11,892 research outputs found

    Quantified Constraints in Twenty Seventeen

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    I present a survey of recent advances in the algorithmic and computational complexity theory of non-Boolean Quantified Constraint Satisfaction Problems, incorporating some more modern research directions

    Desiring under the Proper Guise

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    According to the thesis of the guise of the normative, all desires are associated with normative appearances or judgments. But guise of the normative theories differ sharply over the content of the normative representation, with the two main versions being the guise of reasons and the guise of the good. Chapter 6 defends the comparative thesis that the guise of reasons thesis is more promising than the guise of the good. The central idea is that observations from the theory of content determination can be used in order to constrain possible theories of the representational contents associated with desire. The authors argue that the initially most promising versions of the guise of the good fail to meet these constraints, and then explain the steep challenge confronting any who wish to craft a new guise of the good theory which meets the constraints while also preserving the initial motivations for adopting any guise of the normative theory at all. But a simple version of the guise of reasons not only avoids the troubles besetting the guise of the good but proceeds immediately from a deep diagnosis of the source of its difficulties

    Explanation in constraint satisfaction: A survey

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    Much of the focus on explanation in the field of artificial intelligence has focused on machine learning methods and, in particular, concepts produced by advanced methods such as neural networks and deep learning. However, there has been a long history of explanation generation in the general field of constraint satisfaction, one of the AI's most ubiquitous subfields. In this paper we survey the major seminal papers on the explanation and constraints, as well as some more recent works. The survey sets out to unify many disparate lines of work in areas such as model-based diagnosis, constraint programming, Boolean satisfiability, truth maintenance systems, quantified logics, and related areas

    The design of aircraft using the decision support problem technique

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    The Decision Support Problem Technique for unified design, manufacturing and maintenance is being developed at the Systems Design Laboratory at the University of Houston. This involves the development of a domain-independent method (and the associated software) that can be used to process domain-dependent information and thereby provide support for human judgment. In a computer assisted environment, this support is provided in the form of optimal solutions to Decision Support Problems

    Deregulatory Cost-Benefit Analysis and Regulatory Stability

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    Cost-benefit analysis (“CBA”) has faced significant opposition during most of its tenure as an influential agency decisionmaking tool. As advancements have been made in CBA practice, especially in more complete monetization of relevant effects, CBA has been gaining acceptance as an essential part of reasoned agency decisionmaking. When carefully conducted, CBA promotes transparency and accountability, efficient and predictable policies, and targeted retrospective review. This Article highlights an underappreciated additional effect of extensive use of CBA to support agency rulemaking: reasonable regulatory stability. In particular, a regulation based on a well-supported CBA is more difficult to modify for at least two reasons. The first reason relates to judicial review. Courts take a “hard look” at agency findings of fact, which are summarized in a CBA, and they require justifications when an agency changes course in ways that contradict its previous factfinding. A prior CBA provides a powerful reference point; any updated CBA supporting a new course of action will naturally be compared against the prior CBA, and the agency will need to explain any changes in CBA inputs, assumptions, and methodology. The second reason relates to the nature of CBA. By focusing on the incremental costs and benefits of a proposed change, CBA can make it difficult for an agency to justify changing course, especially when stakeholders have already relied on the prior policy. Together, these forces constrain the range of changes that agencies could rationally support. CBA thus promotes regulatory stability around transparent and increasingly efficient policies. But, admittedly, this CBA-based stabilizing influence gives rise to several objections. This Article responds to, among others, concerns about democratic accountability and, most importantly, the use of alternative methods of policy modification. Overall, the Article concludes that CBA and judicial review of CBA play a desirable role in stabilizing regulatory policy across presidential administrations

    Finding counterfactual explanations through constraint relaxations

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    Interactive constraint systems often suffer from infeasibility (no solution) due to conflicting user constraints. A common approach to recover infeasibility is to eliminate the constraints that cause the conflicts in the system. This approach allows the system to provide an explanation as: "if the user is willing to drop out some of their constraints, there exists a solution". However, one can criticise this form of explanation as not being very informative. A counterfactual explanation is a type of explanation that can provide a basis for the user to recover feasibility by helping them understand which changes can be applied to their existing constraints rather than removing them. This approach has been extensively studied in the machine learning field, but requires a more thorough investigation in the context of constraint satisfaction. We propose an iterative method based on conflict detection and maximal relaxations in over-constrained constraint satisfaction problems to help compute a counterfactual explanation
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