A repository of online PDF copies of many of the AIAI publications is available.

You might also like to try our Projects Page (or Pre-1999 Projects Page) for pointers to more publicatons and information on our work.

1997 Technical Reports with Abstracts

• AIAI-TR-219 "Designing Knowledge Based Systems: The CommonKADS Design Model"; John K C Kingston, AIAI; Research and Development in Expert Systems XIV, Proceedings of Expert Systems '97, British Computer Society Specialist Group on Expert Systems, Churchill College, Cambridge, 15-17 December 1997.
  Abstract
  The problem of designing a knowledge based system well relies on the knowledge engineer's programming skills, and on his ability to devise, remember and dynamically update a design specification. This is a difficult task for all but the smallest knowledge based systems.
  These problems can be alleviated by producing representations of the expert's knowledge and of the design specification in the form of text or diagrams. The best known approach for producing such documents is the CommonKADS methodology, particularly its Expertise Model, which models expert problem solving. However, the Expertise Model is intended to represent knowledge at a level of abstraction which is independent of implementaton; it neither allows representation of, not gives guidance on, decisions about which programming techniques to use in order to represent the acquired knowledge. The responsibility for these activities is passed to the CommonKADS Design Model.
  This paper describes the three-stage approach to KBS design recommended by the Design Model (choosing an overall approach to design, choosing ideal knowlege representation and programming techniques, and deciding how to implement the recommended techniques in the chosen software), as well as outlining possible sources of quidance for making good selections of knowledge representations and inference techniques. It then illustrates the use of the Design Model for two systems, one for machine fault diagnosis and one for mortgage application assessment. These systems have been developed by AIAI, and CommonKADS Expertise models for both these systems have been published in [Kingston 1993].
  

• AIAI-TR-217 "Repairing Plans On-the-fly"; Brian Drabble, Jeff Dalton and Austin Tate, AIAI; Proceedings of the NASA Workshop on Planning and Scheduling for Space, October 1997, Oxnard, CA, USA.
  Abstract
  Even with the most careful advance preparation, and even with inbuilt allowance for some degree of contingency, plans need to be altered to take into account execution circumstances and changes of requirements. We have developed methods for repairing plans to account for execution failures and changes in the execution situation. We first developed these methods for the Optimum-AIV planner designed to support spacecraft assembly, integration and verification at ESA, and later deployed for Ariane IV payload bay AIV. This system was itself based on our Nonlin and O-Plan planning algorithms and plan representation. We subsequently refined the methods for the O-Plan planner and incorporated plan repair methods into the system. This paper describes the algorithms used for plan repair in O-Plan and gives an example of their use.
  PDF file

• AIAI-TR-216 "Expert Provisioner: A Range Management Aid"; Rhys Power & Steve Reynolds, RAF Logistics Research and John K C Kingston, Ian Harrison, Ann Macintosh & Jon Tonberg, AIAI; Applications and Innovations in Expert Systems V, Proceedings of Expert Systems '97, British Computer Society Specialist Group on Expert Systems, SGES Publications 1997.
  Abstract
  Expert Provisioner is a knowledge-based provisioning system prototyped for use by the RAF Logistics Command to support their Range Managers in the procurement of consumable parts. Spares provisioning is one of the most fundamental and difficult logistics processes. Any item of equipment will need to have some of its component parts replaced at some time during its operational life. Re-provisioning is the art of ensuring that spare parts are available when required without tying up much needed capital in excessive inventory holdings. To conduct re-provisioning properly requires a great deal of specific knowledge about item characteristics and customer requirements, coupled with a high level of expertise in re-provisioning procedures.
  The starting point for Expert Provisioner is an electronic purchase order form and its end point is a recommendation of whether to buy the item or not, its cost and due delivery date. Purchase recommendations are made based on many factors including forecast demand, unit costs, shelf life and existing stock levels. The system removes much of the mundane work in order processing as well as potential for misinterpretation of information. The system is designed such that the user remains in control throughout the consultation and can, if desired, override decisions. Expert Provisioner was implemented using the NASA CLIPS development tool for the inference engine and knowledge base. The system was developed and delivered under Windows 3.1 through the use of AIAI's multi-platform wxCLIPS tool for the user interface.
  

