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Mathematical texts can be computerised in many ways that capture differing amounts of the mathematical meaning. At one end, there is document imaging, which captures the arrangement of black marks on paper, while at the other end there are proof assistants (e.g., Mizar, Isabelle, Coq, etc.), which capture the full mathematical meaning and have proofs expressed in a formal foundation of mathematics. In between, there are computer typesetting systems (e.g., LTEX and Presentation MathML) and semantically oriented systems (e.g., Content MathML, OpenMath, OMDoc, etc.). In this paper we advocate a style of computerisation of mathematical texts which is flexible enough to connect the different approaches to computerisation, which allows various degrees of formalisation, and which is compatible with different logical frameworks (e.g., set theory, category theory, type theory, etc.) and proof systems. The basic idea is to allow a man-machine collaboration which weaves human input with machine computation at every step in the way. We propose that the huge step from informal mathematics to fully formalised mathematics be divided into smaller steps, each of which is a fully developed method in which human input is minimal. Let us consider the following two questions

In this article a method is proposed for ball tracking using 100 Hz computer vision in a semi-automated foosball table. In this application the behavior of the ball is highly dynamic with speeds up to 10 m/s and frequent bounces occur against the sides of the table and the puppets. Moreover, in the overhead camera view of the field the ball is often fully or partially occluded and there are other objects present that resemble the ball. The table is semi-automated to enable single user game play. This article shows that it is possible to perform fast and robust ball tracking by combining efficient image processing algorithms with a priori knowledge of the stationary environment and position information of the automated rods.

Model checking of temporal logic has already been proposed for automatic planning. In this paper, we introduce a simple adaptation of the ATL model checking algorithm that returns a strategy to achieve given goal. We point out that the algorithm generalizes minimaxing, and that ATL models generalize traditional game trees. The paper ends with suggestions about other game theory concepts that can be transfered to ATL-based planning. Keywords: Multi-agent systems, multi-agent planning, model checking.