3,354 research outputs found

    Complexity of Nested Circumscription and Nested Abnormality Theories

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    The need for a circumscriptive formalism that allows for simple yet elegant modular problem representation has led Lifschitz (AIJ, 1995) to introduce nested abnormality theories (NATs) as a tool for modular knowledge representation, tailored for applying circumscription to minimize exceptional circumstances. Abstracting from this particular objective, we propose L_{CIRC}, which is an extension of generic propositional circumscription by allowing propositional combinations and nesting of circumscriptive theories. As shown, NATs are naturally embedded into this language, and are in fact of equal expressive capability. We then analyze the complexity of L_{CIRC} and NATs, and in particular the effect of nesting. The latter is found to be a source of complexity, which climbs the Polynomial Hierarchy as the nesting depth increases and reaches PSPACE-completeness in the general case. We also identify meaningful syntactic fragments of NATs which have lower complexity. In particular, we show that the generalization of Horn circumscription in the NAT framework remains CONP-complete, and that Horn NATs without fixed letters can be efficiently transformed into an equivalent Horn CNF, which implies polynomial solvability of principal reasoning tasks. Finally, we also study extensions of NATs and briefly address the complexity in the first-order case. Our results give insight into the ``cost'' of using L_{CIRC} (resp. NATs) as a host language for expressing other formalisms such as action theories, narratives, or spatial theories.Comment: A preliminary abstract of this paper appeared in Proc. Seventeenth International Joint Conference on Artificial Intelligence (IJCAI-01), pages 169--174. Morgan Kaufmann, 200

    Approaching the boundary problem: Self-determination, inclusion, and the unpuzzling of transboundary conflicts

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    In recent decades, decisionmakers have increasingly faced conflicts juxtaposing demands for self-determination and inclusion. Political theorists term this juxtaposition “the boundary problem.” They have offered normative solutions, especially for “just inclusion,” proposing what states owe to exogenous individuals like migrants and refugees. Meanwhile, as I show, legal scholars have developed parallel observations regarding what I term “just exclusion,” concerning how self-determination by sub-state collectives, such as minority nations, interacts with the inclusion rights of members of the majority. I make, first, a descriptive contribution, showing decisionmakers how political theories of “just inclusion” and legal theories of “just exclusion” are complementary, uniting to frame the boundary problem. Second, I make a prescriptive contribution, deploying this frame to lay out a stepwise approach so decisionmakers can more logically work through boundary-problem conflicts

    Explaining Explanations in AI

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    Recent work on interpretability in machine learning and AI has focused on the building of simplified models that approximate the true criteria used to make decisions. These models are a useful pedagogical device for teaching trained professionals how to predict what decisions will be made by the complex system, and most importantly how the system might break. However, when considering any such model it’s important to remember Box’s maxim that "All models are wrong but some are useful." We focus on the distinction between these models and explanations in philosophy and sociology. These models can be understood as a "do it yourself kit" for explanations, allowing a practitioner to directly answer "what if questions" or generate contrastive explanations without external assistance. Although a valuable ability, giving these models as explanations appears more difficult than necessary, and other forms of explanation may not have the same trade-offs. We contrast the different schools of thought on what makes an explanation, and suggest that machine learning might benefit from viewing the problem more broadly

    A Review on the Cognitive Neuroscience of Autism

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    With increased recognition in the media, heightened prevalence, and advances in research technologies, investigation into the causes of autism has broadened in recent years. Studies at the molecular, structural, and behavioral levels have resulted in significant findings, linking autism to qualitative differences in neurological function and an alteration of early development. Familial aggregation of autism demonstrate a strong genetic factor, although genetics can not completely account for its pathogenesis. Studies show autism having one of the most complex pathologies among neurodevelopmental disorders. Future studies applying sophisticated methodologies in new areas may shed light on current mysteries surrounding the disorder
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