Thesis--University of Wisconsin--Madison. / Typescript. Vita. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 191-197).

Thesis (Ph. D.)--University of Wisconsin--Madison, 1980. / Typescript. Vita. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 177-180).

Thesis (M.S.)--Tennessee Technological University, 2009. / Title from title page screen (viewed on Aug. 19, 2009). Bibliography: leaves 37-39.

Thesis (Ph. D.)--University of Wisconsin--Madison, 1975. / Typescript. Vita. eContent provider-neutral record in process. Description based on print version record. Bibliography: leaves 103-109.

Thesis (M.S.)--Duquesne University, 2005. / Title from document title page. Abstract included in electronic submission form. Includes bibliographical references and abstract.

For several decades, causal decision theory (CDT) has been the orthodox version of philosophical decision theory. However, ever since CDT was first developed there have been those who have disputed the adequacy of this theory. Then, in the last decade and a half, opposition to this theory has intensified, with a vast array of novel objections to CDT emerging. As a result, the field of philosophical decision theory has splintered, with a large number of new versions of decision theory being developed to try to plug the gap left by the apparent collapse of CDT. However, in this thesis I will defend CDT against the objections raised against it and so dispute the need to develop a new version of decision theory. In doing so, I will address old challenges to CDT, based around Newcomb’s Problem and cases where CDT provides unstable guidance. These challenges have been around for some time. While existing solutions have been presented here, these have failed to fully resolve the disquiet that these objections raise. In this thesis, I will have more to say to resolve this disquiet so that we can set these old objections aside. In this thesis, I will also address new challenges to CDT, which have arisen in the past decade and a half. These challenges are based on appeals to quantum mechanics, prophecy, and the laws of nature, among other things. Many of these objections have not previously been addressed. However, I will argue that these challenges fail to appropriately construe CDT and so fail to truly undermine this theory. Causal decision theory, I will conclude, is a robust theory. As such, while there is much work to be done in philosophical decision theory this work involves building on, rather than replacing, CDT.

This dissertation explores and analyzes the performance of several Bayesian anytime inference algorithms for dynamic influence diagrams. These algorithms are compared on the On-Line Maintenance Agent testbed, a software artifact permitting comparison of dynamic reasoning algorithms used by an agent on a variety of simulated maintenance and monitoring tasks. Analysis of their performance suggests that a particular algorithmic property, which I term sampling kurtosis, may be responsible for successful reasoning in the tested half-adder domain. A new algorithm is devised and evaluated which permits testing of sampling kurtosis, revealing that it may not be the most significant algorithm property but suggesting new lines of inquiry. Peculiarities in the observed data lead to a detailed analysis of agent-simulator interaction, resulting in an equation model and a Stochastic Automata Network model for a random action algorithm. The model analyses are extended to show that some of the anytime reasoning algorithms perform remarkably near optimally. The research suggests improvements for the design and development of reasoning testbeds. / Graduation date: 2002

Thesis (Ph. D.)--Ohio State University, 2006. / Title from first page of PDF file. Includes bibliographical references (p. 126-130).

Thesis (Ph. D.)--Ohio State University, 2006. / Title from first page of PDF file. Includes bibliographical references (p. 94-103).

In this thesis, we present a decision-theoretic framework for building decision support systems that incrementally elicit preferences of individual users over multiattribute outcomes and then provide recommendations based on the acquired preference information. By combining decision-theoretically sound modeling with effective computational techniques and certain user-centric considerations, we demonstrate the feasibility and potential of practical autonomous preference elicitation and recommendation systems. More concretely, we focus on decision scenarios in which a user can obtain any outcome from a finite set of available outcomes. The outcome is space is multiattribute; each outcome can be viewed as an instantiation of a set of attributes with finite domains. The user has preferences over outcomes that can be represented by a utility function. We assume that user preferences are generalized additively independent (GAI), and, therefore, can be represented by a GAI utility function. GAI utilities provide a flexible representation framework for structured preferences over multiattribute outcomes; they are less restrictive and, therefore, more widely applicable than additive utilities. In many decision scenarios with large and complex decision spaces (such as making travel plans or choosing an apartment to rent from thousands of available options), selecting the optimal decision can require a lot of time and effort on the part of the user. Since obtaining the user's complete utility function is generally infeasible, the decision support system has to support recommendation with partial preference information. We provide solutions for effective elicitation of GAI utilities in situations where a probabilistic prior about the user's utility function is available, and in situations where the system's uncertainty about user utilities is represented by maintaining a set of feasible user utilities. In the first case, we use Bayesian criteria for decision making and query selection. In the second case, recommendations (and query strategies) are based on the robust minimax regret criterion which recommends the outcome with the smallest maximum regret (with respect to all adversarial instantiations of feasible utility functions). Our proposed framework is implemented in the UTPref recommendation system that searches multiattribute product databases using the minimax regret criterion. UTPref is tested with a study involving 40 users interacting with the system. The study measures the effectiveness of regret-based elicitation, evaluates user comprehension and acceptance of minimax regret, and assesses the relative difficulty of different query types.