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AIAI-TR-205
"Modelling Software Development Processes and Standards"; Jim Doheny
and Ian Filby; This paper was presented at The Software Quality
Conference, Dundee, UK; July, 1996;
£ 4.00 UK/surface mail; £ 6.00 airmail
Abstract
This paper describes a conceptual framework and associated support tool,
which we have developed for modelling and assessing software development
processes and standards. We believe that this technology will play an
important role in assisting project managers to plan projects such that the
software development processes meet quality assurance requirements.
Software process modelling can also assist project auditors and others in
assessing a development process. In addition to using our process modelling
framework to represent specific software development projects, we are also
using the framework to model in an explicit form the contents of software
development standards and quality procedures. Software development
standards and procedures contain provisions for activities, methods or
artifacts; they are effectively fragments of development processes or
constraints on the development process. A support tool, ASPEN (A Software
Process ENgineering tool), is being developed which provides support for
project auditors in evaluating existing projects and for project managers
in planning new projects. Development processes are assessed using
knowledge bases of development methods, technical standards and engineering
"best practice". ASPEN runs on UNIX platforms under MOTIF and PCs under MS
Windows.
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AIAI-TR-204
"A Framework and Tool for Modelling and Assessing Software Development
Processes"; Jim Doheny and Ian Filby, AIAI; This paper was presented
at The European Software Control and Metrics Conference; Wilmslow, UK;
May, 1996;
£ 4.00 UK/surface mail; £ 6.00 airmail
Abstract
This paper describes a conceptual framework and associated support tool,
which we have developed for modelling and assessing software development
processes. We believe that Process modelling technology provides a good
basis for helping to improve understanding and communication of the way
that software is developed. It can assist project managers to plan projects
that meet quality assurance requirements and can also assist project
auditors and others in assessing a development process. Our process
modelling framework is based on a process ontology (vocabulary) that
incorporates software project artifacts (e.g. design specifications and
code), methods, activities and the agents (people or computer programs)
that carry out these activities. Development processes are assessed using
knowledge of development methods, technical standards and engineering
"best practice". In addition to representing specific software development
projects, we are using the framework to model in an explicit form the
contents of software development standards and quality procedures.
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AIAI-TR-203
"A Framework for Modelling Software Development Processes"; Jim Doheny
and Ian Filby, AIAI; This paper was presented at the Software Quality
Management IV, Cambridge, UK; April, 1996;
£ 4.00 UK/surface mail; £ 6.00 airmail
Abstract
This paper describes the ASPEN software process modelling framework and
support tool. The modelling framework is based on a process ontology
(vocabulary) that incorporates software project artifacts (e.g. design
specifications and code), methods, activities and the agents that carry out
these activities. It includes a rich information modelling taxonomy that
supports the classification of project artifacts, by relating the project
artifacts to the things in the 'real-world' which they represent. Software
development standards may be explicitly represented as constraints on the
development process. The ASPEN support tool provides support for project
auditors/assessors in evaluating existing processes and for project
managers in constructing process models.
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AIAI-TR-201
"Evaluation of workbenches which support the CommonKADS methodology";
John Kingston, Jim Doheny and Ian Filby, AIAI; Appears in Knowledge
Engineering Review, 10, 3, 1995;
£ 12.00 UK/surface mail; £ 14.00 airmail
Abstract
The KADS methodology and its successor, CommonKADS, have gained a
reputation for being useful approaches to building knowledge based
systems in a manner which is both systematic and well documented.
However, these methods require considerable effort to use them
completely. It has been suggested that automated support for KADS or
CommonKADS users, in the form of "knowledge engineering workbenches",
could be very useful. These tools would provide computerised
assistance to knowledge engineers in organising and representing
knowledge, in a similar fashion to the support which CASE tools
provide for software engineers. In order to provide support for the
modelling techniques recommended by these methods, which are very
detailed in the representation and analysis stages of knowledge
engineering. A good knowledge engineering workbench should also be
easy to use, should be robust and reliable, and should generate output
in a presentable format.
This paper reports on an evaluation of two commercially available workbenches for supporting the KADS approach: KADS Tool from ILOG and
Open KADS Tool project, funded by the European Community's ESSI
programme, which aimed to introduce CommonKADS to two
technology-oriented companies. Information is also presented on two
other workbenches: the CommonKADS workbench (which will soon become
commercial the VITAL workbench. The results show
various strengths and weaknesses in each tool.
