The accountability of transnational armed groups under international law

Terrorist attacks by transnational armed groups cause on average 15,000 deaths every year worldwide, with the law enforcement agencies of some states facing many challenges in bringing those responsible to justice. Despite various attempts to codify the law on transnational terrorism since the 1930s, a crime of transnational terrorism under International Law remains contested, reflecting concerns regarding the relative importance of prosecuting members of transnational armed groups before the International Criminal Court. However, a study of the emerging jurisprudence of the International Criminal Court suggests that terrorist attacks cannot be classified as a war crime or a crime against humanity. Therefore, using organisational network theory, this thesis will probe the limits of international criminal law in bringing members of transnational armed groups to justice in the context of changing methods of warfare. Determining the organisational structure of transnational armed groups, provides a powerful analytical framework for examining the challenges in holding members of transnational armed groups accountable before the International Criminal Court, in the context of the relationship between the commanders and the subordinate members of the group.

Identifying sources of global contention in constraint satisfaction search

Much work has been done on learning from failure in search to boost solving of combinatorial problems, such as clause-learning and clause-weighting in boolean satisfiability (SAT), nogood and explanation-based learning, and constraint weighting in constraint satisfaction problems (CSPs). Many of the top solvers in SAT use clause learning to good effect. A similar approach (nogood learning) has not had as large an impact in CSPs. Constraint weighting is a less fine-grained approach where the information learnt gives an approximation as to which variables may be the sources of greatest contention. In this work we present two methods for learning from search using restarts, in order to identify these critical variables prior to solving. Both methods are based on the conflict-directed heuristic (weighted-degree heuristic) introduced by Boussemart et al. and are aimed at producing a better-informed version of the heuristic by gathering information through restarting and probing of the search space prior to solving, while minimizing the overhead of these restarts. We further examine the impact of different sampling strategies and different measurements of contention, and assess different restarting strategies for the heuristic. Finally, two applications for constraint weighting are considered in detail: dynamic constraint satisfaction problems and unary resource scheduling problems.