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UA Open Access Publishing Fund / Background: Ethnobotanical knowledge continues to be important for treating illness in many rural communities, despite access to health care clinics and pharmaceuticals. However, access to health care clinics and other modern services can have an impact on the distribution of medical ethnobotanical knowledge. Many factors have been shown to be associated with distributions in this type of knowledge. The goal of the sub-analyses reported in this paper was to better understand the relationship between herbal remedy knowledge, and two such factors, age and social network position, among the Yucatec Maya in Tabi, Yucatan. Methods: The sample consisted of 116 Yucatec Maya adults. Cultural consensus analysis was used to measure variation in herbal remedy knowledge using competence scores, which is a measure of participant agreement within a domain. Social network analysis was used to measure individual position within a network using in-degree scores, based on the number of people who asked an individual about herbal remedies. Surveys were used to capture relevant personal attributes, including age. Results: Analysis revealed a significant positive correlation between age and the herbal medicine competence score for individuals 45 and under, and no relationship for individuals over 45. There was an insignificant relationship between in-degree and competence scores for individuals 50 and under and a significant positive correlation for those over 50. Conclusions: There are two possible mechanisms that could account for the differences between cohorts: 1) knowledge accumulation over time; and/or 2) the stunting of knowledge acquisition through delayed acquisition, competing treatment options, and changes in values. Primary ethnographic evidence suggests that both mechanisms may be at play in Tabi. Future studies using longitudinal or cross-site comparisons are necessary to determine the whether and how the second mechanism is influencing the different cohorts.

Case-Based Reasoning (CBR) is an appealing technique for developing intelligent systems. Besides its psycho- logical plausibility and a substantial body of research during recent years, building a good CBR system remains still a difficult task. The main problems remaining are the development of suitable case retrieval and adaptation mechanisms for CBR. The major issues are how and when to capture the necessary knowledge for both of the above aspects. As a contribution to knowledge this thesis proposes a new approach to address the experienced difficulties. The basic framework of Ripple Down Rules (RDR) is extended to allow the incremental development of a knowledge base for each of the two functions: case retrieval and case adaptation, during the use of the system while solving actual problems. The proposed approach allows an expert-user to provide explanations of why, for a given problem, certain actions should be taken. Incrementally knowledge is acquired from the expert-user in which the expert refines a rule which performs unsatisfactorily for a current given problem. The approach facilitates both, the rule acquisition as well as its validation. As a result the knowledge maintenance task of a knowledge engineer is overcome. This approach is effective with respect to both, the development of highly tailored and complex retrieval and adaptation functions for CBR as well as the provision of an intuitive and feasible approach for the expert. The approach has been implemented in a CBR system named MIKAS (Menu Construction using Incre- mental Knowledge Acquisition Systems) for the design of menus (diets) according to dietary requirements. The experimental evidence indicates the suitability of the approach to address the retrieval and adaptation problems of the menu construction domain. The experimental evidence also indicates that the difficulties of developing retrieval and adaptation functions for CBR can be effectively overcome by the proposed new approach. It is expected that the approach is likely to be useful in other problem solving domains where expert intervention is Required to modify a solution.

Knowledge Based Systems (KBS) have been actively investigated since the early period of AI. There are four common methods of building expert systems: modeling approaches, programming approaches, case-based approaches and machine-learning approaches. One particular technique is Ripple Down Rules (RDR) which may be classified as an incremental case-based approach. Knowledge needs to be acquired from experts in the context of individual cases viewed by them. In the RDR framework, the expert adds a new rule based on the context of an individual case. This task is simple and only affects the expert???s workflow minimally. The rule added fixes an incorrect interpretation made by the KBS but with minimal impact on the KBS's previous correct performance. This provides incremental improvement. Despite these strengths of RDR, there are some limitations including rule redundancy, lack of intermediate features and lack of models. This thesis addresses these RDR limitations by applying automatic learning algorithms to reorganize the knowledge base, to learn intermediate features and possibly to discover domain models. The redundancy problem occurs because rules created in particular contexts which should have more general application. We address this limitation by reorganizing the knowledge base and removing redundant rules. Removal of redundant rules should also reduce the number of future knowledge acquisition sessions. Intermediate features improve modularity, because the expert can deal with features in groups rather than individually. In addition to the manual creation of intermediate features for RDR, we propose the automated discovery of intermediate features to speed up the knowledge acquisition process by generalizing existing rules. Finally, the Ripple Down Rules approach facilitates rapid knowledge acquisition as it can be initialized with a minimal ontology. Despite minimal modeling, we propose that a more developed knowledge model can be extracted from an existing RDR KBS. This may be useful in using RDR KBS for other applications. The most useful of these three developments was the automated discovery of intermediate features. This made a significant difference to the number of knowledge acquisition sessions required.