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AIAI-TR-200
"Knowledge Level Planning in the Search and Rescue domain"; Hugh
Cottam, University of Nottingham; Nigel Shadbolt, University of
Nottingham; John Kingston, AIAI; Howard Beck, AIAI and Austin Tate,
AIAI; Appears in Research and Development in Expert Systems XII,
Proceedings of BCS Expert Systems '95, Cambridge, December, 1995;
£ 5.00 UK/surface mail; £ 7.00 airmail
Abstract
The increased use of intelligent decision support systems has created
a demand for efficient acquisition, implementation and maintenance of
the knowledge required by such systems. The field of knowledge level
modelling has developed as a means to this end. This has led to the
construction of methodologies for KBS development that facilitate a
generic approach to knowledge acquisition. Such generic approaches
have achieved great success when applied to various domains, yet have
thus far largely neglected the generic areas of planning, scheduling
and resource allocation. In this paper we outline the development of
such a generic approach within the domain of planning for Search and
Rescue. Our generic approach makes a distinction between domain
derived knowledge level models and those derived from systems. We
describe how the combination of these two types of model can achieve
definite benefits within the course of KBS development.
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AIAI-TR-199
"CommonKADS Models for Knowledge Based Planning"; John Kingston, AIAI;
Austin Tate, AIAI and Nigel Shadbolt, University of Nottingham;
Presented at the AAAI 96', Portland, Oregon; August 1996;
Abstract
The CommonKADS methodology is a collection of structured methods for building
knowledge based systems. A key component of CommonKADS is the
library of generic inference models which can be applied to tasks of specified
types. These generic models can either be used as frameworks for
knowledge acquisition, or to verify the completeness of models
developed by analysis of the domain. However, the generic models for
some task types, such as knowledge-based planning, are not
well-developed. Since knowledge-based planning is an important
commercial application of Artificial Intelligence, there is a clear need for
the development of generic models for planning tasks.
Many of the generic models which currently exist have been derived from
modelling of
existing AI systems. These models have the strength of proven applicability.
There are a number of well-known and well-tried AI planning systems in
existence; one of the best known is the Open Planning Architecture (O-Plan).
This paper describes the development of a CommonKADS generic inference model
for knowledge-based planning tasks, based on the capabilities of the O-Plan
system. The paper also briefly describes the verification of this model in
the context of a real-life planning task: the assignment and management of
RAF Search and Rescue operations.
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AIAI-TR-198
"The PIF Process Interchange Format and Framework Version 1.1";
Jintae Lee, Michael Gruninger, Yan Jin, Thomas Malone, Austin Tate, Gregg Yost,
and other members of the PIF Working Group;
MIT Center for Coordination Science Working Paper ~194, Cambridge, MA; 1996;
£ 10.00 UK/surface mail; £ 12.00 airmail
Abstract
This document provides the specification of the Process Interchange
Format (PIF) version 1.1. The goal of this work is to develop an
interchange format to help automatically exchange process descriptions
among a wide variety of business process modelling and support systems
such as workflow software, flow charting tools, planners, process
simulation systems, and process repositories. Instead of having to
write ad hoc translators for each paif of such systems, each system
will only need to have a single translator for converting process
descriptions in that system into and out of the common PIF format.
Then any system will be able to automatically exchange basic process
descriptions with any other system.
This document describes the PIF-CORE 1.1, ie., the core set of object
types (such as activities, agents and prerequisite relations) that can
be used to describe the basic elements of any process. The document
also describes a framework for extending the core set of object types
to include additional information needed in specific applications.
These extended descriptions are exchanged in such a way that the
common elements are interpretable by any PIF translator and the
additional elements are interpretable by any translator that knows
about the extensions.
The PIF format was developed by a working group including
representatives from several universities and companies and has been
used for experimental automatic translations among systems developed
independently at three of these sites. This document is being
distributed in the hopes that other groups will comment upon the
interchange format proposed here and that this format (or future
versions of it) may be useful to other groups as well. The PIF
Document 1.0 was released in December 1994, and the current document
reports the revised PIF that incorporate the feedback received since then.
AIAI-TR-194
"Enterprise Modelling and Knowledge"; John Fraser; presented at the Oil and
Gas Information Conference 96, Stavanger, Norway; 1996;
£ 2.00 UK/surface mail; £ 3.00 airmail
Abstract
In this paper I acknowledge the value of Information Technology (IT)
to the modern enterprise. I suggest, however, that the value of IT in
managing the key resource of any company - its people, their knowledge
and their ability to learn - is limited. The long-term health of
companies, industries and national economies relies on an
understanding of the dynamic nature of knowledge, of systems and
ultimately of people. It can be a great help but, without the
understanding, it can create a dangerous veneer of progress.
