Reasoning is an indispensable action both for natural intelligence and for artificial intelligence. In automated reasoning, relatively expressive logics are used to define and derive complex facts about the real world. Many facts cannot be expressed in inexpressive logics such as syllogistic logic and thus, those logics are naturally ignored for automated reasoning. Despite their inexpressiveness, logics with intuitive propositions can provide two advantages: favorable complexity properties for reasoning tasks, and better correspondence with the natural language statements. Syllogistic logic, the first known formal logic in history, is so intuitive that it is often studied by cognitive scientists in order to understand and model human reasoning. The problem is that its syntax and semantics do not allow for representing much knowledge. Hence, propositions of syllogistic logic should be generalized with useful extensions without sacrificing the advantageous properties much. The aim of this thesis work is to bridge the gap between syllogistic reasoning and automated reasoning via designing the underlying logic and framework for performing time-efficient fully-automated deduction over generalized syllogistic propositions. In this thesis work, we define a practical family of generalized syllogistic logics, reveal the theoretical properties of those logics and their relationships with some other logics, and specify comprehensive frameworks with alternative representations and methods.