_Type_of(Aircraft1,Type1), the_Aircraft_on_segment(Segment2,Aircr aft2), the_Type_of(Aircraft2,Type2), Type1=Type2, are_cruise_climbed(Segment1,Segment2) ),.. ETC

22. ICAPS Summer School June 2006Informatics Research Group University of Huddersfield Planning Domain Engineering with GIPO

23. ICAPS Summer School June 2006Informatics Research Group University of Huddersfield 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: http://scom.hud.ac.uk/planformhttp://scom.hud.ac.uk/planform Our long term aim is make planning technology more usable and available!

24. ICAPS Summer School June 2006Informatics Research Group University of Huddersfield GIPO – versions GIPO 1Generally available For ‘Flat’ models (ECP’01) GIPO 2 + automated induction of action schema (AIPS’02) + HTN represntation (ICAPS’03) GIPO+ Not on release For models with cts time, events and processes (PlanSig’03) GIPO 3 + OLHE + Cts domains ICKEPS award (ICAPS’05)

25. ICAPS Summer School June 2006Informatics Research Group University of Huddersfield GIPO -functions GIPO allows a user to create new domain models, import and change old ones via a GUI, or store and re-use components. It features on-line tutorial and OCL manual Tools for initial model acquisition and re-use Tools for model validation Planning engines 3 rd party Planning engines can be ‘bolted on’ at the ‘GIPO 1’ level as it outputs PDDL v1.2, and can accept generated plans from them.

26. ICAPS Summer School June 2006Informatics Research Group University of Huddersfield GIPO – main tools Some are specific to a particular GIPO version syntax and semantic checks for individual components of a model. Semantic cross checking between components. Object Life History Editor an action schema induction tool a plan stepper a plan animator a random task generator built-in planners

27. ICAPS Summer School June 2006Informatics Research Group University of Huddersfield Language underlying GIPO: OCL h OCL h 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

28. ICAPS Summer School June 2006Informatics Research Group University of Huddersfield Demonstration Example: Lazy Hiking World Imagine Sue and Fred want to have a hiking holiday in the Lake District in North West England. They walk in one direction, and do one ``leg'' each day. But not being very fit, they use two cars to carry them / the tent / their luggage to the start/end of a leg. They must have their tent up already so they can sleep the night, before they set off again to do the next leg in the morning. Actions include walking, driving, moving and erecting tents, and sleeping. The requirement for the planner is to work out the logistics and generate plans for each day of the holiday. Helvelyn Fairfield Coniston

29. ICAPS Summer School June 2006Informatics Research Group University of Huddersfield Automated acquisition by induction (Opmaker) INPUTS: Offline: Partial domain spec, got via GIPO or other acquisition method (eg importing an ontology):- Objects, object classes, predicates, invariants Online: Training sequences, initial states, and user input. Example training sequence: Load tent1 sue keswick Get-in-car sue car1 keswick Drive sue car1 keswick helvelyn tent1 Unload tent1 sue car1 helvelyn Putup tent1 sue helvelyn ETC OUTPUTS: A set of Action Schema – one for each action name in training

30. ICAPS Summer School June 2006Informatics Research Group University of Huddersfield Knowledge Engineering in AI Planning

31. ICAPS Summer School June 2006Informatics Research Group University of Huddersfield My Definition Knowledge Engineering 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 (mainly off-line) knowledge- based aspects of planning that are to do with the application being built. (definition in EU PLANET Roadmap – http://scom.hud.ac.uk/planet/home/ )

32. ICAPS Summer School June 2006Informatics Research Group University of Huddersfield Knowledge Engineering for AI Planning: More Terminology Domain is the conceptualisation of the application area Domain model is a formal model (theory or ontology) of the conceptualisation 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 What about domain description, domain specification, domain definition…?

33. ICAPS Summer School June 2006Informatics Research Group University of Huddersfield Knowledge Engineering for AI Planning: Terminology DOMAIN = Conceptualisation of an APPLICATION AREA Symbolic World Domain Model Validation and Model Refinement Acquisition Domain Model Language

34. ICAPS Summer School June 2006Informatics Research Group University of Huddersfield 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. 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.

