PLANSERVE – An Intelligent Problem Solving Grid

1. PLANSERVE –An Intelligent Problem Solving Grid Lee McCluskey and Ron Simpson Artform Research Group, Department of Computing And Mathematical Sciences

2. CONTENTS + RESOURCES 1. Background to PLANSERVE - a EU project at the proposal stage conceived + led at Huddersfield 2. PLANSERVE overview 3. Feasibility & related technologies 4. Summary Resources for today’s subject: • Click “Recent Talks” in my home page – slides from seminars on the Semantic Web (Oct 2002) and Ontologies (March 2001) as well as this one!

3. BACKGROUND There have been some high profile, very successful applications involving “intelligent software” recently. Best known – NASA’s Remote Agent Experiment, where a spacecraft was input: high level mission goals each day And reasoning with knowledge of its environment, and sensor information, the software synthesised: instructions to control the spacecraft’s operations

4. BACKGROUND There have been great technological advances in intelligent reasoning algorithms in the last 10 years – eg planning algorithms that can handle many forms of reasoning to do with eg time, resources, uncertainty, can solve problems orders of magnitude larger than previously.

5. BACKGROUND But there are STILL few software applications that embody some kind of high level intelligent behaviour such as learning, planning and problem solving EG there are lots of “mundane” applications that could benefit – • Workflow management • Resource scheduling • Travel planning WHY?

6. BACKGROUND major reasons: -- knowledge acquisition bottleneck: ‘intelligent’ reasoning requires an application ‘knowledge base’, and the area of (autonomous) knowleck dge acquisition is still poor! New forms of reasoning / intelligent process have had to have new hand-crafted knowledge bases built for them for each application area in which they are need to work. -- ‘General’ reasoning systems don’t scale up: The high profile successes tend to be in very narrow applications such as in Chess or Expert Systems NASA (intelligent space applications) and SRI (military applications) use teams of very bright knowledge engineers to craft knowledge bases

7. 2. The Answer: PLANSERVE Is an EU Framework 6 project proposal conceived by the authors to solve all these problems!! We aim to bring intelligent reasoning services [for solving problems that involve reasoning about time, actions, events, goals, activities, and resources] to the desktop of any scientist or engineer, and make them available as web services to intelligent agents.

8. PLANSERVE • IDENTIFY PROBLEM TYPE • BUILD UP DOMAIN MODEL • GENERATE A PROBLEM-SOLVING SERVICE WEB AGENTS SOLVER - 1 SOLVER - 2 PLANSERVE INTERFACE SOLVER AGENT ……. SOLVER - N USER – SCIENTIST, SOFTWARE ENGINEER MODEL BUILDER APPICATION ONTOLOGIES UPPER ONTOLOGIES

9. 3. Feasibility: related technologies The PLANSERVE idea is only feasible because of the continued development in: • 3.1 Semantic Web • 3.2 Ontologies • 3.3 The KBS ‘bottleneck’ solution: • 3.4 Grid Computing • Advances in reasoning technology + support from a large European group of establishments!

10. 3.1 Semantic Web • The Semantic Web is the Vision of an internet with resources that are machine understandable or accessible to automated processes - machines should do much more than present the information visually or do human-consumable IR. • Already very high level languages are being designed for a semantic web with very rich web languages (with XML as “machine code”) • The SW will be a bit like an enormous distributed OODB with SW services acting likeoperations • The Semantic Web’s dynamic, boundless aspects coupled with structured descriptions of info and processes will present great opportunities for research and developers of AI technology (as well as much else!)

11. SW Base Level - the Resource Description Framework RDF is a convention for describing meta-data. It’s a ‘lightweight’ model in data terms - and one that can be encoded in XML. It is based on • everything having a URI = Universal Resource Identifier • Properties - resources with a ‘name’ such as slots in an object frame • An RDF document is a series of Statements - (Resource, Property, Value)

12. RDF example RDF ~ set of (Resource, Property, Value) "The Author of http://scom.hud.ac.uk/scomtlm/Artform/planning.html is Lee McCluskey.” IN RDF: <rdf:Description about= http://scom.hud.ac.uk/scomtlm/Artform/planning.html'> <Author> Lee McCluskey </Author> </rdf:Description> Resource, Property, Values can all have URI’s

13. RDFS, DAML RDF Schema = RDF + classes, properties of properties, etc - gives more structure to RDF This would make the Semantic Web look like one enormous distributed OODB… DAML = Darpa Agent markup language ~ RDFS + some logic

14. Hierarchy of Languages DAML RDFS RDF XML

15. Semantic Web for Services + PLANSERVE Currently web services don’t formally advertise their semantics eg as pre and post conditions. SW languages will allow an expressive and standard way of doing this. The I/O of Planserve will be also require a rich knowledge-based language encoded in a suitable SW language such as DAML. This will make it easily accessible over the internet.

