1998 Technical Reports with Abstracts

• AIAI-TR-230 "Internet-Based Decision Support for Evidence-based Medicine"; Jon Simpson, John Kingston, and Neil Molony; Publisehd in Applications and Innovations in Expert Systems VI, proceedings of Expert Systems '98, the annual conference of the British Computer Society's Specialist Group on Expert Systems, Peterhouse College, Cambridge, UK, 14-16 December 1998.
  Abstract
  The Protocool Assistant is a knowledge-based system, developed by the Department of Artificial Intelligence at the University of Edinburgh, which advises on the treatment of parotid tumours. It has been developed to support both adherence to a clinical protocol based on the latest evidence and the use of clinical judgment where the technique named PROforma, which is specifically desinged for representing best practice guidelines; the PROforma models were used as the basis for a user interface, which was implemented in HTML. A set of rules were developed in JESS (the Java Expert System Shell) which were capable of "running" the protocol; a simple method of reasoning with certainties, based on the "goodness" of each relevant item of published evidence, was used to recommend which path to follow at choice points. However, the user is also supplied with access to the abstracts of all relevatn published papaers, using the hypertext facilities of HTML. The Protocol Assistant can thus be used either as a "wizard" which guides the user through the decision making process, or as a "hypertext manual" which leads them to the information relevant to the decision they are making. This dual-role capability is crucial for the acceptance of KBS in the real world.
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• AIAI-TR-229 "Producing BT's Yellow Pages with Formation"; Gail Anderson, Andrew Casson-du Mont, Ann Macintosh, Robert Rae and Barry Gleeson; presented & publisehd in the Proceedings of the Tenth Conference on Innovative Applications of Artificial Intelligence (IAAI-98), July 26-30 1998, at the Fifteenth National Conference on Artificial Intelligence (AAAI-98) held by the American Association for Artificial Intelligence.
  Abstract
  This case study illustrates how the adoption of AI technology can benefit smaller companies as well as major corporations. Pindar Set is a small UK company which has originated the Yellow Pages directories for British Telecommunications plc since 1979. AIAI is a technology transfer organisation which has delivered innovative solutions to industrial clients since 1984. Together, AIAI and Pindar have developed a next-generation layout system, Formation. Formation is fast, east to use and flexible, and had already delievered benefits through marketing trials before being successfully deployed in production of the Yellow Pages in December 1997. The heart of Formation is a 2D layout engine which formats input data according to styles written in LSSL, a domain-specific language developed at AIAI. Through representing the layout knowledge in Formation explicitly in LSSL styles, and ensuring that it can easily be modified, Pindar has enabled itself to respond far better to its customers present and future needs.
  
  
• AIAI-TR-228 "Knowledge Asset Road Maps"; Ann Macintosh, Ian Filby, John Kingston, and Austin Tate; in Proceedings of The Second International Conference on Practical Aspects of Knowledge Management (PAKM98), 29-30 October, 1998; Basel, Switzerland.
  Abstract
  This paper describes how AIAI has used the ideas and techniques behind Technology Road Maps in order to provide a framework for developing Knowledge Asset Road Maps to support knowledge management initiatives. By carefully relating knowledge management actions upwards to business objectives and strategies, and downwards to specific knowledge assets, a co-ordinated picture of the various parts of an organisation's overall knowledge management programme can be visualized and justified. Knowledge Asset Road Maps used as a strategic planning tool, allow the gaps between an organisation's current know-how and future requirements to be identified, and informed investment decisions to close this gap to be made.
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• AIAI-TR-227 "Extending the HPKB-Upper-Level Ontology: Experiences and ObservationsKnowledge Asset Road Maps"; Stuart Aitken; in Proceedings of The Second International Conference on Practical Aspects of Knowledge Management (PAKM98), 29-30 October, 1998; Basel, Switzerland.
  Abstract
  This paper describes our experience of extending the HPKB-upper-level ontology. Reuse by extension is key to reuse of generic upper-level ontologies, and we report on the use of structuring principles in this task. We argue that the documentation of design rationale is key to reuse of this type of ontology, and that the HPKB-upper-level ontology would benefit from reorgansation.
