planserve – an intelligent problem solving grid

PLANSERVE –An Intelligent Problem Solving Grid

Lee McCluskey and Ron SimpsonArtform Research Group,Department of Computing And Mathematical Sciences

Artform Research Group

CONTENTS + RESOURCES1. Background to PLANSERVE - a EU project at the

proposal stage conceived + led at Huddersfield2. PLANSERVE overview3. Feasibility & related technologies4. SummaryResources 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!


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


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.


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?


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


2. The Answer: PLANSERVEIs 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.


PLANSERVE

PLANSERVEINTERFACE

SOLVER - 2

SOLVER - 1

SOLVER - N

APPICATIONONTOLOGIES

UPPERONTOLOGIES

SOLVERAGENT

MODELBUILDER

-IDENTIFY PROBLEM TYPE

-BUILD UP DOMAIN MODEL

-GENERATE A PROBLEM-SOLVING SERVICE

USER – SCIENTIST, SOFTWARE ENGINEER

WEBAGENTS

…….


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!


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 like operations

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!)


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)


RDF example

RDF ~ set of (Resource, Property, Value)"The Author of

http://scom.hud.ac.uk/scomtlm/Artform/planning.htmlis 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


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


Hierarchy of Languages

DAMLRDFSRDFXML


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.


3.2 Ontologies

RDF/RDFS allows anyone to write their ownname-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.


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


Oil (Ontology Infrastructure Language)

Oil is a web-integrated prototype standard for specifying Ontologies

OIL

Frame-based (OO) language

Based on Web Standards XML and RDF(S)

Description-logic-based semantics and reasoning

Tool Support e.g. Oiled


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>


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.


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.


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 .


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


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!

planserve – an intelligent problem solving grid

Documents

intelligent reasoning

intelligent software

intelligent problem

intelligent agents

software applications

dayand reasoning

reasoning technology

successful applications