مولوی حسین حسان ندوی
راقم کے لیے تیسرا حادثہ طالب علمی کے زمانہ کے رفیق مولوی حسین حسان صاحب ندوی اڈیٹر پیام تعلیم کی وفات کا ہے، مرحوم درجہ میں مجھ سے دو تین سال نیچے اور غالباً عمر میں بھی اسی قدر چھوٹے تھے، لیکن ہم دونوں عرصہ تک ایک ہی کمرے میں رہے تھے، اس لئے ان سے دوستانہ تعلقات تھے جو آخر تک قائم رہے، اسی زمانہ سے ان میں مضمون نگاری کا ذوق تھا، چنانچہ عشاق عرب کے عنوان سے ایک طویل مضمون لکھا تھا۔ جو زمانہ کانپور کے کئی نمبروں میں چھپا تھا، ندوہ سے فراغت کے بعد جامعہ ملیہ چلے گئے اور وہاں تعلیم کے ساتھ مختلف اوقات میں جامعہ کے مختلف شعبوں سے ان کا تعلق رہا، بچوں کا ادب لکھنے میں ان کو خاص ملکہ تھا اس کے وہ صاحب طرز ادیب تھے، برسوں بچوں کے رسالہ پیام تعلیم کے اڈیٹر رہے اور اس کو ان کا بڑا مقبول رسالہ بنادیا، پیام تعلیم کے مضامین کے علاوہ انھوں نے بچوں کے ذوق کی بہت سی کتابیں لکھیں اور بچکانہ ادب کا بڑا ذخیرہ فراہم کردیا، ان سے تعلقات کا سلسلہ برابر قائم رہا، کبھی کبھی ملاقات بھی ہوجاتی تھی، گزشتہ دسمبر میں دلی میں ملاقات ہوئی تھی، کیا معلوم تھا کہ یہ آخری ملاقات ہے، اﷲ تعالیٰ ان کی مغفرت فرمائے۔ (شاہ معین الدین ندوی، اگست ۱۹۷۴ء)
History comes to us from various agencies not just academics in schools and colleges; but diverse inputs to all those who haven’t studied history, like popular history, through cinema, poetry, folklore, myths, theatre; history has several modes of percolation to society. Also, a kind of history is propagated in an organised manner as is done by organisations as RSS which is a practical approach to history as differentiated from an academic approach to history; the former is more political than the latter though both come with an aspect of politics. History thus has much wider reach than what is taught in schools or colleges.
This dissertation aims to enhance the real-time decision making of autonomous agents in a complex adversarial domain. Explicit opponent modeling techniques are applied to store the strengths of opponents and use them to create an opponent model. The devised strategies are optimized specifically for each type of opponent. To deal with changing strategies of the opponents, the strategies are adaptive and revised after predefined time instances. An evolutionary computation-based framework, namely SASO, has been developed that automates the creation of the opponent model and optimizes strategies specific to each opponent model. An opponent in this research comprises of a team of autonomous robots while the adversarial domain is the simulated soccer platform. For devising opponent-specific strategies, several teams of soccer-playing agents have been chosen and their strategies are analyzed. This analysis facilitates grouping teams into different opponent models to improve gameplay against unseen opponents. The framework proposes a modular approach with a clear distinction between online and offline phases. Both opponent modeling and strategy optimization are performed offline while strategy prediction and strategy adaptation are performed online. Empirical evidence shows that the team, that adapts its strategy according to the opponent outperforms the team that disregards its opponent. The challenges addressed in this research are an accurate prediction of the type of opponent, anticipating the opponent's strategy and then making correct decisions in real-time. For designing an explicit opponent model, the research uses past actions of the opponents to build the model Secondly, there are issues regarding strategy evolution where parameters need to be me-tuned and a workable strategy has to be guaranteed for all instances. To test the effectiveness of the framework, the RoboCup Soccer Simulation 3D league has been chosen as a testbed. The league offers a dynamic and partially observable environment making strategy recognition and adaptation truly challenging tasks. The novelty of this framework is its end to end approach for strategy extraction, identification, optimization as well as strategy execution in real-time to improve the overall performance of the team. It also serves as a generalized approach that prepares agents to interact with unknown opponents. The approach has been implemented over a reasonable number of opponents and can be extended to an exhaustive number of opponent teams