Results 1  10
of
115
Utility Models for GoalDirected DecisionTheoretic Planners
 Computational Intelligence
, 1993
"... AI planning agents are goaldirected: success is measured in terms of whether or not an input goal is satisfied, and the agent's computational processes are driven by those goals. A decisiontheoretic agent, on the other hand, has no explicit goals success is measured in terms of its preferences ..."
Abstract

Cited by 95 (10 self)
 Add to MetaCart
AI planning agents are goaldirected: success is measured in terms of whether or not an input goal is satisfied, and the agent's computational processes are driven by those goals. A decisiontheoretic agent, on the other hand, has no explicit goals success is measured in terms of its preferences or a utility function that respects those preferences. The two approaches have complementary strengths and weaknesses. Symbolic planning provides a computational theory of plan generation, but under unrealistic assumptions: perfect information about and control over the world and a restrictive model of actions and goals. Decision theory provides a normative model of choice under uncertainty, but offers no guidance as to how the planning options are to be generated. This paper unifies the two approaches to planning by describing utility models that support rational decision making while retaining the goal information needed to support plan generation. We develop an extended model of goals tha...
Scheduling as a Fuzzy Multiple Criteria Optimization Problem
, 1994
"... Realworld scheduling is decision making under vague constraints of different importance, often using uncertain data, where compromises between antagonistic criteria are allowed. We explain in theory and by detailed examples a new combination of fuzzy set based constraints and repair based heuristi ..."
Abstract

Cited by 42 (12 self)
 Add to MetaCart
Realworld scheduling is decision making under vague constraints of different importance, often using uncertain data, where compromises between antagonistic criteria are allowed. We explain in theory and by detailed examples a new combination of fuzzy set based constraints and repair based heuristics that help to model these scheduling problems. We simplify the mathematics needed for a method of eliciting the criteria's importances from human experts. We introduce a new consistency test for configuration changes. This test also helps to evaluate the sensitivity to configuration changes. We describe the implementation of these concepts in our fuzzy constraint library ConFLIP++ and in our heuristic repair library D'ej`aVu. Finally, we present results from scheduling a continuous caster unit in a steel plant.
Optimization under uncertainty: Stateoftheart and opportunities
 Computers and Chemical Engineering
, 2004
"... A large number of problems in production planning and scheduling, location, transportation, finance, and engineering design require that decisions be made in the presence of uncertainty. Uncertainty, for instance, governs the prices of fuels, the availability of electricity, and the demand for chemi ..."
Abstract

Cited by 41 (0 self)
 Add to MetaCart
A large number of problems in production planning and scheduling, location, transportation, finance, and engineering design require that decisions be made in the presence of uncertainty. Uncertainty, for instance, governs the prices of fuels, the availability of electricity, and the demand for chemicals. A key difficulty in optimization under uncertainty is in dealing with an uncertainty space that is huge and frequently leads to very largescale optimization models. Decisionmaking under uncertainty is often further complicated by the presence of integer decision variables to model logical and other discrete decisions in a multiperiod or multistage setting. This paper reviews theory and methodology that have been developed to cope with the complexity of optimization problems under uncertainty. We discuss and contrast the classical recoursebased stochastic programming, robust stochastic programming, probabilistic (chanceconstraint) programming, fuzzy programming, and stochastic dynamic programming. The advantages and shortcomings of these models are reviewed and illustrated through examples. Applications and the stateoftheart in computations are also reviewed. Finally, we discuss several main areas for future development in this field. These include development of polynomialtime approximation schemes for multistage stochastic programs and the application of global optimization algorithms to twostage and chanceconstraint formulations.
A decision theoretic framework for approximating concepts
 International Journal of Manmachine Studies
, 1992
"... This paper explores the implications of approximating a concept based on the Bayesian decision procedure, which provides a plausible unification of the fuzzy set and rough set approaches for approximating a concept. We show that if a given concept is approximated by one set, the same result given by ..."
Abstract

Cited by 36 (20 self)
 Add to MetaCart
This paper explores the implications of approximating a concept based on the Bayesian decision procedure, which provides a plausible unification of the fuzzy set and rough set approaches for approximating a concept. We show that if a given concept is approximated by one set, the same result given by the αcut in the fuzzy set theory is obtained. On the other hand, if a given concept is approximated by two sets, we can derive both the algebraic and probabilistic rough set approximations. Moreover, based on the well known principle of maximum (minimum) entropy, we give a useful interpretation of fuzzy intersection and union. Our results enhance the understanding and broaden the applications of both fuzzy and rough sets. 1.
Imprecision in Engineering Design
 ASME JOURNAL OF MECHANICAL DESIGN
, 1995
"... Methods for incorporating imprecision in engineering design decisionmaking are briefly reviewed and compared. A tutorial is presented on the Method of Imprecision (MoI), a formal method, based on the mathematics of fuzzy sets, for representing and manipulating imprecision in engineering design. The ..."
Abstract