What we should be working towards is the creation and management of
knowledge - the test for knowledge is whether prediction is possible -
and perhaps we will see a new era of "knowledge technology" to follow
that of information technology.
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AIAI-TR-192
"Converting an Informal Ontology into Ontolingua: Some Experiences;
Mike Uschold; A slightly abridged version of this paper appears in the
Proceedings of the Workshop on Ontological Engineering held in
conjunction with ECAI 96, Budapest; March 1996;
£ 5.00 UK/surface mail; £ 7.00 airmail
Abstract
We report our experiences of converting a carefully defined informal
ontology expressed in natural language into the formal language: Ontolingua.
The objectives of this paper are 1) to explore some of the nitty gritty
details of formalising ontology definitions and 2) to serve as a basis for
clarifying the relationship between this and other approaches to ontology
construction (e.g. using competency questions), for the eventual aim
of producing a comprehensive methodology
We first discuss concepts in the meta-ontology, including entities, classes,
instances, relationships, roles, sets and states of affairs. With respect
to roles, we define a special meta-class to classify objects whose existence
necessarily depends on their being in a relationship with some other entity
(e.g a customer). We describe a mechanism for classifying states of affairs
which can be used to restrict what can be in certain relationships (e.g
pre-condition).
We then note some general issues that arise when producing formal
definitions of the main terms; e.g. representing terms from a
difference perspective, and identifying when and how new terms must be
introduced. The need for new terms arises not only to fill gaps, but also
to make explicit facts and logical dependencies that were only implied by
the text definitions.
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AIAI-TR-191
"ONTOLOGIES: Principles, Methods and Applications"; Mike Uschold, AIAI
& Michael Gruninger, University of Toronto; To appear in Knowledge
Engineering Review, Volume 11 Number 2, June 1996;
£ 15.00 UK/surface mail; £ 17.00 airmail
Abstract
This paper is intended to serve as a comprehensive introduction to the
emerging field concerned with the design and use of ontologies. We observe
that disparate backgrounds, languages, tools, and techniques are a
major barrier to effective communication among people, organisations, and/or
software systems. We show how the development and implementation of an
explicit account of a shared understanding (i.e. an `ontology') in a given
subject area, can improve such communication, which in turn, can give rise
to greater reuse and sharing, inter-operability, and more reliable software.
After motivating their need, we clarify just what ontologies are and what
purposes they serve. We outline a methodology for developing and evaluating
ontologies, first discussing informal techniques, concerning such issues as
scoping, handling ambiguity, reaching agreement and producing definitions.
We then consider the benefits of and describe, a more formal approach. We
re-visit the scoping phase, and discuss the role of formal languages and
techniques in the specification, implementation and evaluation of
ontologies. Finally, we review the state of the art and practice in this
emerging field, considering various case studies, software tools for
ontology development, key research issues and future prospects.
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AIAI-TR-164
"MOBEDIC - A Decision Modelling Tool For Emergency Situations";
Jim Doheny, John Fraser;
Expert Systems with Applications, vol 10, 1996;
£ 2.50 UK/surface mail; £ 3.50 airmail
Abstract
This paper describes a software tool that we have developed at AIAI
for modelling the decisions that people make in emergency situations
in offshore environments. The tool was developed using C++ and runs
on a PC under MS Windows. It has a generic architecture and can be
easily extended to other environments with different characteristics,
e.g., hospitals, commercial buildings, etc. We use frames to
represent a person's characteristics and their perception of the
environment; scripts are used to define typical behaviours for
particular situations. Our tool can be used to predict the likely
behaviours of a population in hazardous situations and help evaluate
the effectiveness of emergency procedures and training.
We have worked with our collaborators to integrate our decision model
with their model of people's movement to produce a system that can
realistically simulate emergency scenarios on offshore structures. We
believe that this is the first egress and evacuation modelling tool to
incorporate both decision making and movement modelling. Our work is
therefore an important step in the introduction of improved approaches
to the evaluation of offshore safety management.
Validating the decision model proved difficult because of lack of
suitable data. We acquired additional data by interviewing offshore
personnel and monitoring a mustering exercise. We then simulated an
offshore emergency scenario and the results were encouraging. In the
future we would like to enhance our model by incorporating
communication between personnel. This would allow us to model complex
scenarios, especially those that cannot be simulated realistically in
training exercises.
