Network models provide an efficient way to represent many real life problems mathematically. In the last few decades, the field of network optimization has witnessed an upsurge of interest among researchers and practitioners. The network models considered in this thesis are broadly classified into four types including transportation problem, shortest path problem, minimum spanning tree problem and maximum flow problem. Quite often, we come across situations, when the decision parameters of network optimization problems are not precise and characterized by various forms of uncertainties arising from the factors, like insufficient or incomplete data, lack of evidence, inappropriate judgements and randomness. Considering the deterministic environment, there exist several studies on network optimization problems. However, in the literature, not many investigations on single and multi objective network optimization problems are observed under diverse uncertain frameworks. This thesis proposes seven different network models under different uncertain paradigms. Here, the uncertain programming techniques used to formulate the uncertain network models are (i) expected value model, (ii) chance constrained model and (iii) dependent chance constrained model. Subsequently, the corresponding crisp equivalents of the uncertain network models are solved using different solution methodologies. The solution methodologies used in this thesis can be broadly categorized as classical methods and evolutionary algorithms. The classical methods, used in this thesis, are Dijkstra and Kruskal algorithms, modified rough Dijkstra algorithm, global criterion method, epsilon constraint method and fuzzy programming method. Whereas, among the evolutionary algorithms, we have proposed the varying population genetic algorithm with indeterminate crossover and considered two multi objective evolutionary algorithms.
EnvBridge: Bridging Diverse Environments with Cross-Environment Knowledge Transfer for Embodied AI
Panasonic Connect Co., Ltd.（松下连接有限公司）；National University of Singapore（新加坡国立大学）；Panasonic R&D Center Singapore（松下新加坡研发中心）；Panasonic R&D Center Germany（松下德国研发中心）
PlanU: Large Language Model Reasoning through Planning under Uncertainty
Fujian Key Laboratory of Sensing and Computing for Smart Cities, School of Informatics, Xiamen University（福建智慧城市感知与计算重点实验室，信息学院，厦门大学）；Key Laboratory of Multimedia Trusted Perception and Efficient Computing, XMU（多媒体可信感知与高效计算重点实验室，XMU）；School of Computer, National University of Defense Technology（计算机学院，国防科技大学）
Planning in Stochastic Environments with Goal Uncertainty
College of Information and Computer Sciences, University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校信息与计算机科学学院）；Alliance Innovation Lab Silicon Valley（联盟硅谷创新实验室）
Information-Seeking Decision Strategies Mitigate Risk in Dynamic, Uncertain Environments
BioFrontiers Institute, University of Colorado Boulder（科罗拉多大学博尔德分校生物前沿研究所）；University of Pennsylvania（宾夕法尼亚大学）；University of Houston（休斯顿大学）；University of Colorado Boulder（科罗拉多大学博尔德分校）
Automated Lane Change Decision Making using Deep Reinforcement Learning in Dynamic and Uncertain Highway Environment
ITU Artificial Intelligence and Data Science Research Center and Department of Aeronautical Engineering, Istanbul Technical University（伊斯坦布尔技术大学人工智能与数据科学研究中心和航空工程系）；Eatron Technologies；Faculty of Mechatronics Engineering, Istanbul Technical University（伊斯坦布尔技术大学机电一体化工程学院）；Department of Computer Engineering, University of California, Santa Cruz（加利福尼亚大学圣克鲁兹分校计算机工程系）；Faculty of Aeronautics and Astronautics, Aerospace Engineering, Istanbul Technical University（伊斯坦布尔技术大学航空航天学院航空航天工程系）；Istanbul Technical University（伊斯坦布尔技术大学）；University of California, Santa Cruz（加利福尼亚大学圣克鲁兹分校）；Eatron Technologies, Istanbul, Turkey（土耳其伊斯坦布尔Eatron Technologies）；ITU Artificial Intelligence and Data Science Research Center（伊斯坦布尔技术大学人工智能与数据科学研究中心）；Department of Aeronautical Engineering, Istanbul Technical University（伊斯坦布尔技术大学航空工程系）
