At birth, male and female rats were randomly assigned to either a handled or nonhandled condition. The handled Ss were given tactual stimulation for a five minute period from days five to twenty-five. During the preweaning period Ss in the nonhandled condition were given the least possible amount of experimenter handling. When Ss were between 80 to 100 days old both handled and nonhandled Ss were randomly assigned to one of four acquisition conditions and were trained and tested in a straight alley runway. These conditions were; continuous reinforcement in a large goalbox, continuous reinforcement in a small goalbox, partial reinforcement in a large goalbox, and partial reinforcement in a small box. Each S was then given 60 acquisition trials at the rate of 5 trials per day. Following completion of the acquisition trials all Ss received 50 nonreward trials which were also given in blocks of 5 trials per day. On both acquisition and extinction trials, time measures were taken for startbox and runway performance. For the purpose of analysis all time measures were converted into speed scores. The results showed that all experimental treatments, (i.e. handling, reward schedule and goalbox size), had a significant effect on the rate of experimental extinction. Infantile handling, a partial reinforcement schedule, and a large goalbox were all conducive to decreasing the rate of extinction of a running response. / Arts, Faculty of / Psychology, Department of / Graduate

Rapid urbanization poses huge challenges to people's daily lives, such as traffic congestion, environmental pollution, and public safety. Mobile Internet of things (MIoT) applications serving smart cities bring the promise of innovative and enhanced public services such as air pollution monitoring, enhanced road safety and city resources metering and management. These applications rely on a number of energy constrained MIoT units (MUs) (e.g., robots and drones) to continuously sense, capture and process data and images from their environments to produce immediate adaptive actions (e.g., triggering alarms, controlling machinery and communicating with citizens). In this thesis, we consider a scenario where a battery constrained MU executes a number of time-sensitive data processing tasks whose arrival times and sizes are stochastic in nature. These tasks can be executed locally on the device, offloaded to one of the nearby edge servers or to a cloud data center within a mobile edge computing (MEC) infrastructure. We first formulate the problem of making optimal offloading decisions that minimize the cost of current and future tasks as a constrained Markov decision process (CMDP) that accounts for the constraints of the MU battery and the limited reserved resources on the MEC infrastructure by the application providers. Then, we relax the CMDP problem into regular Markov decision process (MDP) using Lagrangian primal-dual optimization. We then develop advantage actor-critic (A2C) algorithm, one of the model-free deep reinforcement learning (DRL) method to train the MU to solve the relaxed problem. The training of the MU can be carried-out once to learn optimal offloading policies that are repeatedly employed as long as there are no large changes in the MU environment. Simulation results are presented to show that the proposed algorithm can achieve performance improvement over offloading decisions schemes that aim at optimizing instantaneous costs.

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Large-scale spatial data such as air quality of a city, biomass content in a lake, Wi-Fi Received Signal Strengths (RSS, also referred as fingerprints) in indoor spaces often play vital roles to applications like indoor localization. However, it is extremely labor-intensive and time-consuming to collect those data manually. In this thesis, the main goal is to develop efficient means for large-scale spatial data collection. Robotic technologies nowadays offer an opportunity on mobile sensing, where data are collected by a robot traveling in target areas. However, since robots usually have a limited travel budget depending on battery capacity, one important problem is to schedule a data collection path to best utilize the budget. Inspired by existing literature, we consider to collect data along informative paths. The process to search the most informative path given a limited budget is known as the informative path planning (IPP) problem, which is NP-hard. Thus, we propose two heuristic approaches, namely a greedy algorithm and a genetic algorithm. Experiments on Wi-Fi RSS based localization show that data collected along informative paths tend to achieve lower errors than that are opportunistically collected. In practice, the budget of a mobile robot can vary due to insufficient charging or battery degradation. Although it is possible to apply the same path planning algorithm repetitively whenever the budget changes, it is more efficient and desirable to avoid solving the problem from scratch. This can be possible since informative paths for the same area share common characteristics. Based on this intuition, we propose and design a reinforcement learning based IPP solution, which is able to predict informative paths given any budget. In addition, it is common to have multiple robots to conduct sensing tasks cooperatively. Therefore, we also investigate the multi-robot IPP problem and present two solutions based on multi-agent reinforcement learning. Mobile crowdsourcing (MCS) offers another opportunity to lowering the cost of data collection. In MCS, data are collected by individual contributors, which is able to accumulate a large amount of data when there are sufficient participants. As an example, we consider the collection of a specific type of spatial data, namely Wi-Fi RSS, for indoor localization purpose. The process to collect RSS is also known as site survey in the localization community. Though MCS based site survey has been suggested a decade ago~\cite{park2010growing}, so far, there has not been any published large-scale fingerprint MCS campaign. The main issue is that it depends on user's participation, and users may be reluctant to make a contribution. To investigate user behavior in a real-world site survey, we design an indoor fingerprint MCS system and organize a data collection campaign in the McMaster University campus for five months. Although we focus on Wi-Fi fingerprints, the design choices and campaign experience are beneficial to the MCS of other types of spatial data as well. The contribution of this thesis is two-fold. For applications where robots are available for large-scale spatial sensing, efficient path planning solutions are investigated so as to maximize data utility. Meanwhile, for MCS based data acquisition, our real-world campaign experience and user behavior study reveal essential design factors that need to be considered and aspects for further improvements. / Thesis / Doctor of Philosophy (PhD) / A variety of applications such as environmental monitoring require to collect large-scale spatial data like air quality, temperature and humidity. However, it usually incurs dramatic costs like time to obtain those data, which is impeding the deployment of those applications. To reduce the data collection efforts, we consider two mobile sensing schemes, i.e, mobile robotic sensing and mobile crowdsourcing. For the former scheme, we investigate how to plan paths for mobile robots given limited travel budgets. For the latter scheme, we design a crowdsourcing platform and study user behavior through a real word data collection campaign. The proposed solutions in this thesis can benefit large-scale spatial data collection tasks.

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