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Many problems arising in computational social choice are of high computational complexity, and some are located at higher levels of the Polynomial Hierarchy. We argue that a parameterized complexity analysis provides valuable insight into the factors contributing to the complexity of these problems , and can lead to practically useful algorithms. As a case(More)
Planning is a notoriously difficult computational problem of high worst-case complexity. Researchers have been investing significant efforts to develop heuristics or restrictions to make planning practically feasible. Case-based planning is a heuris-tic approach where one tries to reuse previous experience when solving similar problems in order to avoid(More)
Not all NP-complete problems share the same practical hardness with respect to exact computation. Whereas some NP-complete problems are amenable to efficient computational methods, others are yet to show any such sign. It becomes a major challenge to develop a theoretical framework that is more fine-grained than the theory of NP-completeness, and that can(More)
We review several different languages for collective decision making problems, in which agents express their judgments, opinions, or beliefs over elements of a logically structured domain. Several such languages have been proposed in the literature to compactly represent the questions on which the agents are asked to give their views. In particular, the(More)
Planning is an important AI task that gives rise to many hard problems. In order to come up with efficient algorithms for this setting, it is important to understand the sources of complexity. For planning problems that are beyond NP, identifying fragments that allow an efficient reduction to SAT can be a feasible approach due to the great performance of(More)
We consider the two-sided stable matching setting in which there may be uncertainty about the agents' preferences due to limited information or communication. We consider three models of uncertainty: (1) lottery model — in which for each agent, there is a probability distribution over linear preferences, (2) compact indifference model — for each agent, a(More)