Search results for 'Algorithms' (try it on Scholar)

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  1.  11
    Sven Nyholm & Jilles Smids (forthcoming). The Ethics of Accident-Algorithms for Self-Driving Cars: An Applied Trolley Problem? Ethical Theory and Moral Practice:1-15.
    Self-driving cars hold out the promise of being safer than manually driven cars. Yet they cannot be a 100% safe. Collisions are sometimes unavoidable. So self-driving cars need to be programmed for how they should respond to scenarios where collisions are highly likely or unavoidable. The accident-scenarios self-driving cars might face have recently been likened to the key examples and dilemmas associated with the trolley problem. In this article, we critically examine this tempting analogy. We identify three important ways in (...)
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  2.  9
    Harsh Bhasin & Sushant Mehta (2016). On the Applicability of Diploid Genetic Algorithms. AI and Society:01-10.
    The heuristic search processes like simple genetic algorithms help in achieving optimization but do not guarantee robustness so there is an immediate need of a machine learning technique that also promises robustness. Diploid genetic algorithms ensure consistent results and can therefore replace Simple genetic algorithms in applications such as test data generation and regression testing, where robustness is more important. However, there is a need to review the work that has been done so far in the field. (...)
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  3.  32
    Katja de Vries (2010). Identity, Profiling Algorithms and a World of Ambient Intelligence. Ethics and Information Technology 12 (1):71-85.
    The tendency towards an increasing integration of the informational web into our daily physical world (in particular in so-called Ambient Intelligent technologies which combine ideas derived from the field of Ubiquitous Computing, Intelligent User Interfaces and Ubiquitous Communication) is likely to make the development of successful profiling and personalization algorithms, like the ones currently used by internet companies such as Amazon , even more important than it is today. I argue that the way in which we experience ourselves necessarily (...)
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  4.  10
    M. W. Bunder & R. M. Rizkalla (2009). Proof-Finding Algorithms for Classical and Subclassical Propositional Logics. Notre Dame Journal of Formal Logic 50 (3):261-273.
    The formulas-as-types isomorphism tells us that every proof and theorem, in the intuitionistic implicational logic $H_\rightarrow$, corresponds to a lambda term or combinator and its type. The algorithms of Bunder very efficiently find a lambda term inhabitant, if any, of any given type of $H_\rightarrow$ and of many of its subsystems. In most cases the search procedure has a simple bound based roughly on the length of the formula involved. Computer implementations of some of these procedures were done in (...)
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  5.  5
    Katja Vries (2010). Identity, Profiling Algorithms and a World of Ambient Intelligence. Ethics and Information Technology 12 (1):71-85.
    The tendency towards an increasing integration of the informational web into our daily physical world (in particular in so-called Ambient Intelligent technologies which combine ideas derived from the field of Ubiquitous Computing, Intelligent User Interfaces and Ubiquitous Communication) is likely to make the development of successful profiling and personalization algorithms, like the ones currently used by internet companies such as Amazon, even more important than it is today. I argue that the way in which we experience ourselves necessarily goes (...)
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  6.  3
    Robert Zimmer, Robert Holte & Alan MacDonald (1997). The Impact of Representation on the Efficacy of Artificial Intelligence: The Case of Genetic Algorithms. [REVIEW] AI and Society 11 (1-2):76-87.
    This paper is about representations for Artificial Intelligence systems. All of the results described in it involve engineering the representation to make AI systems more effective. The main AI techniques studied here are varieties of search: path-finding in graphs, and probablilistic searching via simulated annealing and genetic algorithms. The main results are empirical findings about the granularity of representation in implementations of genetic algorithms. We conclude by proposing a new algorithm, called “Long-Term Evolution,” which is a genetic algorithm (...)
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  7.  14
    Jeff Edmonds (2008). How to Think About Algorithms. Cambridge University Press.
    There are many algorithm texts that provide lots of well-polished code and proofs of correctness. Instead, this book presents insights, notations, and analogies to help the novice describe and think about algorithms like an expert. By looking at both the big picture and easy step-by-step methods for developing algorithms, the author helps students avoid the common pitfalls. He stresses paradigms such as loop invariants and recursion to unify a huge range of algorithms into a few meta-algorithms. (...)
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  8.  9
    O. B. Lupanov (ed.) (2005). Stochastic Algorithms: Foundations and Applications: Third International Symposium, Saga 2005, Moscow, Russia, October 20-22, 2005: Proceedings. [REVIEW] Springer.