  

• AIAI-TR-225 "Rationale in Planning: Causality, Dependencies, and Decisions"; Stephen T Polyak and Austin Tate; Knowledge Engineering Review 13(3), September, pp. 247-262, 1998.
  Abstract
  Traditional approaches to plan representation focused on the generation of a sequence of actions and orderings. Knowledge rich models, which incorporate plan rationale, provide benefits to the planning process in a number of ways. The use of rationale in planning is reviewed in terms of causality, dependencies and decisions. Each dimension adresses practical issues in the planning process and adds value to the resultant plan. The contribution of this paper is to explore this categorisation and to motivate the need to explicitly record and represent rationale knowledge for situated, mixed-initiative planning systems.
  
• AIAI-TR-224 "Using the Task Formalism Method to Guide the Development of a Principled HTN Planning Solution for the Construction Industry"; Peter Jarvis and Graham Winstanley; Submitted to the 17th Workshop of the UK Planning and Scheduling Special Interest Group, to be held during September at the University of Huddersfield, UK. September 1998.
  Abstract
  To develop a quality Hierarchical Task Network (HTN) planning application, one must understand how to use the representational devices provided by these systems to construct a principled model of an application domain. Support for this objective is currently limited to evolving guidance frameworks and repositories of domain descriptions. To date, there has been no description of the application of these guidance frameworks to form concrete domain descriptions. The developer is instead left to discover for themself the mapping between the steps of the frameworks and entries in the repositories. In this paper, we address this issue by describing the development of a HTN solution for the construction industry with the guidance of the TF Method. From this experience we conclude that the TF Method offers significant assistance to the knowledge engineer. Specifically, the method highlights the importance of a planned approach to the development of an application, a conscious commitment to a primary modelling method, and the gradual and considered development of HTN descriptions. The method would, however, benefit from pointers to complementary techniques developed in other areas (e.g. the KADS methodology), and from toolĦsupport.
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• AIAI-TR-223 "Reducing the Representational Distance Between Application Domain Experts and AI Planning Technology: a compilation approach"; Peter Jarvis and Graham Winstanley; in Proceedings of the Workshop on Knowledge Engineering and Acquisition for Planning: Bridging Theory and Practice, held during the Fourth International Conference on Artificial Intelligence Planning Systems (AIPS-98), Pittsburgh, USA.
  Abstract
  We present a compilationĦbased approach to reducing the representational distance between application domain experts and AI planning technology. The approach combines a representation designed to match the structure of human expertise in the construction industry with an established planning technique. The design of this representation is derived from a study carried out with experts in the industry. This study shows that expertise in the industry is centred on the components of a building and organised into a subcomponent structure. We demonstrate by encoding the results of this study into a HTN formalism that such formalisms fragment expert knowledge. This fragmentation leads to a large representational distance between expert and formalism, making the task of encoding and maintaining a planner knowledge base a complex one. Our solution is to provide a representation designed around the modelling requirements of the construction industry and then to compile HTN schemata from that representation. We argue that this union reduces the representational distance between expert and formalism, thus lowering the complexity of the knowledge encoding and maintenance tasks, whilst still exploiting powerful AI planning techniques. We conclude by proposing further investigations of this type with the aim of providing a library of domainĦoriented formalisms from which a knowledge engineer may choose an appropriate representation for a given domain.
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• AIAI-TR-222 "TF Method: An Initial Framework For Modelling and Analysing Planning Domains"; Austin Tate, Steve Polyak and Peter Jarvis; in Proceedings of the Workshop on Knowledge Engineering and Acquisition for Planning: Bridging Theory and Practice, held during the Fourth International Conference on Artificial Intelligence Planning Systems (AIPS-98), Pittsburgh, USA. 1998.
  Abstract
  Early work on the NONLIN and O-Plan projects indicated a need for a defined methodology which would guide users performing various roles in the acquisition and analysis of domain requirements for planning. This work included links to a requirement analysis methodology, CORE (COntrolled Requirements Expression), tool support via an intelligent assistant as part of the Task Formalism (TF) Workstation and an initial collection of guidelines and checklists to aid in using the TF domain description language. This paper describes work underway to follow-on from this past research and to infuse it with knowledge gained from recent research related to planning domain development, knowledge modelling, design rationale and ontological and requirements engineering.
