Using the Integrated Mission System of the Equal Employment Opportunity Commission (EEOC), the employment discrimination experience of Americans with Learning Disabilities (SLD) is documented for Title I of the Americans with Disabilities Act. The study examines demographic characteristics of the charging parties and the industry of the responding employer against whom complaints are filed. It establishes the nature of the discriminatory act, specifically, pin-points the issue(s) that predicated the allegation, and shows the final outcome or resolution of these complaints. Key dimensions of workplace discrimination as experienced by individuals with LD are detected using two Tests of Proportion. The first test compared individuals with LD to persons who have similar, non-physical disabilities (mental retardation and autism). The second test compares the experience of the LD group to a group representing all other physical, sensory, and neurological disabilities. The Exhaustive CHAID technique is then used to identify and prioritize the most significant variables that contribute to predicting the outcomes of the allegations filed by persons with LD. The comparative findings of both Tests of Proportion in this study indicate that among other industries, Educational Services is more likely to experience allegations of discrimination charged by individuals with LD. Among disability groups, the LD populace was also more likely to make charges of discrimination relative to Assignment, Testing, Harassment, Training, and Discipline. The predictive findings of this study identify eleven specific Issues that drive allegations of discrimination filed by individuals with LD. Derivative implications are discussed as they affect individuals with LD, designated industries, the EEOC, and other stakeholders. Recommendations for future research are made.

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A dissertation submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in fulfilment of requirements for the degree of Master of Science. Johannesburg, 2016. / In order to complete real-world tasks, autonomous robots require a mix of fine-grained control and high-level skills. A robot requires a wide range of skills to handle a variety of different situations, but must also be able to adapt its skills to handle a specific situation. Reinforcement learning is a machine learning paradigm for learning to solve tasks by interacting with an environment. Current methods in reinforcement learning focus on agents with either a fixed number of discrete actions, or a continuous set of actions. We consider the problem of reinforcement learning with parameterized actions—discrete actions with continuous parameters. At each step the agent must select both which action to use and which parameters to use with that action. By representing actions in this way, we have the high level skills given by discrete actions and adaptibility given by the parameters for each action. We introduce the Q-PAMDP algorithm for model-free learning in parameterized action Markov decision processes. Q-PAMDP alternates learning which discrete actions to use in each state and then which parameters to use in those states. We show that under weak assumptions, Q-PAMDP converges to a local maximum. We compare Q-PAMDP with a direct policy search approach in the goal and Platform domains. Q-PAMDP out-performs direct policy search in both domains. / TG2016

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Varje elev ska ges förutsättningar att skapa och upprätthålla goda relationer. Syftet med studien var att undersöka och beskriva fritidslärares uppfattningar om varför olika grupperingar skapas och vilka faktorer som kan påverka elevgrupperingar. För att få svar på forskningsfrågorna användes ett målinriktat urval där sex respondenter intervjuades. Studien utgick från en kvalitativ tolkningsmetod med semistrukturerade intervjuer som datainsamlingsmetod. Då studien undersöker fritidslärares uppfattningar användes en fenomenografisk metodansats. Studien lutar mot det sociokulturella perspektivet, där mediering blir ett centralt begrepp för relationsskapande. Genom intervjuerna framkom det att intressen, trygghet och likhet är faktorer som blir avgörande i elevernas grupptillhörighet.

"This thesis focuses on two key problems in reinforcement learning: How to design reward functions to obtain intended behaviors in autonomous systems using the learning-based control? Given complex mission specification, how to shape the reward function to achieve fast convergence and reduce sample complexity while learning the optimal policy? To answer these questions, the first part of this thesis investigates inverse reinforcement learning (IRL) method with a purpose of learning a reward function from expert demonstrations. However, existing algorithms often assume that the expert demonstrations are generated by the same reward function. Such an assumption may be invalid as one may need to aggregate data from multiple experts to obtain a sufficient set of demonstrations. In the first and the major part of the thesis, we develop a novel method, called Non-parametric Behavior Clustering IRL. This algorithm allows one to simultaneously cluster behaviors while learning their reward functions from demonstrations that are generated from more than one expert/behavior. Our approach is built upon the expectation-maximization formulation and non-parametric clustering in the IRL setting. We apply the algorithm to learn, from driving demonstrations, multiple driver behaviors (e.g., aggressive vs. evasive driving behaviors). In the second task, we study whether reinforcement learning can be used to generate complex behaviors specified in formal logic — Linear Temporal Logic (LTL). Such LTL tasks may specify temporally extended goals, safety, surveillance, and reactive behaviors in a dynamic environment. We introduce reward shaping under LTL constraints to improve the rate of convergence in learning the optimal and probably correct policies. Our approach exploits the relation between reward shaping and actor-critic methods for speeding up the convergence and, as a consequence, reducing training samples. We integrate compositional reasoning in formal methods with actor-critic reinforcement learning algorithms to initialize a heuristic value function for reward shaping. This initialization can direct the agent towards efficient planning subject to more complex behavior specifications in LTL. The investigation takes the initial step to integrate machine learning with formal methods and contributes to building highly autonomous and self-adaptive robots under complex missions."

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