    This book constitutes the refereed proceedings of the Third International Symposium on Stochastic Algorithms: Foundations and Applications, SAGA 2005, held in Moscow, Russia in October 2005. The 14 revised full papers presented together with 5 invited papers were carefully reviewed and selected for inclusion in the book. The contributed papers included in this volume cover both theoretical as well as applied aspects of stochastic computations whith a special focus on new algorithmic ideas involving stochastic decisions and the design and (...)
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  9.  58
    Rolf Niedermeier (2006). Invitation to Fixed-Parameter Algorithms. Oxford University Press.
    A fixed-parameter is an algorithm that provides an optimal solution to a combinatorial problem. This research-level text is an application-oriented introduction to the growing and highly topical area of the development and analysis of efficient fixed-parameter algorithms for hard problems. The book is divided into three parts: a broad introduction that provides the general philosophy and motivation; followed by coverage of algorithmic methods developed over the years in fixed-parameter algorithmics forming the core of the book; and a discussion of (...)
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  10.  19
    F. Masarani & S. S. Gokturk (1989). On the Existence of Fair Matching Algorithms. Theory and Decision 26 (3):305-322.
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  11.  28
    Luis Rosa (2014). Justification and Cognitive Algorithms. Philosophia 42 (2):503-515.
    In this paper, we offer an alternative interpretation for the claim that ‘S is justified in believing that φ’. First, we present what seems to be a common way of interpreting this claim: as an attribution of propositional justification. According to this interpretation, being justified is just a matter of having confirming evidence. We present a type of case that does not fit well with the standard concept, where considerations about cognition are made relevant. The concept of cognitive algorithm is (...)
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  12.  5
    Freddy Bugge Christiansen & Marcus W. Feldman (1998). Algorithms, Genetics, and Populations: The Schemata Theorem Revisited. Complexity 3 (3):57-64.
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  13.  3
    Florian Eitel, Karl‐Georg Kanz & Arthur Tesche Ma (2000). Do We Face a Fourth Paradigm Shift in Medicine – Algorithms in Education? Journal of Evaluation in Clinical Practice 6 (3):321-333.
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  14.  1
    Ozgen A. Ozbek, Mehmet A. Oktem, Guliz Dogan & Yusuf H. Abacioglu (2004). Application of Hepatitis Serology Testing Algorithms to Assess Inappropriate Laboratory Utilization. Journal of Evaluation in Clinical Practice 10 (4):519-523.
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  15.  6
    V. Rybakov (2008). Discrete Linear Temporal Logic with Current Time Point Clusters, Deciding Algorithms. Logic and Logical Philosophy 17 (1-2):143-161.
    The paper studies the logic TL(NBox+-wC) – logic of discrete linear time with current time point clusters. Its language uses modalities Diamond+ (possible in future) and Diamond- (possible in past) and special temporal operations, – Box+w (weakly necessary in future) and Box-w (weakly necessary in past). We proceed by developing an algorithm recognizing theorems of TL(NBox+-wC), so we prove that TL(NBox+-wC) is decidable. The algorithm is based on reduction of formulas to inference rules and converting the rules in special reduced (...)
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  16.  5
    David E. Goldberg, Kumara Sastry & Xavier Llorà (2007). Toward Routine Billion‐Variable Optimization Using Genetic Algorithms. Complexity 12 (3):27-29.
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  17.  1
    W. Teed Rockwell (2013). Algorithms and Stories. Human Affairs 23 (4):633-644.
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  18. S. I. Adi͡an (ed.) (1977). Mathematical Logic, the Theory of Algorithms, and the Theory of Sets. American Mathematical Society.
  19. Ljiljana Brankovic, Yuqing Lin & Bill Smyth (eds.) (2008). Proceedings of the International Workshop on Combinatorial Algorithms, 2007. College Publications.
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  20. A. P. Ershov & Donald Ervin Knuth (eds.) (1981). Algorithms in Modern Mathematics and Computer Science: Proceedings, Urgench, Uzbek Ssr, September 16-22, 1979. Springer-Verlag.
  21.  7
    M. Y. Kao (ed.) (2007). Encyclopedia of Algorithms. Springer.
    The online edition supplements this index with hyperlinks as well as including internal hyperlinks to related entries in the text, CrossRef citations, and links ...