35. ICAPS Summer School June 2006Informatics Research Group University of Huddersfield Knowledge Engineering for AI Planning: Validation Validation Criteria –Internal criteria includes properties such as syntactic correctness and logical consistency; in general these properties can be proved formally and are not 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).

36. ICAPS Summer School June 2006Informatics Research Group University of Huddersfield Support for KE in AI Planning: the Domain Model Language When experts think about creating domain models/description, they immediate think of the DML/DDL eg PDDL. Languages should.. Be associated with a method. This will give a set of ordered steps to be carried out in order to capture the domain model, thus guiding the knowledge engineer throughout the process. It could contain activities such as modeling of state changes and the discharging of proof obligations. Be tool supported. These tools will support the steps in the method and, using the structure of the model language, be able to provide powerful support for statically validating, analyzing and operationalizing the model. Be expressive and customizable. The language needs to be generally applicable, yet customizable in some sense so that it ``fits'' well with applications. Since there is a whole range of assumptions involved in planning which may or may not hold in an application (\eg to do with uncertainty, resources, closed world) it may be that the modeling language will have ``variants'' to deal with different assumptions.

37. ICAPS Summer School June 2006Informatics Research Group University of Huddersfield Support for KE in AI Planning: the Domain Model Language Support the operational aspects of the model. The language's framework should include a set of properties and metrics which can be evaluated to assess a model's operationality and likely efficiency. It should be possible predict whether the model can be translated to an efficient application. Have a clear syntax and semantics. As a basis for the other aspects above, and for the analysis of models encoded in the language, it should be possible to map models to a ``meaning'' within some well- known formal system such as a modal logic. Be structured. It should provide mechanisms that allow complex actions, complex states and complex objects to be broken down into manageable and maintainable units. For example, the dynamic state of a planning application could be broken down into the dynamic state associated with each object. On this structure can then be hung ways of checking the model for internal consistency and completeness.

38. ICAPS Summer School June 2006Informatics Research Group University of Huddersfield Current State of AI Planning PDDL is the common communication language. Main variants of PDDL.. PDDL 1 1998 – first IPC PDDL 2.1 PDDL+ PDDL 2.2 2002 - Added Duration and Numerical Quatities 2002 - Added Processes, Events, cts time 2003 - Added timed initial facts, derived predicates

39. ICAPS Summer School June 2006Informatics Research Group University of Huddersfield PDDL PDDL is a family of ‘standard’ communication language for domain descriptions/models which is ‘extensible’ and ‘modular’ to some degree But PDDL … -has no associated method for building models -has little ‘structure’ for helping in model building -has little in the way of ‘standard’ static tools to help in de- bugging. They are some dynamic tools (eg VAL) Rather – designers use an environment for KE and then produce PDDL (machine code!) to interface with a planner

40. ICAPS Summer School June 2006Informatics Research Group University of Huddersfield 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 tend to use planners themselves to try to develop and debug a domain model. Planners have not generally been designed for this purpose

41. ICAPS Summer School June 2006Informatics Research Group University of Huddersfield Summary 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. Acquisition is Very hard!! Application Domain Model

42. ICAPS Summer School June 2006Informatics Research Group University of Huddersfield Brief Biased Survey of Knowledge Engineering for AI Planning: Tools and Systems

43. ICAPS Summer School June 2006Informatics Research Group University of Huddersfield Introduction There are few deployed AI Planning systems There are even fewer KE tools for Planning There are even fewer planner-independent KE tools for Planning

44. ICAPS Summer School June 2006Informatics Research Group University of Huddersfield Applications of AI Planning See web site http://vitalstatistix.nicve.salford.ac.uk/planet2/ Industrial / Government / Control e.g. Planning in a forest fire simulation system (PHOENIX, SIADEX).. + disaster recovery in general Brewery production-line scheduling (SIPE-2) Generation of control programmes for industrial plant - the MACHINE planning system Oil Spill Response Planning in SIPE-2 Ship Building using ARGOS shipyard scheduling optimisation system Aircraft crew scheduling Chemical Plant control

45. ICAPS Summer School June 2006Informatics Research Group University of Huddersfield Applications of AI Planning See web site http://vitalstatistix.nicve.salford.ac.uk/planet2/ Aerospace e.g. Satellite mission planning and scheduling (European Meteostat) Planning and Scheduling of Spacecraft Assembly Autonomous control of spacecraft - the NASA Remote Agent experiment ‘Vicar’ Planner applied to automated image processing Military e.g. Planning for military air campaigns - SIPE-2 DARPA/Rome Planning Initiative - A military scheduling project

