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281,603
Invariance of Optimal Decision Criterion and
, 2004
"... We present a general framework to study the project selection problem in an organization of fallible decisionmakers. We show that when the organizational size and the majority rule for project acceptance are optimized simultaneously, the optimal quality of decisionmaking, as determined by the deci ..."
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by the decision criterion, is invariant, and depends only on the expertise of decisionmakers. This result clarifies that the circumstances under which the decisionmaking quality varies with the organizational structure are situations where the organizational size or majority rule is restricted from reaching
Minimax Regret Decision Criterion
"... Utility elicitation is a critical function of any automated decision aid, allowing decisions to be tailored to the preferences of a specific user. However, the size and complexity of utility functions often precludes full elicitation, requiring that decisions be made without full utility information ..."
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information. Adopting the minimax regret criterion for decision making with incomplete utility information, we describe and empirically compare several new procedures for incremental elicitation of utility functions that attempt to reduce minimax regret with as few questions as possible. Specifically, using
Decision Criterion Learning 1 Toward a Unified Theory of Decision Criterion Learning in Perceptual Categorization
, 2002
"... Optimal decision criterion placement that maximizes expected reward requires sensitivity to the category baserates (prior probabilities) and payoffs (costs and benefits of incorrect and correct responding). When baserates are unequal, human decision criterion is nearly optimal, but when payoffs ar ..."
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Optimal decision criterion placement that maximizes expected reward requires sensitivity to the category baserates (prior probabilities) and payoffs (costs and benefits of incorrect and correct responding). When baserates are unequal, human decision criterion is nearly optimal, but when payoffs
Incremental Utility Elicitation with the Minimax Regret Decision Criterion
 In Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence
, 2003
"... Utility elicitation is a critical function of any automated decision aid, allowing decisions to be tailored to the preferences of a specific user. However, the size and complexity of utility functions often precludes full elicitation, requiring that decisions be made without full utility inform ..."
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Cited by 36 (14 self)
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Utility elicitation is a critical function of any automated decision aid, allowing decisions to be tailored to the preferences of a specific user. However, the size and complexity of utility functions often precludes full elicitation, requiring that decisions be made without full utility
TOWARD A UNIFIED THEORY OF DECISION CRITERION LEARNING IN PERCEPTUAL CATEGORIZATION
 JOURNAL OF THE EXPERIMENTAL ANALYSIS OF BEHAVIOR
, 2002
"... Optimal decision criterion placement maximizes expected reward and requires sensitivity to the category base rates (prior probabilities) and payoffs (costs and benefits of incorrect and correct responding). When base rates are unequal, human decision criterion is nearly optimal, but when payoffs are ..."
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Cited by 33 (13 self)
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Optimal decision criterion placement maximizes expected reward and requires sensitivity to the category base rates (prior probabilities) and payoffs (costs and benefits of incorrect and correct responding). When base rates are unequal, human decision criterion is nearly optimal, but when payoffs
Constraintbased Optimization with the Minimax Decision Criterion
 In Ninth International Conference on Principles and Practice of Constraint Programming
, 2003
"... In many situations, a set of hard constraints encodes the feasible configurations of some system or product over which users have preferences. We consider the problem of computing a best feasible solution when the user's utilities are partially known. Assuming bounds on utilities, efficient ..."
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Cited by 22 (7 self)
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In many situations, a set of hard constraints encodes the feasible configurations of some system or product over which users have preferences. We consider the problem of computing a best feasible solution when the user's utilities are partially known. Assuming bounds on utilities, efficient mixed integer linear programs are devised to compute the solution with minimax regret while exploiting generalized additive structure in a user's utility function.
ConstraintbasedOptimizationwiththeMinimaxDecisionCriterion
"... Abstract. Inmanysituations,asetofhardconstraintsencodesthefeasibleconfigurationsofsomesystemorproductoverwhichusershavepreferences.We considertheproblemofcomputingabestfeasiblesolutionwhentheuser'sutilitiesarepartiallyknown.Assumingboundsonutilities,efficientmixedinteger linearprogramsaredevi ..."
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Abstract. Inmanysituations,asetofhardconstraintsencodesthefeasibleconfigurationsofsomesystemorproductoverwhichusershavepreferences.We considertheproblemofcomputingabestfeasiblesolutionwhentheuser'sutilitiesarepartiallyknown.Assumingboundsonutilities,efficientmixedinteger linearprogramsaredevisedtocomputethesolutionwithminimaxregretwhileexploitinggeneralizedadditivestructureinauser'sutilityfunction. 1Introduction Theproblemofinteractivedecisionmakinghasreceivedafairamountofattentionovertheyears[10,17],butrecentlyhasseenincreasinginterestwithinAIasautomated decisionaidsbecomemoreprevalent.Ashasbeenarguedelsewhere[6,4],therearemanysituationsinwhichthesetofdecisionsandtheirdynamicsarefixed,whilethe utilityfunctions ofdifferentusersvarywidely.Insuchacase,someformofutilityelicitationmustbeundertakeninordertocaptureuserpreferencestoasufficientdegree toallowan(approximately)optimaldecisiontobetaken.Differentapproachestothisproblemhavebeenproposed,includingBayesianmethodsthatquantifyuncertainty aboutpreferencesprobabilistically[7,4],andmethodsthatsimplyposeconstraintsonthesetofpossibleutilityfunctionsandrefinetheseincrementally[17,5,16]. Theseissuesariseaswellinthecontextofconstraintbasedoptimizationproblems.Forinstance,inacarrentalscenario,possibleconfigurationsaredefinedbyattributes suchasautomobilesizeandclass,manufacturer,seatingandluggagecapacity,etc.Availablecarsarelimitedbytheconfigurationsofferedbymanufacturersandstock availability,withhardconstraintsusedtoencodeinfeasibleconfigurations(e.g.,noluxurysedanshave4cylinderengines).Differentcustomershavedifferentpreferencesfor configurationsinthisrestricteddecisionspace[14],andthisinformationmustbeobtainedinaneffectiveway.Typically,categoricalpreferencesareobtainedfromthecustomer,andimposedasconstraints;butifnofeasiblesolutionisfound,theseconstraintsarerelaxedincrementally. WhileinteractivepreferenceelicitationhasreceivedlittleattentionintheCSPcommunity,optimizingwithrespecttoagivensetofpreferencesoverconfigurationshas beenstudiedextensively,withmanyframeworksproposedformodelingsuchsystems
High confidence visual recognition of persons by a test of statistical independence
 IEEE TRANS. ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 1993
"... A method for rapid visual recognition of personal identity is described, based on the failure of a statistical test of independence. The most unique phenotypic feature visible in a person’s face is the detailed texture of each eye’s iris: An estimate of its statistical complexity in a sample of the ..."
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Cited by 621 (8 self)
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imply a theoretical “crossover ” error rate of one in 131000 when a decision criterion is adopted that would equalize the false accept and false reject error rates. In the typical recognition case, given the mean observed degree of iris code agreement, the decision confidence levels correspond formally
K.B.: MultiInterval Discretization of ContinuousValued Attributes for Classication Learning. In:
 IJCAI.
, 1993
"... Abstract Since most realworld applications of classification learning involve continuousvalued attributes, properly addressing the discretization process is an important problem. This paper addresses the use of the entropy minimization heuristic for discretizing the range of a continuousvalued a ..."
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Cited by 832 (7 self)
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formally derive a criterion based on the minimum description length principle for deciding the partitioning of intervals. We demonstrate via empirical evaluation on several realworld data sets that better decision trees are obtained using the new multiinterval algorithm.
Results 1  10
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281,603