Cited by 33 (6 self)
 Add to MetaCart
Methods for incorporating imprecision in engineering design decisionmaking are briefly reviewed and compared. A tutorial is presented on the Method of Imprecision (MoI), a formal method, based on the mathematics of fuzzy sets, for representing and manipulating imprecision in engineering design. The results of a design cost estimation example, utilizing a new informal cost specification, are presented. The MoI can provide formal information upon which to base decisions during preliminary engineering design and can facilitate setbased concurrent design.
The Interpretation of Fuzziness
 IEEE Transactions on Systems, Man, and Cybernetics
, 1996
"... From laserscanned data to feature human model: a system based on ..."
Abstract

Cited by 25 (13 self)
 Add to MetaCart
From laserscanned data to feature human model: a system based on
Aggregation Functions for Engineering Design Tradeoffs
, 1998
"... The choice of an aggregation function is a common problem in Multi Attribute Decision Making (MADM) systems. The Method of Imprecision (MoI) is a formal theory for the manipulation of preliminary design information that represents preferences among design alternatives with the mathematics of fuzzy s ..."
Abstract

Cited by 25 (14 self)
 Add to MetaCart
The choice of an aggregation function is a common problem in Multi Attribute Decision Making (MADM) systems. The Method of Imprecision (MoI) is a formal theory for the manipulation of preliminary design information that represents preferences among design alternatives with the mathematics of fuzzy sets. The MoI formulates the preliminary design problem as a MADM problem. To date, two aggregation functions have been developed for the MoI, one representing a compensating strategy and one a noncompensating strategy. Much of the prior fuzzy sets research on aggregation functions has been inappropriate for application to engineering design. In this paper, the selection of an aggregation function for MADM schemes is discussed within the context of the MoI. The general restrictions on designappropriate aggregation functions are outlined, and a family of functions, modeling a range of tradeoff strategies, is presented. The results are illustrated with an example.
What nonlinearity to choose? Mathematical foundations of fuzzy control
 Proceedings of the 1992 International Conference on Fuzzy Systems and Intelligent Control
, 1992
"... Abstract. Fuzzy control is a very successful way to transform the expert’s knowledge of the type “if the velocity is big and the distance from the object is small, hit the brakes and decelerate as fast as possible ” into an actual control. To apply this transformation one must: 1) choose fuzzy varia ..."
Abstract

Cited by 25 (18 self)
 Add to MetaCart
Abstract. Fuzzy control is a very successful way to transform the expert’s knowledge of the type “if the velocity is big and the distance from the object is small, hit the brakes and decelerate as fast as possible ” into an actual control. To apply this transformation one must: 1) choose fuzzy variables corresponding to words like “small”, “big”; 2) choose operations corresponding to “and ” and “or”; 3) choose a method that transforms the resulting fuzzy variable for a into a single value ā. The wrong choice can drastically affect the quality of the resulting control, so the problem of choosing the right procedure is very important. From mathematical viewpoint these choice problems correspond to nonlinear optimization and are therefore extremely difficult. We develop a new mathematical formalism (based on group theory) that allows us to solve the problem of optimal choice and thus: 1) explain why the existing choices are really the best (in some situations); 2) explain a rather mysterious fact that the fuzzy control based on the experts’ knowledge is often better than the control by these same experts; 3) give choice recommendations for the cases when traditional choices do not work. Perspectives of space applications will be also discussed.
Refinements of the Maximin Approach to DecisionMaking in Fuzzy Environment
 Fuzzy Sets and Systems
"... : The most popular approach to decisionmaking in the setting of fuzzy sets is the maximin ranking of solutions. This method is natural when interpreting the fuzzy sets as flexible constraints that cannot compensate with one another. However the obtained ranking of solutions is very coarse. Two kind ..."
Abstract

Cited by 20 (8 self)
 Add to MetaCart
: The most popular approach to decisionmaking in the setting of fuzzy sets is the maximin ranking of solutions. This method is natural when interpreting the fuzzy sets as flexible constraints that cannot compensate with one another. However the obtained ranking of solutions is very coarse. Two kinds of refinements to this ordering are introduced: a partial ordering according to the least satisfied discriminating constraint, and a lexicographical ranking. The latter refines the former and combines utilitarist and egalitarist points of view on the aggregation of feasibility degrees. These orderings are characterized in several ways and their representation by means of two place numerical functions is studied. Dual refinements of the maximax ranking are provided. KEYWORDS: Fuzzy constraints, maximin problems, vectormaximization, partial ordering, leximin ordering. 1. Introduction In their seminal paper, Bellman and Zadeh [2] established a link between fuzzy set theory and multiple cr...