Analyzing Complex Organizational Arguments with Logical Model Building

An E-Librarian Service allows users to enter complete questions in natural language (NL) in order to improve the retrieval capabilities (see section 1.3.3). The aim of the natural language processing (NLP) module of an E-Librarian Service is to translate a user question into a logical and computer-readable form. The more precise and accurate this translation is, the more complex the algorithm gets. In this chapter we present the necessary principles to design the NLP module of an E-Librarian Service that is able to “understand” the meaning of a user question and to translate it into a logical form.

The paper formulates the basic problem of the content automatic processing of textual information. An author describe formal rules by which we can found logical connections between natural language sentences that compose the electronic text document. Principles of synthesis of logic and linguistic models (LLM) are the conditions under which, within LLM, we can replace components identical by content and form one-dimensional arrays, elements of which are words or phrases that connect natural language sentences to each other in text’s parts. The above principles can be applied to text’s sentences, which are arranged sequential, as well as for sentences from several paragraphs. Expansion and improvement of principles of synthesis for various methods of logical connection between the parts of the text will improve the search content quality using formal logic and linguistic models.

Formal logic provides us with a powerful set of techniques for criticizing some arguments and showing others to be valid. These techniques are relevant to all of us with an interest in being skilful and accurate reasoners. In this very accessible book, extensively revised and rewritten for the second edition, Peter Smith presents a guide to the fundamental aims and basic elements of formal logic. He introduces the reader to the languages of propositional and predicate logic, and develops natural deduction systems for evaluating arguments translated into these languages. His discussion is richly illustrated with worked examples and exercises, and alongside the formal work there is illuminating philosophical commentary. This book will make an ideal text for a first logic course and will provide a firm basis for further work in formal and philosophical logic.

This paper does not deal with the topic of ‘the generosity of artificial languages from an Asian or a comparative perspective’. Rather, it is concerned with a particular case taken from a development in the Western tradition, when in the wake of the rise of formal logic at the end of the nineteenth and the beginning of the twentieth century people in philosophy and later in linguistics started to use formal languages in the study of the semantics of natural languages. This undertaking rests on certain philosophical assumptions and instantiates a particular methodology, that we want to examine critically. However, that in itself is still too broad a topic for a single paper, so we will focus on a particular aspect, viz., the distinction between grammatical form and logical form and the crucial role it plays in how the relationship between natural languages and formal languages is understood in this tradition. We will uncover two basic assumptions that underlie the standard view on the distinction between grammatical form and logical form, and discuss how they have contributed to the shaping of a particular methodology and a particular view on the status of semantics as a discipline. But before we turn to the topic at hand, a few more words on the general nature of the investigation are in order. Its general aim is to find out how semantics constructs its object, i.e., we are interested in what semanticists view as the proper object of study, how they think this object can best be approached, and how they view the relations between their own undertaking and neighbouring disciplines that deal with related, or even the same kind of phenomena, such as cognitive psychology, cognitive neuroscience, philosophy, anthropology. The background assumption is that, much like in other disciplines, semantics, too, does not have its object of investigation cut out for itself by nature, but constructs it in a complex process that involves empirical elements (‘facts’ being already too dangerous a term), philosophical assumptions, and borrowings

To understand and generate natural language requires some means of representing knowledge. A particularly appropriate tool in this endeavour is logic. In this chapter, three kinds of logic (default, model, and temporal) are introduced and their use in resolving certain natural language representational issues is outlined.

We present a novel approach to the creation of smart contracts that takes an existing legal document and allows it to be incrementally elaborated into a tested smart contract by domain experts. Smart contracts are currently built with compiled imperative languages, and suffer from lack of agility, elevated risks from errors and security flaws, and high development costs. This paper describes a smart editor that uses a declarative language (Answer Set Programming (ASP)) to represent the business logic of legal documents. The document is incrementally elaborated in a fixed sequence of steps beginning with an ontology discovery step that identifies the explicit and implicit artefacts and applicable constraints. This information is used to generate ASP representations which provide the foundation required for modelling the legal logic. Furthermore, we have achieved the verbalisation of rules built during modelling, and have developed a method of representing artefacts visually which allows logic modelling, model validation and program verification to be visual. During these steps, the original legal document is enhanced with additional embedded information, which results in a tested executable ASP program which can then be used as a smart contract if modifications are made to the blockchain smart contract infrastructure.KeywordsAnswer Set ProgrammingLegal LogicModel ValidationOntologySmart ContractVerbalisationVisualisation

