This research proposes a Preemptive Possibilistic Linear Programming (PPLP) approach for solving multiobjective Aggregate Production Planning (APP) problem with interval demand and imprecise unit price and related operating costs. The proposed approach attempts to maximize profit and minimize changes of workforce. It transforms the total profit objective that has imprecise information to three ...

Intention recognition has significant applications in ambient intelligence, assisted living and care of the elderly, games and intrusion and other crime detection. In this chapter we explore an approach to intention recognition based on clustering. To this end we show how to map the intention recognition problem into a clustering problem. We then use three different clustering algorithms, Fuzzy...

Possibilistic Defeasible Logic Programming (P-DeLP) is an argumentation framework based on logic programming which incorporates a treatment of possibilistic uncertainty at object-language level. In P-DeLP, the closure of justified conclusions is not always consistent, which has been detected to be an anomaly in the context of so-called rationality postulates for rule-based argumentation systems...

Possibilistic Defeasible Logic Programming (P-DeLP) is an argumentation framework based on logic programming which incorporates a treatment of possibilistic uncertainty at object-language level. In P-DeLP, the closure of justified conclusions is not always consistent, which has been detected to be an anomaly in the context of so-called rationality postulates for rule-based argumentation systems...

The effects of uncertainties on the strength of a single lap shear joint are explained. Probabilistic and possibilistic methods are used to account for uncertainties. Linear and geometrically nonlinear finite element analyses are used in the studies. To evaluate the strength of the joint, fracture in the adhesive and material strength failure in the strap are considered. The study shows that li...

Possibilistic networks are important tools for dealing with uncertain pieces of information. For multiplyconnected networks, it is well known that the inference process is a hard problem. This paper studies a new representation of possibilistic networks, called hybrid possibilistic networks. The uncertainty is no longer represented by local conditional possibility distributions, but by their co...

Khatter (Soft Comput 24:6847–16,867, 2020) pointed out that although several approaches are proposed in the literature to solve single-valued neutrosophic linear programming problems (SVNLPPS) (linear which all parameters except decision variables either represented by triangular numbers (SVTNNS) or trapezoidal (SVTrNNS)). However, methods for comparing (SVNNS), used existing approaches, indepe...