Dietary lipid supplements in ruminant diets, whether from various natural sources or supplements such as rumen protected lipids, have a long history and are widely used. The main reasons for using these supplements include an increased energy density of dairy cow diets, e. g. in the early stage of lactation (Clapperton and Steele, 1983). Moreover, reproductive parameters might are affected as well by using lipid supplements, either indirectly by changes in the energy balance of cows or directly due to the effects of certain fatty acids (FA) on reproductive organs and processes (Leroy et al., 2014).
Since the recognition of knowledge as essential for intelligent systems in the 1970s and early 1980s, the efficient acquisition of knowledge has been a key research focus. Initially, expert systems concentrated on creating a suitable knowledge base by eliciting information from experts before deployment. Over time, alternative methods emerged, including incremental approaches that allowed for the initial construction of a provisional knowledge base, which could be refined through practical use. Additionally, some approaches aimed for fully automatic knowledge base construction using machine-learning techniques. Recently, there has been growing interest in ontology construction, particularly in creating reusable ontologies that can be shared across various users and domains. The Pacific Knowledge Acquisition Workshops (PKAW) have established a tradition of providing a platform for researchers to share innovative ideas on this topic, attracting participants globally, especially from the Pacific Rim region. PKAW is part of a series of international workshops on knowledge acquisition held in the Pacific Rim, Canada, and Europe over the past two decades. The previous workshop, PKAW 2004, focused on incremental knowledge acquisition, machine learning, neural networks, and data mining.
This comprehensive collection features invited papers and regular contributions that explore various innovative approaches in artificial intelligence and data mining. Topics include algorithms for collaborative discovery from distributed information sources, training support vector machines, and projects like the Robot Scientist and Arrowsmith. The long papers delve into practical algorithms for pattern-based linear regression, named entity recognition for the Indonesian language, and bias management in Bayesian network classifiers.
Further studies analyze literature-based discoveries, cross-language mining for acronyms, and frequent pattern mining in large databases. The work also covers movement analysis of Medaka fish using decision trees, support vector inductive logic programming, and measuring over-generalization in biosequences.
Regular papers discuss automatic extraction of proteins from biological texts, evaluating interestingness measures, and distributed clustering using probabilistic models. Project reports highlight self-generation of control rules for water treatment, semantic enrichment of data tables for food risk assessment, and knowledge discovery through visualization techniques.
The collection emphasizes advancements in machine learning, classification methods, and text mining, showcasing the intersection of AI with various scientific domains, particularly in biomedical research and data analysis.
This book presents a new methodological analysis of two competing research paradigms of artificial intelligence and cognitive science-the symbolic versus the connectionist paradigms. Providing an accessible introduction to the fundamentals of both paradigms, the book derives new objectives for future research that will help to integrate aspects of both areas to obtain more powerful AI techniques and to promote a deeper understanding of cognition.
