Lee McCluskey, University of Huddersfield - EKAW'04

Knowledge Formulation for AI Planning

Lee McCluskey

Ron Simpson

Artform research groupDepartment of Computing and

Mathematical Sciences,

The University of Huddersfield

Lee McCluskey, University of Huddersfield - EKAW'04

Contents

Background + Problems in KE for AI Planning Automated acquisition by generic object patterns Automated acquisition by induction Advertising some KE+AI planning things

Ontology-free talk

Lee McCluskey, University of Huddersfield - EKAW'04

A I Planning and Scheduling has moved on…

B

A

A

BTHEN

NOW

Lee McCluskey, University of Huddersfield - EKAW'04

Missing Layer…

B

A

A

B

InferenceLogicRDFXML

Trust

Semantic Web

Not just pre-conditionachieventt!

Lee McCluskey, University of Huddersfield - EKAW'04

The Problem – KE for AI PlanningIn order to reason with actions, events, processes etc

symbolic AI technology should have a representation of them.

How is this knowledge acquired?

The manual process of encoding and maintenance is HARD.

APPLICATIONDOMAIN

DomainModel

PlanningEngine

Planning System

Lee McCluskey, University of Huddersfield - EKAW'04

Background and Research Aim

Our area: algorithms and representations for AI plan generation technology.

Our aim is to make the technology

more accessible and usable. The knowledge engineering method/tools should reduce the

complexity of the creation process by abstraction (eg of “mathematical details”) reuse (eg planning patterns, import ontologies) early error ID (static/dynamic tests)

Eventually we would like to construct an autonomous knowledge acquisition agent for planning problems.

Lee McCluskey, University of Huddersfield - EKAW'04

Results of early work: GIPO http://scom.hud.ac.uk/planform/gipo/

Lee McCluskey, University of Huddersfield - EKAW'04

GIPO – versions

GIPO 1.1 DownloadableFor ‘Flat’ models (ECP’01)

GIPO 2

DownloadableFor hierarchical models(ICAPS’03)

GIPO+

For models withcts time, eventsand processes(PlanSig’03)

GIPO 1.2

Incorporating first version operator induction (AIPS’02)

Lee McCluskey, University of Huddersfield - EKAW'04

Problems

GIPO has a user-base but problems prevent it from being very effective:

Dosen’t hide tricky parameter manipulation Re-use only of existing models (not abstract) also

‘re-factoring’ hard Still have to be a Planning/KE expert to use

In the paper we detail two ‘high level’ approaches that suppress some of the mathematical details and (we claim) make the KE process more efficient

Lee McCluskey, University of Huddersfield - EKAW'04

Example in paper:The Lazy Hiking WorldImagine 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

Lee McCluskey, University of Huddersfield - EKAW'04

Automated acquisition by generic object patterns

IDEA - many planning domains are built on common sets of Patterns.

We have ‘hardwired’ some of these patterns into GIPO e.g. mobile, carrier, bistate, portable ..

INPUT: user configures patterns THEN merges them with other configured patterns.

Eg in Hiking world a tent = portable + bistate

Car = carrier

Person = driver + portable + bistate

OUPUT: full domain model.

Lee McCluskey, University of Huddersfield - EKAW'04

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

Lee McCluskey, University of Huddersfield - EKAW'04

EVALUATION?

- Evaluated by re-creating benchmark domains, new domains, or new versions of old domains ..

EG OPMAKER: The Hiking Domain: a full action schema set was generated by Opmaker, passing all local and global validation checks in the GIPO system. The resulting model was fed into Hoffman’s FF via GIPO, generated a plan to solve the general hiking problem. This was all done in approximately 1 day’s development.

- Our claim: encoding time of planning benchmarks - Hours (generic object patterns / induction )- 1 or 2 days (with GIPO) - Several days / weeks (hand written)All this could be independently verified as GIPO is publicly available

BUT the two techniques (generic object patterns, induction of operators) not independently, empirically validated yet

Lee McCluskey, University of Huddersfield - EKAW'04

Related Work

Not a great deal:- Some work in inducing action schema in the

Planning literature (Wang, Grant) but not in the context of a tools environment like GIPO

- Generic patterns for AI Planning: Our work was originally formulated with Fox and Long of Strathclyde University – but we know of no similar work

Lee McCluskey, University of Huddersfield - EKAW'04

Conclusions + Future Work “Planning technology is more accessible / usable /

less error prone with GIPO + new high level methods”

BUT Re-factoring: Can edit configured patterns rather

than domain model (and re-generate domain model) BUT ‘manual’ changes would be lost.

Scaling-up: Generic objects / induction methods still to be implemented on more expressive versions of GIPO

Generic Object Interface: Text -> Diagrammatic (State machine) interface

Lee McCluskey, University of Huddersfield - EKAW'04

Advertisement sections

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 comprehensive web site for planners and

schedulers, planning tools, domain models - on

http://scom.hud.ac.uk/planet/repository/

AND a roadmap for KE in AI Planning sponsored by the EU PLANET Network

Lee McCluskey, University of Huddersfield - EKAW'04

More Advertising

ICAPS’05: we are staging the First International Competition on Knowledge Engineering for Planning and Scheduling

Lee McCluskey, University of Huddersfield - EKAW'04

Even more advertising…