Knowledge Based Systems (KBS) have been actively investigated since the early period of AI. There are four common methods of building expert systems: modeling approaches, programming approaches, case-based approaches and machine-learning approaches. One particular technique is Ripple Down Rules (RDR) which may be classified as an incremental case-based approach. Knowledge needs to be acquired from experts in the context of individual cases viewed by them. In the RDR framework, the expert adds a new rule based on the context of an individual case. This task is simple and only affects the expert???s workflow minimally. The rule added fixes an incorrect interpretation made by the KBS but with minimal impact on the KBS's previous correct performance. This provides incremental improvement. Despite these strengths of RDR, there are some limitations including rule redundancy, lack of intermediate features and lack of models. This thesis addresses these RDR limitations by applying automatic learning algorithms to reorganize the knowledge base, to learn intermediate features and possibly to discover domain models. The redundancy problem occurs because rules created in particular contexts which should have more general application. We address this limitation by reorganizing the knowledge base and removing redundant rules. Removal of redundant rules should also reduce the number of future knowledge acquisition sessions. Intermediate features improve modularity, because the expert can deal with features in groups rather than individually. In addition to the manual creation of intermediate features for RDR, we propose the automated discovery of intermediate features to speed up the knowledge acquisition process by generalizing existing rules. Finally, the Ripple Down Rules approach facilitates rapid knowledge acquisition as it can be initialized with a minimal ontology. Despite minimal modeling, we propose that a more developed knowledge model can be extracted from an existing RDR KBS. This may be useful in using RDR KBS for other applications. The most useful of these three developments was the automated discovery of intermediate features. This made a significant difference to the number of knowledge acquisition sessions required.

Thesis (Ph. D.)--University of New South Wales, 2003. / Also available online.

Thesis (Ph. D.)--University of Washington, 2004. / Vita. Includes bibliographical references (p. 120-133).

Thesis (Ph. D.)--University of Texas at Austin, 1999. / Vita. Includes bibliographical references (leaves 141-147). Available also in a digital version from Dissertation Abstracts.

Kyoto University (京都大学) / 0048 / 新制・課程博士 / 博士(工学) / 甲第15664号 / 工博第3322号 / 新制||工||1501(附属図書館) / 28201 / 京都大学大学院工学研究科機械理工学専攻 / (主査)教授 椹木 哲夫, 教授 松原 厚, 教授 西脇 眞二 / 学位規則第4条第1項該当

Knowledge Based System (KBS) development is a difficult and challenging task, in particular in knowledge intensive domains. The traditional view of knowledge engineering is one of mining experts' knowledge and somehow transforming it into a machine usable form. This process, in general, suffers from insufficient or misconstrued representation of experts' problem solving behaviour. It is also unstructured and unduly biased at an early stage by design and implementation issues - normally in the form of incremental prototyping. We believe that both knowledge acquisition and KBS development for real life applications will require a 'structured' approach. This approach should harness a KBS developer's ability in extracting knowledge and developing systems. The structure should also be sufficiently flexible to allow the knowledge engineer to use his sense of creativity in developing a KBS. This thesis puts forward such a structured approach, in which KBS development is carried out in an engineering fashion. A process in which the worker is provided with an environment for developing knowledge based systems as an engineering process, as opposed to that of an artform or crafting. The main emphasis of this work is that part of the process which deals with the analysis and design phases in developing KBS. The analysis is performed at an 'epistemological' level, not coloured by design or implementation issues. The output of this phase captures both an expert's problem solving capability, and the business constraints placed upon the intended system. This is then used by the design process in order to create an optimal, workable, and elegant design architecture for the ultimate system.