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  22. Donald Ervin Knuth (2010). Selected Papers on Design of Algorithms. Center for the Study of Language and Information.
  23. S. J. Koopman (2008). Statistical Algorithms for Models in State Space Form: Ssfpack 3. Timberlake Consultants.
     
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  24. S. J. Koopman (2008). Statistical Algorithms for Models in State Space Form: Ssfpack 3. Timberlake Consultants.
     
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  25. Robert R. Korfhage (1966). Logic and Algorithms. New York, Wiley.
     
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  26. Ljiljana Brankovic, Yuqing Lin & Bill Smyth (eds.) (2008). Proceedings of the International Workshop on Combinatorial Algorithms, 2007. College Publications.
     
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  27. André Thayse (1984). P-Functions and Boolean Matrix Factorization a Unified Approach for Wired, Programmed, and Microprogrammed Implementations of Discrete Algorithms.
     
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  28. Carol E. Cleland (2001). Recipes, Algorithms, and Programs. Minds and Machines 11 (2):219-237.
    In the technical literature of computer science, the concept of an effective procedure is closely associated with the notion of an instruction that precisely specifies an action. Turing machine instructions are held up as providing paragons of instructions that "precisely describe" or "well define" the actions they prescribe. Numerical algorithms and computer programs are judged effective just insofar as they are thought to be translatable into Turing machine programs. Nontechnical procedures (e.g., recipes, methods) are summarily dismissed as ineffective on (...)
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  29.  36
    Franz Baader & Ulrike Sattler (2001). An Overview of Tableau Algorithms for Description Logics. Studia Logica 69 (1):5-40.
    Description logics are a family of knowledge representation formalisms that are descended from semantic networks and frames via the system Kl-one. During the last decade, it has been shown that the important reasoning problems (like subsumption and satisfiability) in a great variety of description logics can be decided using tableau-like algorithms. This is not very surprising since description logics have turned out to be closely related to propositional modal logics and logics of programs (such as propositional dynamic logic), for (...)
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  30.  19
    Andreas Tolias, Prediction on Spike Data Using Kernel Algorithms.
    We report and compare the performance of different learning algorithms based on data from cortical recordings. The task is to predict the orientation of visual stimuli from the activity of a population of simultaneously recorded neurons. We compare several ways of improving the coding of the input (i.e., the spike data) as well as of the output (i.e., the orientation), and report the results obtained using different kernel algorithms.
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  31.  34
    David Freedman & Paul Humphreys (1999). Are There Algorithms That Discover Causal Structure? Synthese 121 (1-2):29-54.
    There have been many efforts to infer causation from association byusing statistical models. Algorithms for automating this processare a more recent innovation. In Humphreys and Freedman[(1996) British Journal for the Philosophy of Science 47, 113–123] we showed that one such approach, by Spirtes et al., was fatally flawed. Here we put our arguments in a broader context and reply to Korb and Wallace [(1997) British Journal for thePhilosophy of Science 48, 543–553] and to Spirtes et al.[(1997) British Journal for (...)
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  32.  24
    Nikos Logothetis, Prediction on Spike Data Using Kernel Algorithms.
    We report and compare the performance of different learning algorithms based on data from cortical recordings. The task is to predict the orientation of visual stimuli from the activity of a population of simultaneously recorded neurons. We compare several ways of improving the coding of the input (i.e., the spike data) as well as of the output (i.e., the orientation), and report the results obtained using different kernel algorithms.
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  33.  35
    D. Fallis (2000). The Reliability of Randomized Algorithms. British Journal for the Philosophy of Science 51 (2):255-271.
    Recently, certain philosophers of mathematics (Fallis [1997]; Womack and Farach [(1997]) have argued that there are no epistemic considerations that should stop mathematicians from using probabilistic methods to establish that mathematical propositions are true. However, mathematicians clearly should not use methods that are unreliable. Unfortunately, due to the fact that randomized algorithms are not really random in practice, there is reason to doubt their reliability. In this paper, I analyze the prospects for establishing that randomized algorithms are reliable. (...)
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  34.  22
    Andreas Blass, Nachum Dershowitz & Yuri Gurevich (2009). When Are Two Algorithms the Same? Bulletin of Symbolic Logic 15 (2):145-168.
    People usually regard algorithms as more abstract than the programs that implement them. The natural way to formalize this idea is that algorithms are equivalence classes of programs with respect to a suitable equivalence relation. We argue that no such equivalence relation exists.
