Many Important problems In Artificial Intelligence can be defined as Constralnt SATISFACTION Problems (CSP). These types of problems are defined by a limited set of variables, each having a limited domain and a number of Constralnts on the values of those variables (these problems are also called Consistent Labelling Problems (CLP), in which "Labeling" nuans assigning a value to a variable.) Solution to these problems is a set of unique values for variables such that all the problem constralnts are satisfied. Several search algorithms have been proposed for solving these problems, som of which reduce the need for bacJctracklng by doing some sort of looking to future, and produce more efficient solutions. These are the so-called Forward Checking (FC), Partialiy Lookahead (PL), and Fully Lookahead (FL) algorithms. They are different In terms of the amount of looking to the future, number of backtracks thaJ are performed, and the quality of the solution that they find. In this paper, wepropose a new search algorithm we call Modified Fully Lookahead (MFL) which is Shown to be more efficient than the original Fully Lookahead algorithm

Knowledge graphs are widely used tools in the field of reasoning, where reasoning is facilitated through link prediction within the knowledge graph. However, traditional methods have limitations, such as high complexity or an inability to effectively capture the structural features of the graph. The main challenge lies in simultaneously handling both the structural and similarity features of the graph. In this study, we employ a CONSTRAINT SATISFACTION approach, where each proposed link must satisfy both structural and similarity CONSTRAINTs. For this purpose, each CONSTRAINT is considered from a specific perspective, referred to as a view. Each view computes a probability score using a GRU-RNN, which satisfies its own predefined CONSTRAINT. In the first CONSTRAINT, the proposed node must have a probability of over 0.5 with frontier nodes. The second CONSTRAINT computes the Bayesian graph, and the proposed node must have a link in the Bayesian graph. The last CONSTRAINT requires that a proposed node must fall within an acceptable fault. This allows for N-N relationships to be accurately determined, while also addressing the limitations of embedding. The results of the experiments showed that the proposed method improved performance on two standard datasets.

The present study aimed to provide a comprarative analysis of the performance of Persian monoligual and Azari-Persian bilingual adolescents in comprehension of Persian proverbs. The study has been made on the basis of the CONSTRAINT SATISFACTION MODEL, within which the effect of the variables of "linguistic context", "familiarity" and "gender" is examined on their "speed of comprehension". The corpus includes 142 high school students in two groups of monolingual and bilingual individuals. The proverb comprehension test has been provided as a testing software in which the data are saved in the textual format and the response timing is saved in milliseconds. Data analysis was performed by a two-way analysis of variance. The research findings illustrated the significant effect of the variables studied, that by itself supports the efficiency of the CONSTRAINT SATISFACTION MODEL, as its theoretical base, in the comprehension of Persian proverbs.

In this paper an uncertain multi objective closed-loop supply chain is developed. The first objective function is maximizing the total profit. The second objective function is minimizing the use of row materials. In the other word, the second objective function is maximizing the amount of remanufacturing and recycling. Genetic algorithm is used for optimization; and for finding the pareto optimal line, Epsilon-CONSTRAINT method is used. Finally a numerical example is solved with proposed approach and performance of the MODEL is evaluated in different sizes. The results show that this approach is effective and useful for managerial decisions.

This paper presents a hybrid scheduling technique for generating the predictive schedules of passenger trains. The algorithm, which represents a combination of simulated annealing and a CONSTRAINT-based heuristic, has been designed using an object-oriented methodology and is suitable for a primarily single-track railway with some double-track sections. The search process gets started from a good initial solution created by the scheduling heuristic and continues, according to the simulated annealing search control strategy. The heuristic is also used in the neighborhood exploration process. This hybrid approach solves the problem in a short span of time. Simulation experiments, with the real data of manual timetables and two corridors of Iran's railway, show the superiority of the hybrid method to the heuristic designed and the manual system, in terms of the three performance measures used.

University course timetabling problem is a challenging and time-consuming task on the overall structure of timetable in every academic environment. The problem deals with many factors such as the number of lessons, classes, teachers, students and working time, and these are influenced by some hard and soft CONSTRAINTs. The aim of solving this problem is to assign courses and classes to teachers and students, so that the restrictions are held. In this paper, a CONSTRAINT programming method is proposed to satisfy maximum CONSTRAINTs and expectation, in order to address university timetabling problem. For minimizing the penalty of soft CONSTRAINTs, a cost function is introduced and AHP method is used for calculating its coefficients. The proposed MODEL is tested on department of management, University of Isfahan dataset using OPL on the IBM ILOG CPLEX Optimization Studio platform. A statistical analysis has been conducted and shows the performance of the proposed approach in satisfying all hard CONSTRAINTs and also the satisfying degree of the soft CONSTRAINTs is on maximum desirable level. The running time of the MODEL is less than 20 minutes that is significantly better than the non-automated ones.