• AIAI-TR-215 "Multi-Perspective Modelling of Air Campaign Planning"; John K C Kingston and Terri Lydiard, AIAI and Anna Griffith, ISX Corporation; Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI '97), Nagoya, Japan, 23-29 August 1997.
  Abstract
  This paper describes work performed to acquire knowledge about, and produce models of, the USAF Air Campaign Planning (ACP) process. The aim of this work was to produce a set of "knowledge models" which researchers in the area could refer to, rather than having each of them interview the expert planners. It was decided that the models which were produced should be multi-perspective models; that is, a variety of models would be produced, each containing a particular type of knowledge about the air campaign planning process. The basis for this approach was the CommonKads methodology for modelling organisational and expert knowledge. This paper describes the development of organisational, task and communication models to represent air campaign planning from various perspectives. For some models, it was decide that CommonKads' representations were not sufficiently rich, and so alternative modelling techniques (IDEF3 and Role Activity Diagrams) were used to represent the Task and Communication models. It was discovered that these techniques could be used without modification to represent CommonKADS models. An architecture is proposed, based on the Sowa/Zachman framework for Information Systems Architecture, to help determine the types of knowledge addressed by various modelling techniques.
  PDF file

• AIAI-TR-214 "Selecting a KBS tool for a knowledge based system"; Stefan Robertson, Midland Treasury Support Services and John K.C.Kingston, AIAI; Joint Pacific Asian Conference on Expert Systems/Singapore International Conference on Intelligent Systems, Singapore 24-27 February 1997.
  Abstract
  When an organisation embarks onto a project or strategy which will bring knowledge based systems into their business processes, one of the most frequently asked questions is, "Which software tool should be used?". The task of selecting the best tool requires both an underatanding of the relevant characteristics of a project and a considerable knowledge of the competing alternatives, from which it was deduced that this selection task could usefully be implemented using a knowledge based system. The purpose of this paper is to describe the nature of the task of tool selection, and to describe how the relevant aspects of this task were represented using the CommonKADS methodology in order to encode them in a knowledge based system. The finished system was tested by applying it to the task of selecting the best tool for implementing itself. The paper concludes with a discussion of the important factors in selecting a KBS tool, and the appropriateness of CommonKads for this project.
  

• AIAI-TR-213 "An Intelligent System For Bid Management"; Jussi Stader, AIAI; in The International Journal of Project & Business Risk Management; published by Project Manager Today Publications; Autumn 1997.
  Abstract
  Managing change in a complex business environment presents real challenges. Chief amongst these is the pace of change in terms of technology, management innovation and the whole competitive environment. Understanding how the business operates makes it possible to identify and address areas that are restraining business performance. Enterprise modelling methods capture various aspects of how a business works and how it is organised. This helps to obtain an enterprise-wide view of an organisation, which can then be used as a basis for taking decisions. AIAI at the University of Edinburgh lead the Enterprise project, which focused on management innovation and the strategic use of IT to help manage change. AIAI and Pilkington Optronics worked together to demonstrate how the approach of the Enterprise project could be used to address Pilkington Optronics's real business needs. This lead to the development of the Bid Manager system which supports the bid / no-bid decision. By basing the Bid Manager on the Enterprise Toolset, Pilkington Optronics have been able to obtain a system that integrates existing IT application systems, enabling them to upgrade the overall system as their business processes develop.
  PDF file

• AIAI-TR-211 "A Tool Set For Enterprise Modelling"; Jussi Stader, AIAI; in Proceedings of Interfaces'97, 6th International Conference, held in Montpellier, France on 28-30 May 1997; published by EC2 & Developpement, Paris, France.
  Abstract
  In this paper we present a tool set for enterprise modelling developed during the Enterprise project at AIAI, the University of Edinburgh. Our approach concentrates on integration, communication, flexibility, and support. We describe the Enterprise Tool Set which uses executable process models to help users to perform their tasks. The Tool Set is implemented using an agent-based architecture to integrate off-the-shelf tools in a plug-and-play style. To ensure effective interchange of information and knowledge between different users, tasks and systems, we developed the Enterprise Ontology which defines terms used in organisations. The project has been successful and valuable insights have been gained and made available.
  PDF file

• AIAI-TR-210 "Mixed Initiative Interaction in O-Plan"; Austin Tate, AIAI; Paper for AAAI 1997 Spring Symposium -- Computational Models for Mixed Initiative Interaction; Copyright 1997, American Association for Artificial Intelligence; December 1996.
  Abstract
  Work is described which seeks to support mixed initiative interaction between a "task assignment" or "command" agent and a planning agent. Each agent maintains an agenda of outstanding task it is engaged in and uses a common representation of tasks, plans, processes and activities based on the notion that these are all "constraints on behaviour". Interaction between the agents uses explicit task and option management information. This framework can form a basis for mixed initiative user/system agents working together to mutually constrain task descriptions and plans and to coordintate the task oriented generations, refinement and enactment of those plans.
  PDF file