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  35.  8
    James H. Fetzer (1994). Mental Algorithms: Are Minds Computational Systems? Pragmatics and Cognition 21 (1):1-29.
    The idea that human thought requires the execution of mental algorithms provides a foundation for research programs in cognitive science, which are largely based upon the computational conception of language and mentality. Consideration is given to recent work by Penrose, Searle, and Cleland, who supply various grounds for disputing computationalism. These grounds in turn qualify as reasons for preferring a non-computational, semiotic approach, which can account for them as predictable manifestations of a more adquate conception. Thinking does not ordinarily (...)
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  36.  33
    Jaakko Kuorikoski & Samuli Pöyhönen (2013). Understanding Nonmodular Functionality: Lessons From Genetic Algorithms. Philosophy of Science 80 (5):637-649.
    Evolution is often characterized as a tinkerer that creates efficient but messy solutions to problems. We analyze the nature of the problems that arise when we try to explain and understand cognitive phenomena created by this haphazard design process. We present a theory of explanation and understanding and apply it to a case problem – solutions generated by genetic algorithms. By analyzing the nature of solutions that genetic algorithms present to computational problems, we show that the reason for (...)
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  37.  2
    Danièle Beauquier & Anatol Slissenko (2001). A First Order Logic for Specification of Timed Algorithms: Basic Properties and a Decidable Class. Annals of Pure and Applied Logic 113 (1-3):13-52.
    We consider one aspect of the problem of specification and verification of reactive real-time systems which involve operations and constraints concerning time. Time is continuous what is motivated by specifications of hybrid systems. Our goal is to try to find a framework that is based on applied first order logic that permits to represent the verification problem directly, completely and conservatively , and that is apt to describe interesting decidable classes, maybe showing way to feasible algorithms. To achieve this (...)
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  38.  3
    Thomas Ehrhard (1996). Projecting Sequential Algorithms on Strongly Stable Functions. Annals of Pure and Applied Logic 77 (3):201-244.
    We relate two sequential models of PCF: the sequential algorithm model due to Berry and Curien and the strongly stable model due to Bucciarelli and the author. More precisely, we show that all the morphisms araising in the strongly stable model of PCF are sequential in the sense that they are the “extensional projections” of some sequential algorithms. We define a model of PCF where morphisms are “extensional” sequential algorithms and prove that any equation between PCF terms which (...)
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  39.  42
    Murray Clarke (1996). Darwinian Algorithms and Indexical Representation. Philosophy of Science 63 (1):27-48.
    In this paper, I argue that accurate indexical representations have been crucial for the survival and reproduction of homo sapiens sapiens. Specifically, I want to suggest that reliable processes have been selected for because of their indirect, but close, connection to true belief during the Pleistocene hunter-gatherer period of our ancestral history. True beliefs are not heritable, reliable processes are heritable. Those reliable processes connected with reasoning take the form of Darwinian Algorithms: a plethora of specialized, domain-specific inference rules (...)
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  40.  30
    Ron Sun, Integrating Reinforcement Learning, Bidding and Genetic Algorithms.
    This paper presents a GA-based multi-agent reinforce- ment learning bidding approach (GMARLB) for perform- ing multi-agent reinforcement learning. GMARLB inte- grates reinforcement learning, bidding and genetic algo- rithms. The general idea of our multi-agent systems is as follows: There are a number of individual agents in a team, each agent of the team has two modules: Q module and CQ module. Each agent can select actions to be performed at each step, which are done by the Q module. While the (...)
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  41.  19
    Martin Peterson (2011). Is There an Ethics of Algorithms? Ethics and Information Technology 13 (3):251-260.
    We argue that some algorithms are value-laden, and that two or more persons who accept different value-judgments may have a rational reason to design such algorithms differently. We exemplify our claim by discussing a set of algorithms used in medical image analysis: In these algorithms it is often necessary to set certain thresholds for whether e.g. a cell should count as diseased or not, and the chosen threshold will partly depend on the software designer’s preference between (...)
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  42.  20
    Joseph Ramsey & Clark Glymour, Experiments on the Accuracy of Algorithms for Inferring the Structure of Genetic Regulatory Networks From Microarray Expression Levels.