Most universities in Belgium do research in AI and offer AI courses to students. The largest part of the research is into the more classical AI subjects, such as logic programming and theorem proving. However, sub-symbolic AI is also investigated. It seems that all Belgian Universities have their own distinct flavour of research. Below, we present the different Belgian universities in alphabetical order. The Facultes Universitaires Notre Dame de la Paix Namur (University of Namur) has a group called ‘semantics and computational logic’ led by B. Le Charlier and P. Schobbens. This group is theoretically oriented and although their interests are broader than just AI, some of their research is into computational logic, automated theorem proving and formalisation of agentbased systems. The Katholieke Universiteit Leuven (Flemish catholic university of Leuven) has different groups working on artificial-intelligence subjects. First of all, there is the group on declarative languages and artificial intelligence led by M. Bruynooghe. The artificialintelligence interest of this group is into knowledge representation and machine learning, with research themes such as data mining, constraint logic programming, reasoning with time, natural-language processing and learning in agents. They are also involved in the Robocup simulation league. Second, there is a group doing research into intelligent mobile robots led by H. Van Brussel and J. De Schutter. This group is part of the mechanical engineering department. Finally, there is a group at the economy department, led by J. Vanthienen and M. Verhelst, that investigates knowledge systems and modelling. At the Limburgs Universitair Centrum (Limburg University Centre) M. Gyssens of the theoretical computer science group works on the structure and complexity of constraint satisfaction problems. J. Van den Bussche of the same group works on implementation of query languages for data mining. The Universite Catholique de Louvain (French catholic university of Louvain) has several small groups investigating different AI subjects. Computational linguistics is investigated by E. Milgrom and S. Granger. Logic programming is investigated by Y. Deville. A. Thayse investigates natural-language processing, P.J. Courtois investigates self-organising systems, P. Van Hentenryck works on optimisation and constraint satisfaction and Y. Kamp investigates collective intelligence. The Universite de Liege (Montefiore) (University of Liege) has D. Gribomont working on verification methods, automatic theorem proving and formal logic. The Universitaire Instelling Antwerpen (University of Antwerp) has the centre for Dutch language and speech, led by W. Daelemans. Part of the research of this institute is purely linguistic, but a great deal of research is also performed into (memory-based) machine learning of natural language, using large corpora of text, as well as into intelligent processing of text. The Universit Libre de Bruxelles (French Free University of Brussels) has the IRIDIA (Institut de Recherches Interdisciplinaires et de Developpements en Intelligence Artificielle) group led by M. Dorigo and H. Bersini. The main research interests are: ‘belief representation and AI-techniques for process control and classification’ and ‘nature-inspired heuristics for the solution of combinatorial and continuous space optimisation problems’. Sub-symbolic techniques are

Evidence is presented to show that the role of a generative grammar of a natural language is not merely to generate the grammatical sentences of that language, but also to relate them to their logical forms. The notion of logical form is to be made sense of in terms a ‘natural logic’, a logical for natural language, whose goals are to express all concepts capable of being expressed in natural language, to characterize all the valid inferences that can be made in natural language, and to mesh with adequate linguistic descriptions of all natural languages. The latter requirement imposes empirical linguistic constraints on natural logic. A number of examples are discussed.

We study the task of semantic parse correction with natural language feedback. Given a natural language utterance, most semantic parsing systems pose the problem as one-shot translation where the utterance is mapped to a corresponding logical form. In this paper, we investigate a more interactive scenario where humans can further interact with the system by providing free-form natural language feedback to correct the system when it generates an inaccurate interpretation of an initial utterance. We focus on natural language to SQL systems and construct, SPLASH, a dataset of utterances, incorrect SQL interpretations and the corresponding natural language feedback. We compare various reference models for the correction task and show that incorporating such a rich form of feedback can significantly improve the overall semantic parsing accuracy while retaining the flexibility of natural language interaction. While we estimated human correction accuracy is 81.5%, our best model achieves only 25.1%, which leaves a large gap for improvement in future research. SPLASH is publicly available at https://aka.ms/Splash_dataset.

