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Register a new userA Model for Representing Knowledge and Inference in Artificial Intelligence Systems
نموذج لتمثيل المعرفـة و الاستـدلال في نظم الذكاء الصنعي
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في حقل الذكاء الصنعي، تعد هندسة المعرفة المرحلة الأهم من دورة حياة تطوير نظم قواعد المعرفة عموماً و النظم الخبيرة خصوصاً . و قد سيطر المنطق الصوري بشكل عام و قاعدة الاسـتدلال الأولـى (مودس بوننس) بشكل خاص، على الأدوات المستخدمة لبناء هذه المعرفة ، مما أدى إلى تشكل فجوة بين مجالي المعرفة و المعلومات التي تعتمد في بنائها على نظرية المجموعات بشكل عام و علـى الجبـر العلاقاتي بشكل خاص . وسعياً في توفير أحد جسور استبدال المنطق بنظرية المجموعات فـي تمثيـل المعرفة و معالجتها، فقد قمنا في هذا البحث بتأسيس نموذج لتمثيل المعرفة مبني علـى أسـس نظريـة المجموعات (العادية و الترجيحية). و استفدنا من هذا البناء في تأسيس نموذج للاستدلال مبني على جبـر المجموعات، يقوم عبر المرور بسلسلة من المراحل و باستخدام مجموعة من العمليات الجبرية، بالتوصـل إلى حل للمسألة المدروسة بشكل مشابه لأسلوب الإنسان في التعامل معها، متوخين سرعة الأداء و دقـة النتائج بالقدر الذي يسمح به نوع المسائل الذي تتصدى له هذه النظم.
In Artificial Intelligence field, Knowledge Engineering phase is considered the most crucial phase of the development life cycle of the Knowledge Base Systems [1]. In fact, Formal Logic in general and Modus Ponens specifically has been the dominant tools for structuring this knowledge [3]. This led for forming a gap between the knowledge area and the information area, which depends structurally on the Set Theory in general and on the Relational Algebra in particular [1]. Thus, trying to introduce a bridge to pass this gap in structuring and treating knowledge, we have conducted a new knowledge representation model that depends structurally on (Classical and Fuzzy) Set Theory. Then we used it as the base for conducting an inference model that attempt, using a set of algebraic operations and by going through a series of stages, to reach a solution of the problem under study, in a manner very close to the one that humans usually use in treating their knowledge, taking into consideration the speed and accuracy as much as the problem allows.
References used
Jackson, P.. (1999) Introduction to Expert Systems , England: Addison Wesley
Leondes, T., (1999) Fuzzy Logic and Expert Systems Applications , London: Academic Press
Durkin, J., (1994) Expert Systems: Design and Development , New York : Macmillan
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المسؤولية الجنائية للذكاء الاصطناعي
تتمثل أهمية هذه الدراسة في أهمية موضوعها الجديد والحيوي، وهو المسؤولية الجنائية الناتجة عن أخطاء الذكاء الاصطناعي في التشريع الإماراتي "دراسة مقارنة"، فعلى امتداد الخمسين سنة الماضية تضافرت الجهود العالمية في عدد
من الميادين، كالفلسفة والقانون وعلم النفس وعلم المنطق والرياضيات، وعلم الأحياء وغيرها من العلوم، ومنذ سنوات بدأت هذه الجهود تحصد من ثمارها وظهرت إلى الوجود تطبيقات مذهلة للذكاء الاصطناعي، وهذا ما دفع دولة الإمارات العربية المتحدة لاستحداث وزارة للذكاء الاصطناعي وعلوم المستقبل، فهذه الخطوة تُضاف إلى سجل الإمارات الحافل بكل ما هو جديد في الثقافة والعلوم وغيرها من المجالات، فالإمارات سبّاقة في البحث وجلب أي أفكار جديدة أو عالمية وتطبيقها، والهدف من ذلك هو الارتقاء بالعمل الإداري. لأن اعتماد الإدارة على الذكاء الاصطناعي يساعدها على التكيف مع التغيرات المتلاحقة، ويساعدها أيضاً على مواجهة التحديات المتعددة والمختلفة، وبالتالي تحقيق الميزة التنافسية التي تسعى الإدارة إلى تحقيقها.
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Knowledge management procedures, also that the most important requisites of Knowledge
management application in libraries was existing of three elements which are:
organizational culture, organizational knowledge, knowledge management technology.
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