    After reviewing theoretical reasons for doubting that machine learning methods can accurately infer gene regulatory networks from microarray data, we test 10 algorithms on simulated data from the sea urchin network, and on microarray data for yeast compared with recent experimental determinations of the regulatory network in the same yeast species. Our results agree with the theoretical arguments: most algorithms are at chance for determining the existence of a regulatory connection between gene pairs, and the algorithms that (...)
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  43.  7
    Hermann Wagner & Dirk Kautz (1998). Evolutionary Conservation and Ontogenetic Emergence of Neural Algorithms. Behavioral and Brain Sciences 21 (2):285-286.
    Neural algorithms are conserved during evolution. Neurons with different shapes and using different molecular mechanisms can perform the same computation. However, evolutionary conservation of neural algorithms is not sufficient for claiming the realization of an algorithm for a specific computational problem. A plausible scheme for ontogenetic emergence of the structure of the algorithm must also be provided.
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  44.  13
    Helena Rasiowa (1981). On Logic of Complex Algorithms. Studia Logica 40 (3):289 - 310.
    An algebraic approach to programs called recursive coroutines — due to Janicki [3] — is based on the idea to consider certain complex algorithms as algebraics models of those programs. Complex algorithms are generalizations of pushdown algorithms being algebraic models of recursive procedures (see Mazurkiewicz [4]). LCA — logic of complex algorithms — was formulated in [11]. It formalizes algorithmic properties of a class of deterministic programs called here complex recursive ones or interacting stacks-programs, for which (...)
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  45.  23
    Richard E. Korf (1995). Heuristic Evaluation Functions in Artificial Intelligence Search Algorithms. Minds and Machines 5 (4):489-498.
    We consider a special case of heuristics, namely numeric heuristic evaluation functions, and their use in artificial intelligence search algorithms. The problems they are applied to fall into three general classes: single-agent path-finding problems, two-player games, and constraint-satisfaction problems. In a single-agent path-finding problem, such as the Fifteen Puzzle or the travelling salesman problem, a single agent searches for a shortest path from an initial state to a goal state. Two-player games, such as chess and checkers, involve an adversarial (...)
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  46.  19
    Jan van Eijck, Formal Specification with Alloy: Specification of Algorithms.
    Overview • Alloy peculiarity • Alloy utilities • Assignments and pre- and postconditions in Alloy • Alloy for automated logical reasoning • Alloy specifications of algorithms • On your to do list: – Look through the example code in these slides, – make sure you understand what is happening. Note: Alloy Peculiarity..
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  47.  5
    Shane Harwood & Richard Scheines, Learning Linear Causal Structure Equation Models with Genetic Algorithms.
    Shane Harwood and Richard Scheines. Learning Linear Causal Structure Equation Models with Genetic Algorithms.
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  48. Gerd Gigerenzer (1996). The Psychology of Good Judgment Frequency Formats and Simple Algorithms. Medical Decision Making 16 (3):273-280.
    Mind and environment evolve in tandem—almost a platitude. Much of judgment and decision making research, however, has compared cognition to standard statistical models, rather than to how well it is adapted to its environment. The author argues two points. First, cognitive algorithms are tuned to certain information formats, most likely to those that humans have encountered during their evolutionary history. In par ticular, Bayesian computations are simpler when the information is in a frequency format than when it is in (...)
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  49.  2
    Dan Klein & Christopher D. Manning, Interpreting and Extending Classical Agglomerative Clustering Algorithms Using a Model-Based Approach.
    erative clustering. First, we show formally that the common heuristic agglomerative clustering algorithms – Ward’s method, single-link, complete-link, and a variant of group-average – are each equivalent to a hierarchical model-based method. This interpretation gives a theoretical explanation of the empirical behavior of these algorithms, as well as a principled approach to resolving practical issues, such as number of clusters or the choice of method. Second, we show how a model-based viewpoint can suggest variations on these basic agglomerative (...)
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  50.  2
    S. Luan, L. Magnani & G. Dai (2006). Algorithms for Computing Minimal Conflicts. Logic Journal of the IGPL 14 (2):391--406.
    In this paper we present some algorithms for computing minimal conflicts. First of all we discuss the relationship between minimal conflicts and minimally inconsistent subsets. Then we introduce an algorithm for computing all minimally inconsistent subsets, which is applied to generating all minimal conflicts. Furthermore, an algorithm for computing all minimal conflicts using structured description is introduced, and its correctness is proved; its time complexity is also shown. The algorithm using structured description terminates in polynomial time for some special (...)
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