16. 3.2 Ontologies • RDF/RDFS allows anyone to write their own name-space document (a ‘schema’). This defines properties and classes in some application domain. These form vocabularies which can be used globally for sharing the meaning of tags An ‘Ontology’ is an agreed upon, shared, common understanding of a ‘conceptual domain’ written as an explicit, formal specification. It can be implemented as an RDFS vocabulary.

17. Ontologies - background • First used for Knowledge-Sharing in KBS, it seems that many scientific areas are creating their own Ontologies (even Genomics!!) They can be as simple as a ‘concept hierarchy’ or as complex as an axiomatic theory of sets. • There are various kinds of ontology: • ‘upper’ ontology representing common sense knowledge; • representation ontology - axiomatization of basic operations used in many applications • application ontology representing shared knowledge of an application domain. Shared Understanding - Expressive - Formal - Focussed

18. Oil(Ontology Infrastructure Language) Oil is a web-integrated prototype standard for specifying Ontologies Based on Web Standards XML and RDF(S) Frame-based (OO) language OIL Description-logic-based semantics and reasoning Tool Support e.g. Oiled

19. DAML+Oil example Define a "product number"'s domain and range.. <daml:DatatypeProperty rdf:ID="productNumber"> <rdfs:label>Product Number</rdfs:label> <rdfs:domain rdf:resource="#Product"/> <rdfs:range rdf:resource= "http://www.w3.org/2000/10/XMLSchema#nonNegativeInteger"/> </daml:DatatypeProperty> ”Availability" is a sort of enumerated type.. <daml:Class ID="Availability"> <daml:oneOf parseType="daml:collection"> <daml:Thing rdf:ID="InStock"> <rdfs:label>In stock</rdfs:label> </daml:Thing> <daml:Thing rdf:ID="BackOrdered"> <rdfs:label>Back ordered</rdfs:label> </daml:Thing> <daml:Thing rdf:ID="SpecialOrder"> <rdfs:label>Special order</rdfs:label> </daml:Thing> </daml:oneOf> </daml:Class>

20. Ontologies + PLANSERVE The growth of the Ontology area should facilitate the access and usability of application domain models. ..as the creation of application ontologies becomes more common in science, one might think of an application ontology as being an input to Planserve.

21. 3.3 The KBS ‘bottleneck’ • The KBS ‘bottleneck’ was the problem of acquiring, validating and engineering enough specific (expert) knowledge to make a KBS useful. • Apparently the solutions are : • knowledge base and problem solver connectivity via language and interface conventions (eg KB API’s such as OKBC, PDDL and their XML equivalents) • knowledge re-use and knowledge sharing via • ontologies • the use of generic problem solving methods and components.

22. 3.4 Grid Computing • Grid computing is the deployment of huge amounts of distributed computing power to the solution of scientific tasks that demand such resources. • PLANSERVE will require a great deal of distributed computing power. Hence we will need to derive a grid architecture to support the distribution and flow of knowledge acquisition, knowledge revision, and knowledge-based problem solving. • In using Grid Computing to solve major scientific tasks, a major process is to generate a workflow - an ordered configuration of computational components to achieve the task. An application of the PLANSERVE itself would be to automate this process of Workflow Generation .

23. 3.5 The Consortium: • Universities: Huddersfield, Ulm, Open, Salford, Madrid, Granada, Troyes, Prague, Brescia, Perugia, Macedonia, Ankara, Porto, Marseilles, Cyprus • Research Institutes + Companies: Atos-origin Netherlands, NCR Rome, Institute of Graphics Darmstadt, DAI Laboratory Berlin, IKERLAN Spain, National Aerospace Laboratory Netherlands, LabAge Belgium, COSYTEC France

24. 4. Summary We are hoping to embark on a VERY ambitious project in prototyping PLANSERVE involving • The Semantic Web • Ontologies • KBS technologies and language standards • Grid Computing and Infrastructure • Intelligent reasoning technology Everyone is welcome to get involved!