خان بہادر میر ناصر علی
مدیر ’’صلائے عام‘‘ دہلی
اردو کے ایک اور کہنہ صاحب قلم استاد کی وفات پر دو آنسو بہانا ہے، ایک زمانہ تھا کہ اس کی انشاء پردازی اور نکتہ نوازی پر ملک کے اچھے اچھے اہل قلم رشک کرتے تھے مگر افسوس کہ نوجوانوں نے اس کو بھلا دیا، یہ خان بہادر میر ناصر علی، مدیر صلائے عام دہلی تھے، مرحوم نے عمر کی چھیاسی ۸۶ بہاریں دیکھ کر ۱۲؍ جون ۳۳ء کو دہلی میں وفات پائی، ان کے قلم میں جو نزاکت اور ان کی انشاء میں جو لطافت تھی وہ اب بھی ہماری زبان کا سرمایہ ہے، مگر افسوس ہے کہ آخر میں وہ یہ ساری جگر کاوی ان ناقدر شناس انگریز افسروں کے لئے کرتے تھے جو ہندوستانی زبان کو امتحان کے لئے سیکھتے تھے اور اسی لئے ان کی یہ ادبی کوششیں عام نگاہوں سے چھپ کر رہ گئی تھیں، خدا اپنے دربار میں ہمارے بوڑھے صاحب قلم کی آبرو رکھے۔ (سید سلیمان ندوی، جولائی ۱۹۳۳ء)
Islam has been a subject of great importance for Muslim and Non-Muslim Scholars. Countless numbers of researchers have written on the Quran, Hadith, Fiqh and Islamic History. Among these scholars Dr. Mahmood Ahmad Ghazi (1950-2010) is renowned for his contribution to Islamic social sciences. He was equally well versed in the classical, as well as, modern scholarly trends in the study of Islam. He worked in various academic, administrative and judicial positions during his life including as professor, Director Da’wah Academy, Director Shariah Academy, Vice President Academics and then President of International Islamic University, Islamabad and was finally elevated in 2010 as a judge at Federal Shariah Court of Pakistan where he served till the end of his life. He was a prolific writer and authored nearly thirty works of high academic value in different Islamic social sciences including Economics, law and Islamic education in Arabic, English and Urdu languages. He attended a large number of conferences across the globe to present his research papers.
Our quest to understand, model, and reproduce natural intelligence has opened new avenues of research. One such area is artificial intelligence (AI). AI is the branch of computer science aiming to create machines able to engage in activities that humans consider intelligent. The ability to create intelligence in a machine has intrigued humans ever since the advent of computers. With recent advancements in computer science we are coming closer every day to the realization of our dreams of smarter or intelligent machines. New algorithms and methods are constantly being designed by researchers. However these techniques must be evaluated and their performance compared before they can be accepted. For this purpose games have caught the attention of AI researchers and gaming environment have proven to be excellent test beds for such evaluation. Although games have redeemed AI research, one limitation most researchers have applied is of perfect information. Perfect information environments imply that the information available to the agents in the environment does not change. Essentially what this means is that agents can detect entities that they have been trained for but will ignore entities for which training has not taken place. This limitation results in agents that do not gain a single iota of learning while they are in the environment. Whatever learning has taken place during their training, they will not increase upon it. This would all be fine if we were living in a static world of perfect information, but we do not! Learning in such an unpredictable and changing environment is a continuous process for the agents. For this reason we developed a “Continuous Learning Framework” (CLF). CLF enables each agent to detect the changes in the environment and take necessary action accordingly. Agents who fail to do so die out during the evolutionary process. CLF based learning is triggered by stimulus from the environment. We have intentionally kept CLF independent of this environment or of the underlying evolutionary approaches, allowing our CLF to be ported to other environments with dynamic nature. Learning new abilities and adapting successful strategies is crucial to the survival of species. Results of our experimentation show that CLF not only enables agents to learn new strategies suitable to their current environmental state but also5BAbstract ensures proper dissemination of information within a species. Forgetfulness is an inherent feature of the co-evolutionary processes. Keeping this in view we have also explored the integration of historical information and the ability to retain and recall past learning experiences. We have tested a social learning based flavor of our CLF to see whether learning from past is profitable for agents. Each of the species was allowed to maintain a social pool of successful strategies. Results from these experiments show that strategy from the pool results in a significant boost to performance in cases where the environmental conditions are similar to when the strategy was established. This social pools acts like a general reservoir of knowledge which is similar in nature to the one we humans hold with ancient civilizations. This historical information also results in performance boosts by eliminating the “reinvention of wheel” phenomena common to evolutionary strategies. This research not only presents a new way of learning along within a dynamic and uncertain medium but also aims to establish the importance of learning in such an imperfect environment. Much work still needs to be undertaken in this path. Possible future channels of this research include designing better performance evaluation criteria of agents residing in different locations of the environment, and establishing individual archive for learning based on personal experience.