The federal photovoltaics commercialization program

The dissertation presents a political and economic history of the federal government's program to commercialize photovoltaic energy for terrestrial use. Chapter 1 is a detailed history of the program. Chapter 2 is a brief review of the Congressional roll call voting literature. Chapter 3 develops PV benefit measures at the state and Congressional district level necessary for an econometric analysis of PV roll call voting. Chapter 4 presents the econometric analysis.

Because PV power was considerably more expensive than conventional power, the program was designed to make PV a significant power source in the long term, emphasizing research and development, although sizeable amounts have been spent for procurement (direct government purchases and indirectly through tax credits). The decentralized R and D program pursued alternative approaches in parallel, with subsequent funding dependent on earlier progress. Funding rose rapidly in the 1970s before shrinking in the 1980s. Tax credits were introduced in 1978, with the last of the credits due to expire this year.

Major issues in the program have been the appropriate magnitude of demonstrations and government procurement, whether decentralized, residential use or centralized utility generation would first be economic, the role of storage in PV, and the role of PV in a utility's generation mix.

Roll call voting on solar energy (all votes analyzed occurred from 1975-1980) was influenced in a cross-sectional sense by all the influences predicted: party and ideology, local economic benefits of the technology, local PV federal spending and manufacturing, and appropriations committee membership. The cross-sectional results for ideology are consistent with the strongly ideological character of solar energy politics and the timing of funding increases and decreases discussed in Chapter 1. Local PV spending and manufacturing was less significant than ideology or the economic benefits of the technology. Because time series analysis of the votes was not possible, it is not possible to test the role of economic benefits to the nation as a whole.

Formal methods for control synthesis in partially observed environments : application to autonomous robotic manipulation

Modern robots are increasingly expected to function in uncertain and dynamically challenging environments, often in proximity with humans. In addition, wide scale adoption of robots requires on-the-fly adaptability of software for diverse application. These requirements strongly suggest the need to adopt formal representations of high level goals and safety specifications, especially as temporal logic formulas. This approach allows for the use of formal verification techniques for controller synthesis that can give guarantees for safety and performance. Robots operating in unstructured environments also face limited sensing capability. Correctly inferring a robot's progress toward high level goal can be challenging.

This thesis develops new algorithms for synthesizing discrete controllers in partially known environments under specifications represented as linear temporal logic (LTL) formulas. It is inspired by recent developments in finite abstraction techniques for hybrid systems and motion planning problems. The robot and its environment is assumed to have a finite abstraction as a Partially Observable Markov Decision Process (POMDP), which is a powerful model class capable of representing a wide variety of problems. However, synthesizing controllers that satisfy LTL goals over POMDPs is a challenging problem which has received only limited attention.

This thesis proposes tractable, approximate algorithms for the control synthesis problem using Finite State Controllers (FSCs). The use of FSCs to control finite POMDPs allows for the closed system to be analyzed as finite global Markov chain. The thesis explicitly shows how transient and steady state behavior of the global Markov chains can be related to two different criteria with respect to satisfaction of LTL formulas. First, the maximization of the probability of LTL satisfaction is related to an optimization problem over a parametrization of the FSC. Analytic computation of gradients are derived which allows the use of first order optimization techniques.

The second criterion encourages rapid and frequent visits to a restricted set of states over infinite executions. It is formulated as a constrained optimization problem with a discounted long term reward objective by the novel utilization of a fundamental equation for Markov chains - the Poisson equation. A new constrained policy iteration technique is proposed to solve the resulting dynamic program, which also provides a way to escape local maxima.

The algorithms proposed in the thesis are applied to the task planning and execution challenges faced during the DARPA Autonomous Robotic Manipulation - Software challenge.

Probability in Quantum Mechanics: The Everett Interpretation and the Decision-Theoretic Program

The Everett interpretation of quantum mechanics is an increasingly popular alternative to the traditional Copenhagen interpretation, but there are a few major issues that prevent the widespread adoption. One of these issues is the origin of probabilities in the Everett interpretation, which this thesis will attempt to survey. The most successful resolution of the probability problem thus far is the decision-theoretic program, which attempts to frame probabilities as outcomes of rational decision making. This marks a departure from orthodox interpretations of probabilities in the physical sciences, where probabilities are thought to be objective, stemming from symmetry considerations. This thesis will attempt to offer evaluations on the decision-theoretic program.