School of Computing and Mathematics, University of Huddersfield Knowledge Engineering: Issues for the Planning Community Lee McCluskey Department of Computing and Mathematical Sciences, The University of Huddersfield

School of Computing and Mathematics, University of Huddersfield Talk Overview n What is Knowledge Engineering in AI Planning? n Its relation to other fields of study (KBS) n Current and future tools / techniques n Issues for the Community

School of Computing and Mathematics, University of Huddersfield Knowledge Engineering: Our Definition Knowledge Engineering (KE) in AI Planning is the support process that deals with: u the acquisition, validation and maintenance of a planning domain model, u the selection of appropriate planning machinery and heuristics for a particular application. Hence, knowledge engineering processes comprise of the off-line,knowledge-based aspects of planning that are to do with the application being built.

School of Computing and Mathematics, University of Huddersfield Knowledge Engineering Contains various sub-processes: acquisition: eliciting, encoding new knowledge and re-using old knowledge modelling: reasoning with and processing the acquired knowledge validation: promoting the accuracy of (debugging) the acquired knowledge base compilation: translating/refining the knowledge base into an operational form

School of Computing and Mathematics, University of Huddersfield Knowledge Engineering..is a term originating from the KBS community. KBS applications (Expert Systems) utilise simple reasoning processes and complex knowledge structures. This is in CONTRAST to traditional AI planning with its relatively simple knowledge base (half a dozen operators) and complex temporal reasoning.

School of Computing and Mathematics, University of Huddersfield KE for Planning inherits.. Knowledge engineering for Planning inherits from n Knowledge Engineering for Knowledge-based Systems n (Formal Models in) Requirements Engineering Particular Areas: Methods (e.g. KADS) Tools (e.g. Static Analysis) Expressive Modelling Languages (e.g. RML) Problems (e.g. The “KA Bottleneck”)

School of Computing and Mathematics, University of Huddersfield KE for Planning problem? But Planning is different from typical KBS.. It is a synthetic task with Domain Models containing knowledge about actions. AI Planning applications might be characterised as requiring: a complex knowledge base + complex temporal reasoning capabilities

School of Computing and Mathematics, University of Huddersfield Knowledge Engineering in Planning - History n A handful of well known knowledge-based planning applications have been developed in Robots (e.g. RAX), Image Processing (e.g. Vicar) and Military applications. n The approach to KE in these projects must be classified as ad-hoc - tools to support the KE process are chiefly visualisation tools and are planner or perhaps even application specific. There are no “accepted” methods of performing KE in Planning.

School of Computing and Mathematics, University of Huddersfield Knowledge Engineering in Planning - History : ML Tools Process support tools appeared first in the form of machine learning algorithms! - use problem solution examples for operator induction and operator theory revision - use domain model to induce/deduce types, invariants, goal structure, macro operators - use trace analysis/EBL for heuristic acquisition to tune a planner to a particular domain model - use plans retrieved from a plan library to guide the solution to similar problems

School of Computing and Mathematics, University of Huddersfield A Desirable Process Model ? ACQUISITION MODELLING REFINEMENT ONTOLOGIES GENERIC CLASSES DOMAIN- INDEPENDENT- PLANNERS LIBRARIES: TOOLS/TECHNIQUES: GUI’s DOMAIN MODEL INDUCTION CONSISTENCY CHECKERS PLAN STEPPER TASK GENERATOR INVARIANT GENERATOR HEURISTIC ACQUISITION PLANNER CONFIGURATION V & V APPLICATION DOMAIN APPLICATION

School of Computing and Mathematics, University of Huddersfield Summary: Issues for the Community n KE in Planning is in its infancy! Planning research has a history of association with toy problems where KE issues don’t count that much. n Evaluation of KE methods n Improved Representation Languages / Engineering Platforms n Standardisation n Ontologies n Planners as Embedded Components, or used in Mixed Initiative or Planning Assistant mode. Do the same KE problems/solutions apply? n Mapping of application areas to appropriate planning technology

School of Computing and Mathematics, University of Huddersfield A Note About Terminology Terminology is Confusing! E.g. “Domain”.. Domain - the reality Domain Description - any form of symbols (including natural language) used to describe the domain Domain Definition- a precise, completed description Domain Model - an operational description/ specification But what’s a Domain Theory? A Domain Specification?

School of Computing and Mathematics, University of Huddersfield Conclusions AI Planning technology is maturing and is on the point of being exploited. A great opportunity is for planners to be embedded with enabling technology of the Semantic Web. But applied planners are invariably knowledge-based and often embedded. Therefore, the AI Planning Community is waking up to the need for investigating and creating experimental platforms for KE for AI Planning. There is much to do..

School of Computing and Mathematics, University of Huddersfield Appendix: our road-map summary KBSRE / FM KE for Planning Existing Planning Applications Adapt Induce General Methods Build Prototype Environments Planning Theory Planner - Domain Mapping