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• AIAI-TR-221 "Generation of Multiple Qualitatively Different Plan Options"; Austin Tate, Jeff Dalton and John Levine, AIAI; in Proceedings of AIPS-98, Pittsburgh, June 1998.
  Abstract
  In this paper, we present a Web-based demonstration of a Course of Action (COA) comparison matrix being used as an interface to an O-Plan plan server to explore multiple qualitatively different plan options. The scenario used for this demonstration is concerned with crisis operations on the island of Pacifica. The COA comparision matrix allows the user to explore and evaluate several different plan options based on different command-level requirements and different assumptions about the conditions on the island. This work is part of a larger effort to build a comprehensive mixed initiative planning system incorporating human users in designated user roles.
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• AIAI-TR-220 "Intelligent Support for Enterprise Modelling"; Jussi Stader and Peter Jarvis, AIAI; Submitted to The 13th biennial European Conference on Artificial Intelligence (ECAI-98), Brighton Centre, Brighton, UK, 23-28 August 1998.
  Abstract
  Enterprise modelling - integrating models of all pertinent aspectsof an enterprise - is essential to the management of change in organisations. An integrated view of an organisation provides insightinto what aspects may be changed, how they may be changed, and what the overall effect of specific changes will be. AIAI at the University of Edinburgh has an ongoing research programme which focuses on the use AI techniques to cover the requirements of enterprise modelling and the tools to support it. The AI techniques used in AIAI's programme range from knowledge representation, ontologies and process modelling techniques to visualisation techiques, intelligent workflow and coordination technology. The techniques are combined in an integrated toolset delivered on an agent-based architecture. Part of AIAI's programme is the Enterprise project which has been instrumental in determining the requirements for enterprise modelling and in the development of an integrated toolset to support it. The results of the Enterprise project show that when combined with task management support, enterprise models may directly control the operation of an organisation. Based on the results of the Enterprise project, AIAI's TBPM project currently addresses coordination issues of enterprise modelling support. In this paper, we first describe the requirements for enterprise modelling and enactment in general. We then discuss the Enterprise Toolset which was designed and was implemented to address these requirements. Finally, we evaluate the toolset and describe extensionsthat are currently being undertaken.
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• AIAI-TR-195 "The Enterprise Ontology"; Mike Uschold, Martin King, Stuart Moralee and Yannis Zorgios, 1998, The Knowledge Engineering Review, Vol. 13, Special Issue on Putting Ontologies to Use (eds. Mike Uschold and Austin Tate).
  Abstract
  This is a comprehensive description of the Enterprise Ontology, a collection of terms and definitions relevant to business enterprises. We state its nintended purposes, describe how we went about building it, define all the terms and describe our experiences in converting these into formal definitions. We then describe how we used the Enterprise Ontology and give an evaluation which compares the actual uses with original purposes. We conclude by summarising what we have learned.
  The Enterprise Ontology was developed within the Enterprise Project, a collaborative effort to provide a framework for enterprise modelling. The Ontology was built to serve as a basis for this framework which includes methods and a computer tool set for enterprise modelling.
  We give an overview of the Enterprise Project, elaborate on the intended use of the Ontology, and give a brief overview of the process we went through to build it. The scope of the Enterprise Ontology covers those core concepts required for the project, which will appeal to a wider audience.
  We present natural language definitions for all the terms, starting with the foundational concepts (e.g. entity, relationship, actor). These are used to define the main body of terms, wwhich are divided into the following subject areas: activities, organisation, strategy and marketing.
  We review some of the things learned during the formalisation process of converting the natural language definitions into Ontolingua. We identify and propose solutions for what may be general problems occurring in the development of a wide range of ontologies in other domains. We then characterise in general terms the sorts of issues that will be faced when converting an informal ontology into a formal one.
  Finally, we describe our experiences in using the Enterprise Ontology. We compare these with the intended uses, noting our successes and failures. We conclude with an overall evaluation and summary of what we have learned.
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