The BDI model, as a practical reasoning architecture aims at making decisions about what to do based on cognitives notions as beliefs, desires and intentions. However, during the decision making process, BDI agents also have to make background decisions like choosing what intention to achieve next from a set of possibly conflicting desires; which plan to execute from among the plans that satisf...

The purpose of the current research is to provide a performance appraisal system capable of considering the value chain network structure of research and development (R&D) projects for Complex products and systems (CoPS) under uncertainty of data. Therefore, in order to achieve this goal, a network data envelopment analysis (NDEA) approach and the possibilistic programming to provide a new fuzz...

Possibility theory offers either a qualitative, or a numerical framework for representing uncertainty, in terms of dual measures of pos­ sibility and necessity. This leads to the ex­ istence of two kinds of possibilistic causal graphs where the conditioning is either based on the minimum, or on the product opera­ tor. Benferhat et al. [3] have investigated the connections between min-based grap...

به دلیل عدم قطعیت‌های شناختی، تصادفی و محدودیت‌های انعطاف پذیر، در این مطالعه مدل جدیدی از برنامه‌ریزی محدودیت شانس انعطاف‌پذیر، امکانی، استوار مختلط بر مبنای تئوری اعتبار برای طراحی شبکه زنجیره تامین حلقه بسته توسعه داده شد. نگرش‌های متفاوت تصمیم‌گیرندگان با اندازه‌گیری انعطاف‌پذیرتر پارامترهای خوش‌بینانه بدبینانه قالب معیار پاسخ حداقل سطح رضایت انعطاف‌پذیر حل بهینه گردید. پیشنهادی، قادر کاهش ...

The problem of merging multiple-source uncertain information is a crucial issue in many applications. This paper proposes an analysis of possibilistic merging operators where uncertain information is encoded by means of product-based (or quantitative) possibilistic networks. We first show that the product-based merging of possibilistic networks having the same DAG structures can be easily achie...

Possibilistic logic is a weighted logic introduced and developed since the mid-1980s, in the setting of arti(cial intelligence, with a view to develop a simple and rigorous approach to automated reasoning from uncertain or prioritized incomplete information. Standard possibilistic logic expressions are classical logic formulas associated with weights, interpreted in the framework of possibility...

The analysis and processing of large data are a challenge for researchers. Several approaches have been used to model these complex data, and they are based on some mathematical theories: fuzzy, probabilistic, possibilistic, and evidence theories. In this work, we propose a new unsupervised classification approach that combines the fuzzy and possibilistic theories; our purpose is to overcome th...