Negotiating the Pre-9/11 Muslim Identity in Reluctant Fundamentlist and Home Boy

This paper critically analyses pre-9/11 diasporic identity of Muslims living in the US as immigrants or expatriates depicted in The Reluctant Fundamentlist (TRF) and Home Boy (HB) authored by minority outgroup Muslims (MO). The pre-9/11 identity and image of Muslims has exacerbated from erotic, primitive, barbaric, ignorant, close-minded and semicitizen to maddened, fundamentalist, blood-thirsty and terrorist after the attacks. The study attempts a textual analysis of the novels in the light of Rosenau’s model (2003) of diasporic acculturation process and social identity theory (ST). Given this stereotyping, this study endeavours to dissect the pre-9/11approach Muslims immigrants adopt to negotiate their religious identity in the hostland: whether they are fanatic and diehard separatist or they are moderate and assimilative into the enlightened values of the West. Opposite to popular assumptions, the protagonists have been found very much assimilative and adoptive to the host culture and also adhere to their homeland culture as well.

A Framework of adaptive decision making for a team of autonomous agents

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