Lawry's label semantics for modeling and computing with linguistic information in natural language provides a clear interpretation of linguistic expressions and thus a transparent model for real-world applications. Meanwhile, annotated logic programs (ALPs) and its fuzzy extension AFLPs have been developed as an extension of classical logic programs offering a powerful computational framework for handling uncertain and imprecise data within logic programs. This paper proposes annotated linguistic logic programs (ALLPs) that embed Lawry's label semantics into the ALP/AFLP syntax, providing a linguistic logic programming formalism for development of automated reasoning systems involving soft data as vague and imprecise concepts occurring frequently in natural language. The syntax of ALLPs is introduced, and their declarative semantics is studied. The ALLP SLD-style proof procedure is then defined and proved to be sound and complete with respect to the declarative semantics of ALLPs. © 2010 Wiley Periodicals, Inc.

This paper describes two approaches to the automatic generation of behavioral VHDL models from descriptions written in natural language. Both approaches are based on a modeling style in which behavior is represented by a system of interconnected processes. The first approach employs a semantic grammar to directly generate a single VHDL process from a paragraph written in a restricted English called ModelSpeak. The second approach accepts more general English and generates models consisting of multiple processes. series of sentences describing the behavior of a single process and generated correct VHDL code for it. This interface requires the behavior be expressed in a highly restricted English called ModelSpeak (10). Conditional expressions (if statements) may be nested to two levels, and interaction is invoked by the interface to resolve scope ambiguity of loops. This interface is implemented with a semantic grammar (3) in Prolog. The second interface (Approach II) to the Modeler's Assistant for natural language is substantially more complex, but accepts more general English expressions and generates multiple-process models. In this approach, each sentence is parsed by a bottom-up, parallel chart parser to generate a set of valid parse trees. The trees are examined by a Semantic Analyzer (7) to determine one which is semantically correct, and a conceptual graph (14) (semantic network) of its meaning is generated. Once the conceptual graph of individual sentences are joined together (12), a program called the Linker (8) analyzes the graph to produce a Process Model Graph and VHDL process code for the Modeler's Assistant. Nodes in the conceptual graph representing verbs typically generate processes or the conditions which govern them. Nodes representing objects generate signals. The modeling style underlying the Linker produces a sizable collection of processes, each consisting of a single if_then statement. To avoid unnecessary bus resolutions, multiple processes assigning to the same signal are merged. Since signal types are generally not expressed in the English description, they may be added interactively later. Before describing the approaches in more detail, related research on natural language interfaces is reviewed briefly.

A problem which confronts the developers of concurrent logic programming (CLP) systems concerns the design of the programming environment, particularly the provision of debugging tools. Debugging tools are useful for many activities besides identifying bugs: they can help in program testing and demonstration, in software experiments, and in teaching the language semantics. For CLP languages the questions of what debugging tools should be provided, and of how they should be used, are still open. Although the languages are closely related to other logic programming and concurrent programming languages, they are sufficiently different that new debugging techniques are required. This paper describes a primarily channel-oriented debugging methodology and a set of debugging tools that we have developed in the light of our experience in using and teaching the CLP language Parlog. With these tools a programmer can test a program by observing communication on channels, opening up a process to examine the activity on internal channels and, if necessary, to check in detail the execution steps of a process. Almost all of the ideas, which are equally applicable to other CLP languages, have been implemented in two commercial Parlog systems. As well as fulfilling the needs of existing users of CLP languages, we believe that the tools emphasise the attractions of CLP as a concurrent programming paradigm, since they are made possible by the unique attributes of CLP.

