4th International Conference, HAIS 2009, Salamanca, Spain, June 10-12, 2009. Proceedings

This volume constitutes the refereed proceedings of the 4th International Workshop on Hybrid Artificial Intelligence Systems, HAIS 2009, held in Salamanca, Spain, in June 2009. The 85 papers presented, were carefully reviewed and selected from 206 submissions. The topics covered are agents and multi agents systems, HAIS applications, cluster analysis, data mining and knowledge discovery, evolutionary computation, learning algorithms, real world HAIS applications and data uncertainty, hybrid artificial intelligence in bioinformatics, evolutionary multiobjective machine learning, hybrid reasoning and coordination methods on multi-agent systems, methods of classifiers fusion, knowledge extraction based on evolutionary learning, hybrid systems based on bioinspired algorithms and argumentation methods, hybrid evolutionry intelligence in financial engineering

Diagnosis of breast cancer using elastic-scattering spectroscopy: preliminary clinical results

We report on the first stages of a clinical study designed to test elastic-scattering spectroscopy, mediated by fiberoptic probes, for three specific clinical applications in breast-tissue diagnosis: (1) a transdermal-needle (interstitial) measurement for instant diagnosis with minimal invasiveness similar to fine-needle aspiration but with sensitivity to a larger tissue volume, (2) a hand-held diagnostic probe for use in assessing tumor/resection margins during open surgery, and (3) use of the same probe for real-time assessment of the `sentinel' node during surgery to determine the presence or absence of tumor (metastatic). Preliminary results from in vivo measurements on 31 women are encouraging. Optical spectra were measured on 72 histology sites in breast tissue, and 54 histology sites in sentinel nodes. Two different artificial intelligence methods of spectral classification were studied. Artificial neural networks yielded sensitivities of 69% and 58%, and specificities of 85% and 93%, for breast tissue and sentinel nodes, respectively. Hierarchical cluster analysis yielded sensitivities of 67% and 91%, and specificities of 79% and 77%, for breast tissue and sentinel nodes, respectively. These values are expected to improve as the data sets continue to grow and more sophisticated data preprocessing is employed. The study will enroll up to 400 patients over the next two years

Allocation in Practice

How do we allocate scarcere sources? How do we fairly allocate costs? These are two pressing challenges facing society today. I discuss two recent projects at NICTA concerning resource and cost allocation. In the first, we have been working with FoodBank Local, a social startup working in collaboration with food bank charities around the world to optimise the logistics of collecting and distributing donated food. Before we can distribute this food, we must decide how to allocate it to different charities and food kitchens. This gives rise to a fair division problem with several new dimensions, rarely considered in the literature. In the second, we have been looking at cost allocation within the distribution network of a large multinational company. This also has several new dimensions rarely considered in the literature.Comment: To appear in Proc. of 37th edition of the German Conference on Artificial Intelligence (KI 2014), Springer LNC

Parameterized Algorithmics for Computational Social Choice: Nine Research Challenges

Computational Social Choice is an interdisciplinary research area involving Economics, Political Science, and Social Science on the one side, and Mathematics and Computer Science (including Artificial Intelligence and Multiagent Systems) on the other side. Typical computational problems studied in this field include the vulnerability of voting procedures against attacks, or preference aggregation in multi-agent systems. Parameterized Algorithmics is a subfield of Theoretical Computer Science seeking to exploit meaningful problem-specific parameters in order to identify tractable special cases of in general computationally hard problems. In this paper, we propose nine of our favorite research challenges concerning the parameterized complexity of problems appearing in this context

Artificial Intelligence in Radiotherapy Treatment Planning: Present and Future.

Treatment planning is an essential step of the radiotherapy workflow. It has become more sophisticated over the past couple of decades with the help of computer science, enabling planners to design highly complex radiotherapy plans to minimize the normal tissue damage while persevering sufficient tumor control. As a result, treatment planning has become more labor intensive, requiring hours or even days of planner effort to optimize an individual patient case in a trial-and-error fashion. More recently, artificial intelligence has been utilized to automate and improve various aspects of medical science. For radiotherapy treatment planning, many algorithms have been developed to better support planners. These algorithms focus on automating the planning process and/or optimizing dosimetric trade-offs, and they have already made great impact on improving treatment planning efficiency and plan quality consistency. In this review, the smart planning tools in current clinical use are summarized in 3 main categories: automated rule implementation and reasoning, modeling of prior knowledge in clinical practice, and multicriteria optimization. Novel artificial intelligence-based treatment planning applications, such as deep learning-based algorithms and emerging research directions, are also reviewed. Finally, the challenges of artificial intelligence-based treatment planning are discussed for future works