Stochastic Satisfiability Modulo Theories (SSMT) is a quantitative extension of Satisfiability Modulo Theories (SMT) inspired by stochastic logics. It extends SMT by randomized quantifiers, facilitating capture of stochastic game properties in the logic, like reachability analysis of hybrid-state Markov decision processes. Solving SSMT formulae with quantification over finite and thus discrete ...

The first order theory of the Diagonalizable Algebra of Peano Arithmetic (DA(PA)) represents a natural fragment of provability logic with propositional quantifiers. We prove that the first order theory of the O-generated subalgebra of DA(PA) is decidable but not elementary recursive; the same theory, enriched by a single free variable ranging over DA(PA), is already undecidable. This gives a ne...

We present in this paper a first-order axiomatization of an extended theory T of finite or infinite trees, built on a signature containing an infinite set of function symbols and a relation finite(t) which enables to distinguish between finite or infinite trees. We show that T has at least one model and prove its completeness by giving not only a decision procedure, but a full first-order const...

Over the last decade, first-order constraints have been efficiently used in the artificial intelligence world to model many kinds of complex problems such as: scheduling, resource allocation, computer graphics and bio-informatics. Recently, a new property called decomposability has been introduced and many first-order theories have been proved to be decomposable: finite or infinite trees, ratio...

The main purpose of this paper is to develop a new optimum output quantity decision analysis of a duopoly market under a fuzzy decision environment. To efficiently handle the fuzziness of the decision variables, the linguistic values, subjectively represented by the trapezoidal fuzzy numbers, are used to act as the evaluation tool of decision variables such as fixed cost and unit variable cost....

Decision-making is a complex process that is largely studied from an experimental perspective or in specific organizational contexts. As such, no generalizable framework exists with which to study decision-making from an individual differences perspective for predictive/selection purposes. By generalising a context-specific decision model proposed by Koriat & Goldsmith (1996), the focus of this...

In this paper, I propose that the availability of a bound variable reading for pronouns is predictable from their morphological structure of the pronouns. More specifically I argue that noun-containing pronouns cannot be bound variables. My proposal is different from D&W's (2002) argument in that in their theory, every DPs cannot have a bound variable reading, while in my theory, even DPs can h...

the concept of decision making, factors influencing it and the related outcomes have always attracted researchers. the purpose of this study is to investigate the effect of sacred values on decision making, decision difficulty and the resulting negative emotions. thus, a sample of 288 students from the university of guilan were chosen as subjects and were required to complete a standard questio...

Linear programming (LP) is the most widely used optimization technique for solving real-life problems because of its simplicity and efficiency. Although conventional LP models require precise data, managers and decision makers dealing with real-world optimization problems often do not have access to exact values. Fuzzy sets have been used in the fuzzy LP (FLP) problems to deal with the imprecis...

Selection of effective input variables on decision making or forecasting problems, is one of the most important dilemmas in forecasting and decision making field. Due to research and problem constraints, we can not use all of known variables for forecasting or decision making in real world applications. Thus, in decision making problems or system simulations, we are trying to select important a...