The 2nd International Timetabling Competition (ITC2007) was announced on the 1st August 2007. Building on the success of the first, this competition aimed to further develop interest in the area of educational timetabling while providing researchers with models of the problems faced which incorporate an increased number of real world constraints. A main objective of the competition was that conclusions drawn would further stimulate debate within the widening timetabling research community. The overall aim of the competition was to create better understanding between researchers and practitioners by allowing emerging techniques to be trialed and tested on real world models of timetabling problems. The competition was divided into three tracks to reflect the important variations which exist within educational timetabling within Higher Education. As these formulations incroporate an increased number of 'real world' issues, it is anticipated that the competition will set the research agenda within the field. After finishing on the 25th January 2008, final results of the competition are to be made available in May 2008. Along with background to the competition, the tracks are described here together with initial results for the datasets released.

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Terminological knowledge representation systems (TKRSs) are tools for designing and using knowledge bases that make use of terminological languages (or concept languages). We analyze from a theoretical point of view a TKRS whose capabilities go beyond the ones of presently available TKRSs. The new features studied, often required in practical applications, can be summarized in three main points. First, we consider a highly expressive terminological language, called ALCNR, including general complements of concepts, number restrictions and role conjunction. Second, we allow to express inclusion statements between general concepts, and terminological cycles as a particular case. Third, we prove the decidability of a number of desirable TKRS-deduction services (like satis ability, subsumption and instance checking) through a sound, complete and terminating calculus for reasoning in ALCNR-knowledge bases. Our calculus extends the general technique of constraint systems. As a byproduct of the proof, we get also the result that inclusion statements in ALCNR can be simulated by terminological cycles, if descriptive semantics is adopted.

We study the process of multi-agent reinforcement learning in the context of load balancing in a distributed system, without use of either central coordination or explicit communication. We rst de ne a precise framework in which to study adaptive load balancing, important features of which are its stochastic nature and the purely local information available to individual agents. Given this framework, we show illuminating results on the interplay b e t ween basic adaptive behavior parameters and their e ect on system e ciency. We then investigate the properties of adaptive load balancing in heterogeneous populations, and address the issue of exploration vs. exploitation in that context. Finally, w e show that naive use of communication may not improve, and might e v en harm system e ciency.