Reasoning with sorted-pareto dominance and other qualitative and partially ordered preferences in soft constraints

In decision making problems where we need to choose a particular decision or alternative from a set of possible choices, we often have some preferences which determine if we prefer one decision over another. When these preferences give us an ordering on the decisions that is complete, then it is easy to choose the best or one of the best decisions. However it often occurs that the preferences relation is partially ordered, and we have no best decision. In this thesis, we look at what happens when we have such a partial order over a set of decisions, in particular when we have multiple orderings on a set of decisions, and we present a framework for qualitative decision making. We look at the different natural notions of optimal decision that occur in this framework, which gives us different optimality classes, and we examine the relationships between these classes. We then look in particular at a qualitative preference relation called Sorted-Pareto Dominance, which is an extension of Pareto Dominance, and we give a semantics for this relation as one that is compatible with any order-preserving mapping of an ordinal preference scale to a numerical one. We apply Sorted-Pareto dominance to a Soft Constraints setting, where we solve problems in which the soft constraints associate qualitative preferences to decisions in a decision problem. We also examine the Sorted-Pareto dominance relation in the context of our qualitative decision making framework, looking at the relevant optimality classes for the Sorted-Pareto case, which gives us classes of decisions that are necessarily optimal, and optimal for some choice of mapping of an ordinal scale to a quantitative one. We provide some empirical analysis of Sorted-Pareto constraints problems and examine the optimality classes that result.

High-efficiency photovoltaics through mechanically stacked integration of solar cells based on the InP lattice constant

Solar Energy is a clean and abundant energy source that can help reduce reliance on fossil fuels around which questions still persist about their contribution to climate and long-term availability. Monolithic triple-junction solar cells are currently the state of the art photovoltaic devices with champion cell efficiencies exceeding 40%, but their ultimate efficiency is restricted by the current-matching constraint of series-connected cells. The objective of this thesis was to investigate the use of solar cells with lattice constants equal to InP in order to reduce the constraint of current matching in multi-junction solar cells. This was addressed by two approaches: Firstly, the formation of mechanically stacked solar cells (MSSC) was investigated through the addition of separate connections to individual cells that make up a multi-junction device. An electrical and optical modelling approach identified separately connected InGaAs bottom cells stacked under dual-junction GaAs based top cells as a route to high efficiency. An InGaAs solar cell was fabricated on an InP substrate with a measured 1-Sun conversion efficiency of 9.3%. A comparative study of adhesives found benzocyclobutene to be the most suitable for bonding component cells in a mechanically stacked configuration owing to its higher thermal conductivity and refractive index when compared to other candidate adhesives. A flip-chip process was developed to bond single-junction GaAs and InGaAs cells with a measured 4-terminal MSSC efficiency of 25.2% under 1-Sun conditions. Additionally, a novel InAlAs solar cell was identified, which can be used to provide an alternative to the well established GaAs solar cell. As wide bandgap InAlAs solar cells have not been extensively investigated for use in photovoltaics, single-junction cells were fabricated and their properties relevant to PV operation analysed. Minority carrier diffusion lengths in the micrometre range were extracted, confirming InAlAs as a suitable material for use in III-V solar cells, and a 1-Sun conversion efficiency of 6.6% measured for cells with 800 nm thick absorber layers. Given the cost and small diameter of commercially available InP wafers, InGaAs and InAlAs solar cells were fabricated on alternative substrates, namely GaAs. As a first demonstration the lattice constant of a GaAs substrate was graded to InP using an InxGa1-xAs metamorphic buffer layer onto which cells were grown. This was the first demonstration of an InAlAs solar cell on an alternative substrate and an initial step towards fabricating these cells on Si. The results presented offer a route to developing multi-junction solar cell devices based on the InP lattice parameter, thus extending the range of available bandgaps for high efficiency cells.

Reasoning with imprecise trade-offs in decision making under certainty and uncertainty

In many real world situations, we make decisions in the presence of multiple, often conflicting and non-commensurate objectives. The process of optimizing systematically and simultaneously over a set of objective functions is known as multi-objective optimization. In multi-objective optimization, we have a (possibly exponentially large) set of decisions and each decision has a set of alternatives. Each alternative depends on the state of the world, and is evaluated with respect to a number of criteria. In this thesis, we consider the decision making problems in two scenarios. In the first scenario, the current state of the world, under which the decisions are to be made, is known in advance. In the second scenario, the current state of the world is unknown at the time of making decisions. For decision making under certainty, we consider the framework of multiobjective constraint optimization and focus on extending the algorithms to solve these models to the case where there are additional trade-offs. We focus especially on branch-and-bound algorithms that use a mini-buckets algorithm for generating the upper bound at each node of the search tree (in the context of maximizing values of objectives). Since the size of the guiding upper bound sets can become very large during the search, we introduce efficient methods for reducing these sets, yet still maintaining the upper bound property. We define a formalism for imprecise trade-offs, which allows the decision maker during the elicitation stage, to specify a preference for one multi-objective utility vector over another, and use such preferences to infer other preferences. The induced preference relation then is used to eliminate the dominated utility vectors during the computation. For testing the dominance between multi-objective utility vectors, we present three different approaches. The first is based on a linear programming approach, the second is by use of distance-based algorithm (which uses a measure of the distance between a point and a convex cone); the third approach makes use of a matrix multiplication, which results in much faster dominance checks with respect to the preference relation induced by the trade-offs. Furthermore, we show that our trade-offs approach, which is based on a preference inference technique, can also be given an alternative semantics based on the well known Multi-Attribute Utility Theory. Our comprehensive experimental results on common multi-objective constraint optimization benchmarks demonstrate that the proposed enhancements allow the algorithms to scale up to much larger problems than before. For decision making problems under uncertainty, we describe multi-objective influence diagrams, based on a set of p objectives, where utility values are vectors in Rp, and are typically only partially ordered. These can be solved by a variable elimination algorithm, leading to a set of maximal values of expected utility. If the Pareto ordering is used this set can often be prohibitively large. We consider approximate representations of the Pareto set based on ϵ-coverings, allowing much larger problems to be solved. In addition, we define a method for incorporating user trade-offs, which also greatly improves the efficiency.