Randy Goebel

Norman Foo

This volume constitutes the refereed proceedings of the 4th Pacific Rim International Conference on Artificial Intelligence, PRICAI '96, held in Cairns, Queensland, Australia in August 1996. The 56 revised full papers included in the book were carefully selected for presentation at the conference from a total of 175 submissions. The topics covered are machine learning, interactive systems, knowledge representation, reasoning about change, neural nets and uncertainty, natural language, constraint satisfaction and optimization, qualitative reasoning, automated deduction, nonmonotonic reasoning, intelligent agents, planning, and pattern recognition.

4th Pacific Rim International Conference on Artificial Intelligence, Cairns, Australia, August 26 - 30, 1996, Proceedings

PRICAI '96: Topics in Artificial Intelligence

David Poole

Alan K. Mackworth

David I. Poole

David Lynton Poole

RANDY AUTOR GOEBEL

Alan Mackworth

Provides an integrated introduction to artificial intelligence. Develops AI representation schemes and describes their uses for diverse applications, from autonomous robots to diagnostic assistants to infobots. DLC: Artificial intelligence.

A Logical Approach

Computational Intelligence

Andreas Holzinger

Michael Mengel

Heimo Müller

Data driven Artificial Intelligence (AI) and Machine Learning (ML) in digital pathology, radiology, and dermatology is very promising. In specific cases, for example, Deep Learning (DL), even exceeding human performance. However, in the context of medicine it is important for a human expert to verify the outcome. Consequently, there is a need for transparency and re-traceability of state-of-the-art solutions to make them usable for ethical responsible medical decision support. Moreover, big data is required for training, covering a wide spectrum of a variety of human diseases in different organ systems. These data sets must meet top-quality and regulatory criteria and must be well annotated for ML at patient-, sample-, and image-level. Here biobanks play a central and future role in providing large collections of high-quality, well-annotated samples and data. The main challenges are finding biobanks containing ''fit-for-purpose samples, providing quality related meta-data, gaining access to standardized medical data and annotations, and mass scanning of whole slides including efficient data management solutions.

State-of-the-Art and Future Challenges

Artificial Intelligence and Machine Learning for Digital Pathology

Massimo Ferri

Vasile Palade

The BIRS Workshop “Advances in Interactive Knowledge Discovery and Data Mining in Complex and Big Data Sets” (15w2181), held in July 2015 in Banff, Canada, was dedicated to stimulating a cross-domain integrative machine-learning approach and appraisal of “hot topics” toward tackling the grand challenge of reaching a level of useful and useable computational intelligence with a focus on real-world problems, such as in the health domain. This encompasses learning from prior data, extracting and discovering knowledge, generalizing the results, fighting the curse of dimensionality, and ultimately disentangling the underlying explanatory factors in complex data, i.e., to make sense of data within the context of the application domain. The workshop aimed to contribute advancements in promising novel areas such as at the intersection of machine learning and topological data analysis. History has shown that most often the overlapping areas at intersections of seemingly disparate fields are key for the stimulation of new insights and further advances. This is particularly true for the extremely broad field of machine learning.

BIRS Workshop, Banff, AB, Canada, July 24-26, 2015, Revised Selected Papers

Towards Integrative Machine Learning and Knowledge Extraction

Randy Goebel

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