<b>MRC-NIHR Efficacy and Mechanism Evaluation (EME) Programme Logic Model</b><br /> <br /> Juliana Callaghan&sup1;, Robert Gray&sup1;, Louise Jones&sup2;, Adam Lockwood&sup1;, Danny McAuley&sup3;, John Norrie⁴, Danielle Preedy&sup1;, Sarah Thomas&sup1;, Insa Wemheuer&sup1;<br /> &sup1;National Institute for Health and Care Research (NIHR), School of Healthcare Enterprise and Innovation, University of Southampton, &sup2;UK Research and Innovation (UKRI) Medical Research Council (MRC), &sup3;Queen's University of Belfast, ⁴The University of Edinburgh<br /> February 2023<br /> <br /> The EME Programme is a partnership between the Medical Research Council (MRC) and the National Institute for Health and Care Research (NIHR). The aim of the programme is to improve the health and wealth of the nation by funding clinical efficacy studies that test if an intervention or technology which has shown early promise in patients works as expected in a well-defined group of patients. This may include using such studies to understand treatment mechanisms. The programme plays a key role in translational research by testing novel or repurposed interventions/technologies and determining whether their development should progress to later-phase clinical trials, or inform new earlier-stage research. <a href="https://www.nihr.ac.uk/explore-nihr/funding-programmes/efficacy-and-mechanism-evaluation.htm">More information can be found on the programme page on the NIHR website</a>.<br /> <br /> A logic model is a graphical way to show how an activity, programme or intervention is expected to work and bring about the benefits and changes it intends to achieve. By summarising the core elements, a logic model can then be used to support programme planning, implementation and evaluation. NIHR logic models represent in a linear flow diagram the key activities, outputs, outcomes and impacts of each funding programme as a series of logical steps.<br /> <br /> <br /> <br /> <b>Inputs</b><br /> The first step outlined in the logic model focuses on ‘inputs’, i.e., the resources needed to undertake programme activities. The inputs for the EME Programme are: <ul> <li>MRC and NIHR funding</li> <li>funding from the devolved nations</li> <li>NIHR coordinating centre resources</li> <li>stakeholder time</li> <li>the existing UK research infrastructure in terms of expertise, capacity and funding</li> </ul> <br /> <br /> <b>Activities&nbsp; </b><br /> Inputs feed into activities, the second stage of the logic model. Activities are the actions that NIHR and the funded research community undertake to help achieve the programme’s aims and objectives. Together, inputs and activities represent NIHR’s planned work.<br /> <br /> The initial focus for the programme is identification of key research questions, issues and programme priorities. A NIHR team develops targeted <a href="https://www.nihr.ac.uk/researchers/funding-opportunities/">funding opportunities</a> and activities through horizon scanning and collaboration with key stakeholders to stimulate activity or applications in an area of strategic priority. The programme also invites researcher-led proposals, which enables research into investigator-driven areas. All submitted project proposals are reviewed by harnessing detailed feedback from experts, including academics and other professional experts (for example, clinical staff and health care professionals) as well as patients, carers, service users, specific communities and/or members of the general public. A panel recommends high-quality research projects for funding according to set criteria.<br /> <br /> Funded projects are then actively monitored by a NIHR team that supports the project by providing advice and expertise regarding, for example, risks, appropriate methodologies, stakeholder management or patient and public involvement and engagement.<br /> <br /> NIHR support also includes guidance on the dissemination of research outputs. NIHR supports transparent research management and publication of knowledge, with information on and results of projects being openly accessible via the NIHR <a href="https://fundingawards.nihr.ac.uk/">website</a> and <a href="https://www.journalslibrary.nihr.ac.uk/#/">NIHR Journals Library</a> that hosts the <a href="https://www.journalslibrary.nihr.ac.uk/EME/#/">NIHR EME journal</a>.<br /> <br /> Further, the EME Programme’s application and funding process ensures that funded research projects test interventions/technologies independently and rigorously for efficacy, as well as mechanistic insight. The research process often includes collecting samples, for example in the form of biobanks. By funding projects that follow on from clinical studies supported by the NIHR or other funders, the programme also actively supports researchers in reusing data or samples.<br /> <br /> The EME Programme also builds research capacity through initiatives to broaden, strengthen and upskill the translational research community. &nbsp;For example, the programme provides tailored funding opportunities and activities to support the development of early-career clinical trialists and to stimulate cross-sector and international collaboration.<br /> <br /> <br /> <b>Outputs </b><br /> The next step of the flow diagram focuses on the range of ‘outputs’ that result directly from the undertaken activities. For the EME Programme, these include: <ul> <li>academic outputs such as articles in the <a href="https://www.journalslibrary.nihr.ac.uk/eme/#/">NIHR EME journal</a> and other peer-reviewed journals,</li> <li>communications tailored to key audiences (e.g. patient groups, industry, policy-makers and health-care professionals)</li> <li>study data from the testing of interventions/technologies</li> <li>samples collected as part of the research</li> <li>new intellectual property (IP) being registered as a result of the funded research</li> </ul> <br /> <br /> <br /> <b>Cross-cutting activities</b><br /> Some activities that enable the intended change cut across several steps of the logic model: <ul> <li>Stakeholder collaboration: the programme works closely with stakeholders such as other funders, industrial partners, patients, carers, service users, specific communities and the public to support translational research across the ecosystem</li> <li>Targeted knowledge exchange and dissemination takes place across the project lifecycle</li> <li>Activities, outputs and outcomes produced through research contribute to an increasing pool of knowledge which feeds into and influences both the identification of new questions and methods for answering those questions.</li> </ul> <br /> <br /> <b>Outcomes</b><br /> Outcomes are the changes or benefits the programme intends to produce through its activities and by funding a portfolio of research. Due to the programme’s position on the translational pathway and its role being to inform further research, outcomes are typically seen on a short- to medium-term.