46. ICAPS Summer School June 2006Informatics Research Group University of Huddersfield Applications of AI Planning Other Current and Future Application areas Web Agents, Games Software Physical Robots, Autonomous Vehicles Other area in ‘Control’: –Workflow/Workforce –Air Traffic –Project planning –Transport Logistics

47. ICAPS Summer School June 2006Informatics Research Group University of Huddersfield Deployed planning systems The two main ‘general’ domain-independent planning systems used in ‘real’ applications are: O-Plan / I-X (Edinburgh University) SIPE (Stanford Research Institute) Both have very expressive domain models languages, BUT - to make an application efficient, the user must encode appropriate heuristics -to be able to use them, one has to be a planning expert, a modelling expert and an application expert. Even then, developing domain models is a painstaking process.

48. ICAPS Summer School June 2006Informatics Research Group University of Huddersfield ICKEPS - aims The First International Competition on Knowledge Engineering for Planning and Scheduling was held at ICAPS last year… The aims of the Competition were to promote the knowledge-based and domain modelling aspects of P&S accelerate knowledge engineering research in AI P&S encourage the development and sharing of prototype tools or software platforms that promise more rapid, accessible, and effective ways to construct reliable and efficient P&S systems

49. ICAPS Summer School June 2006Informatics Research Group University of Huddersfield ICKEPS - systems Tailor: Helping end users modify procedures by instruction Jim Blythe and Varun Ratnakar Hamlet: Tool for automatically acquiring control knowledge for planning Daniel Borrajo, Susana Fernández, Raquel Fuentetaja, Juan D. Arias, Manuela Veloso PlanWorks: A debugging environment for constraint based planning systems Patrick Daley, Jeremy Frank, Michael Iatauro, Conor McGann, Will Taylor ModPlan: Knowledge acquisition and knowledge engineering in the ModPlan workbench Stefan Edelkamp and Tilman Mehler GIPO: A graphical interface for planning with objects Ron M. Simpson itSIMPLE: Tool for modelling planning domains Tiago Stegun Vaquero, Flavio Tonidandel, José Reinaldo Silva ARMS: Action-relation modelling system for learning action models Kangheng Wu, Qiang Yang, Yunfei Jiang

50. ICAPS Summer School June 2006Informatics Research Group University of Huddersfield Example ICKEPS tool set: Hamlet/PLTOOL Aim: mixed – initiative tool for creating and selecting control rules and planning heuristics Inputs: Training problems, user choice, quality metric Outputs: A selected set of control rules (macros) and heuristics Method: This system developed at UC3 Madrid is a collection of learning techniques hooked into planners. They analyse the solutions/search trees of the training examples and extract heuristics / macros / control rules from them

51. ICAPS Summer School June 2006Informatics Research Group University of Huddersfield Example ICKEPS tool - ARMS Aim: single tool to learn ACTION SCHEMA Inputs: many action sequences (action name + object parameters), no intermediate state information Outputs: action schema Method: a form of similarity-based learning. 1. Generalise all the object constants => variables 2. Generates frequency sets of variable constraints from - sequenced actions - from 1 st and last actions and initial/goal predicates - - general constraints on pre-post conditions predicates 3. Use a SAT solver to find the optimal action schemas

52. ICAPS Summer School June 2006Informatics Research Group University of Huddersfield Future: Gipo for continuous domains

53. ICAPS Summer School June 2006Informatics Research Group University of Huddersfield Resources..there are plenty of resources to back up this tutorial: Applications of AI Planning website: http://vitalstatistix.nicve.salford.ac.uk/planet2/ Free Planners, Schedulers and Domain Models: http//scom.hud.ac.uk/planet/repository/ Previous Summer School Notes on many aspects of AI Planning, and Planning Curriculum http//www.planet-noe.org/ Free downloadable software: GIPO http://scom.hud.ac.uk/planform/gipo/ KE for Planning ROADMAP http://scom.hud.ac.uk/planet/home/

54. ICAPS Summer School June 2006Informatics Research Group University of Huddersfield 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.