Using Artificial Intelligence to provide Intelligent Dispute Resolution Support.

In this article, we review the use of Artificial Intelligence to provide intelligent dispute resolution support. In the early years there was little systematic development of such systems. Rather a number of ad hoc systems were developed. The focus of these systems was upon the technology being utilised, rather than user needs. Following a review of historic systems, we focus upon what are the important components of intelligent Online Dispute Resolution systems. Arising from this review, we develop an initial model for constructing user centric intelligent Online Dispute Resolution systems. Such a model integrates Case management, Triaging, Advisory tools, Communication tools, Decision Support Tools and Drafting software. No single dispute is likely to require all six processes to resolve the issue at stake. However, the development of such a hybrid ODR system would be very significant important starting point for expanding into a world where Artificial Intelligence is gainfully used.

Performance analysis and prediction in triathlon.

Performance in triathlon is dependent upon factors that include somatotype, physiological capacity, technical proficiency and race strategy. Given the multidisciplinary nature of triathlon and the interaction between each of the three race components, the identification of target split times that can be used to inform the design of training plans and race pacing strategies is a complex task. The present study uses machine learning techniques to analyse a large database of performances in Olympic distance triathlons (2008-2012). The analysis reveals patterns of performance in five components of triathlon (three race "legs" and two transitions) and the complex relationships between performance in each component and overall performance in a race. The results provide three perspectives on the relationship between performance in each component of triathlon and the final placing in a race. These perspectives allow the identification of target split times that are required to achieve a certain final place in a race and the opportunity to make evidence-based decisions about race tactics in order to optimise performance.

Modelling and analysing track cycling Omnium performances using statistical and machine learning techniques.

This article describes the utilisation of an unsupervised machine learning technique and statistical approaches (e.g., the Kolmogorov-Smirnov test) that assist cycling experts in the crucial decision-making processes for athlete selection, training, and strategic planning in the track cycling Omnium. The Omnium is a multi-event competition that will be included in the summer Olympic Games for the first time in 2012. Presently, selectors and cycling coaches make decisions based on experience and intuition. They rarely have access to objective data. We analysed both the old five-event (first raced internationally in 2007) and new six-event (first raced internationally in 2011) Omniums and found that the addition of the elimination race component to the Omnium has, contrary to expectations, not favoured track endurance riders. We analysed the Omnium data and also determined the inter-relationships between different individual events as well as between those events and the final standings of riders. In further analysis, we found that there is no maximum ranking (poorest performance) in each individual event that riders can afford whilst still winning a medal. We also found the required times for riders to finish the timed components that are necessary for medal winning. The results of this study consider the scoring system of the Omnium and inform decision-making toward successful participation in future major Omnium competitions.