<br /> <br /> <b>Scientific advancements</b><br /> Research outputs document and disseminate the scientific advancements made by NIHR-funded projects. Scientific advancements include improved knowledge of the potential health benefits of interventions/ technologies and their mechanisms of action. They also include methodological advancements in the field of clinical trials and mechanistic studies. Improved knowledge can also stimulate further research. For research funded by the EME Programme, this can occur in both directions of the research and development pathway, with, for example, research teams continuing to study the tested interventions/ technologies in later-phase clinical trials or the results stimulating further earlier stage research. This all contributes to the UK’s world-leading reputation in delivering translational health research.<br /> <br /> <b>More efficient and effective health research</b><br /> Developing sample banks and establishing the effectiveness of interventions and technologies results in more efficient and effective health research. For example, trials showing ‘no effect’ of the tested intervention/ technology leads to researchers switching to other approaches, which reduces waste in UK scientific research and increases the efficiency of investments in Phase II and Phase III trials. Involving patients, carers, service users, specific communities and/or members of the general public in all steps of the research process results in increased relevance of research to patient and public need. Increased international and cross-sector collaboration in research enables aligned and complementary research aims to be addressed and optimises delivery of the translational research ecosystem.<br /> <br /> <b>De-risking future investment in research</b><br /> In the medium-term, research funded by the EME programme is expected to result in the de-risking of future investment in research by establishing which interventions and technologies are effective, how they work, and progressing those that are effective for practical use as measured on the Technology Readiness Level (TRL) scale. Increased knowledge around interventions/technologies and their mechanisms of action can also enable increased investment in associated development.<br /> <br /> <b>Enhanced research capacity </b><br /> Research projects funded by the EME Programme also contribute to enhancing research capacity in the form of training and skill-building of project team members through specialist skills and career progression, for example. Working on EME-funded projects is also expected to improve understanding of and interest in translational research for both researchers and trial sites. Collectively, this is expected to result in increased trial activity, capacity and networks, which in turn contributes to international recognition of the UK’s research capabilities.<br /> <br /> <br /> <b>Impacts</b><br /> Impacts, or long-term outcomes, are the anticipated broader (direct and indirect) changes or benefits for organisations, communities, systems and wider society expected to result from the programme's contribution via its activities and portfolio of funded research. These are expected to become apparent in approximately 10-25 years.&nbsp;<br /> For the EME Programme, the overarching long-term benefits are further research and the development of new and repurposed interventions and technologies. These contribute to health benefits through new or repurposed interventions and technologies; improved wealth of the nation through job creation; more effective and efficient use of healthcare resources through targeted investment into promising interventions and technologies; and ultimately, to improving the health and wealth of the nation.&nbsp;<br /> <br /> <br /> <b>Contributions and acknowledgements</b><br /> The NIHR supports the principles of open research, including full and appropriate recognition of the many varied contributions to the creation of knowledge. To support this, we use the<a href="https://credit.niso.org/"> CRediT taxonomy</a> to accurately reflect how each team member has brought their knowledge and skills to the development and delivery of this work. Those that have contributed to this work are listed alphabetically.&nbsp; <ul> <li>Juliana Callaghan:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Writing – review &amp; editing</li> <li>Robert Gray:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Conceptualization, Writing – review &amp; editing&nbsp;</li> <li>Louise Jones:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Conceptualization</li> <li>Adam Lockwood:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Conceptualization, Project administration, Funding acquisition, Methodology, Supervision, Writing – review &amp; editing</li> <li>Danny McAuley:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Conceptualization, Writing – review &amp; editing</li> <li>John Norrie:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Conceptualization, Writing – review &amp; editing</li> <li>Danielle Preedy: &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Conceptualization, Writing – review &amp; editing</li> <li>Sarah Thomas:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Conceptualization, Funding acquisition, Methodology, Supervision</li> <li>Insa Wemheuer:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Project administration, Visualization, Writing – original draft, Writing – review &amp; editing</li> </ul> <br /> In addition, we would specifically like to thank Rebecca Adler, Principal Consultant at NIRAS, and David Salisbury, Independent Consultant and NIRAS Associate, for facilitating the initial development of a Theory of Change for the EME Programme that formed the basis of developing the logic model presented in this document. We would also like to thank the Communications Team at the School of Healthcare Enterprise and Innovation, University of Southampton, for their advice and support in undertaking the visualisation of the model.&nbsp;<br /> <br /> The Logic Model presented in this document also builds on the following document:&nbsp;<br /> <br /> Rentel M C, Simpson K, Dav&eacute; A, Carter S, Blake M, Franke J, <i>et al.</i> A 10-year impact assessment of the Efficacy and Mechanism Evaluation (EME) programme: an independent mixed-method evaluation study. <i>Efficacy Mech Eval</i> 2021;8(20)<br /> <br /> <b>Competing interests&nbsp;</b><br /> This work has been undertaken as part of the delivery of the National Institute for Health and Care Research (NIHR), which is funded by the Department of Health and Social Care. The EME programme is a partnership between the Medical Research Council (MRC) and the NIHR. It is funded by the MRC and the NIHR, with contributions from the CSO in Scotland, Health and Care Research Wales and the HSC R&amp;D Division, Public Health Agency in Northern Ireland. All authors of this document have contributed to it as part of work paid for by the NIHR. No competing interests were disclosed.

In this paper, program logic will be used to 'map out' the planning, development and evaluation of the general practice Pap nurse program in the Australian general practice arena. The incorporation of program logic into the evaluative process supports a greater appreciation of the theoretical assumptions and external influences that underpin general practice Pap nurse activity. The creation of a program logic model is a conscious strategy that results an explicit understanding of the challenges ahead, the resources available and time frames for outcomes. Program logic also enables a recognition that all players in the general practice arena need to be acknowledged by policy makers, bureaucrats and program designers when addressing through policy, issues relating to equity and accessibility of health initiatives. Logic modelling allows decision makers to consider the complexities of causal associations when developing health care proposals and programs. It enables the Pap nurse in general practice program to be represented diagrammatically by linking outcomes (short, medium and long term) with both the program activities and program assumptions. The research methodology used in the evaluation of the Pap nurse in general practice program includes a descriptive study design and the incorporation of program logic, with a retrospective analysis of Australian data from 2001 to 2009. For the purposes of gaining both empirical and contextual data for this paper, a data set analysis and literature review was performed. The application of program logic as an evaluative tool for analysis of the Pap PN incentive program facilitates a greater understanding of complex general practice activity triggers, and also allows this greater understanding to be incorporated into policy to facilitate Pap PN activity, increase general practice cervical smear and ultimately decrease burden of disease.

Reflections on an old problem: That of preserving the logical forms and symmetry