MODELING, LEARNING AND REASONING ABOUT PREFERENCE TREES OVER COMBINATORIAL DOMAINS

In my Ph.D. dissertation, I have studied problems arising in various aspects of preferences: preference modeling, preference learning, and preference reasoning, when preferences concern outcomes ranging over combinatorial domains. Preferences is a major research component in artificial intelligence (AI) and decision theory, and is closely related to the social choice theory considered by economists and political scientists. In my dissertation, I have exploited emerging connections between preferences in AI and social choice theory. Most of my research is on qualitative preference representations that extend and combine existing formalisms such as conditional preference nets, lexicographic preference trees, answer-set optimization programs, possibilistic logic, and conditional preference networks; on learning problems that aim at discovering qualitative preference models and predictive preference information from practical data; and on preference reasoning problems centered around qualitative preference optimization and aggregation methods. Applications of my research include recommender systems, decision support tools, multi-agent systems, and Internet trading and marketing platforms

On learning and visualizing lexicographic preference trees

Preferences are very important in research fields such as decision making, recommendersystemsandmarketing. The focus of this thesis is on preferences over combinatorial domains, which are domains of objects configured with categorical attributes. For example, the domain of cars includes car objects that are constructed withvaluesforattributes, such as ‘make’, ‘year’, ‘model’, ‘color’, ‘body type’ and ‘transmission’.Different values can instantiate an attribute. For instance, values for attribute ‘make’canbeHonda, Toyota, Tesla or BMW, and attribute ‘transmission’ can haveautomaticormanual. To this end,thisthesis studiesproblemsonpreference visualization and learning for lexicographic preference trees, graphical preference models that often are compact over complex domains of objects built of categorical attributes. Visualizing preferences is essential to provide users with insights into the process of decision making, while learning preferences from data is practically important, as it is ineffective to elicit preference models directly from users. The results obtained from this thesis are two parts: 1) for preference visualization, aweb- basedsystem is created that visualizes various types of lexicographic preference tree models learned by a greedy learning algorithm; 2) for preference learning, a genetic algorithm is designed and implemented, called GA, that learns a restricted type of lexicographic preference tree, called unconditional importance and unconditional preference tree, or UIUP trees for short. Experiments show that GA achieves higher accuracy compared to the greedy algorithm at the cost of more computational time. Moreover, a Dynamic Programming Algorithm (DPA) was devised and implemented that computes an optimal UIUP tree model in the sense that it satisfies as many examples as possible in the dataset. This novel exact algorithm (DPA), was used to evaluate the quality of models computed by GA, and it was found to reduce the factorial time complexity of the brute force algorithm to exponential. The major contribution to the field of machine learning and data mining in this thesis would be the novel learning algorithm (DPA) which is an exact algorithm. DPA learns and finds the best UIUP tree model in the huge search space which classifies accurately the most number of examples in the training dataset; such model is referred to as the optimal model in this thesis. Finally, using datasets produced from randomly generated UIUP trees, this thesis presents experimental results on the performances (e.g., accuracy and computational time) of GA compared to the existent greedy algorithm and DPA

Supporting Decisions: Understanding natural resource management assessment techniques

Report to the Land and Water Resources Research and Development Corporation. This document presents a review of NRM decision support techniques. It draws upon previous studies in the fields of management science, operations research, environmental economics and natural resource management. The objectives of the document are to: Explain the workings of the more significant (representative) methods of NRM decision support (including the latest developments); Discuss how these decision support methods may influence the outcome of NRM decisions; and Provide practicing NRM decision makers with guidance for choosing which methods to apply.Australia;natural resource management;assessment;decision support;

SOCIAL VALUES FOR ATTRIBUTES AT RISK FROM WILDFIRE IN NORTHWEST MONTANA

Annual fire management and suppression expenditures by the USDA Forest Service have dramatically increased in recent years, and exceeded $1 billion in the fire seasons of 2000, 2002, 2003, 2006, 2007, 2008, and 2009. These escalating management costs can largely be attributed to the Forest Services’ efforts to protect private property in the wildland-urban interface (WUI) at the expense of other market and non-market attributes. Due to increasing development within the WUI, climate change, and excessive fuel loading from decades of successful fire suppression, it is likely that fire suppression costs will continue to rise if current wildfire management priorities are not modified. Previous economic models utilized by the Forest Service to support wildfire management decisions only accommodated market values like private structures and timber, however, new models are being developed by the Forest Service to account for various market and non-market values at risk from wildfire in order to more efficiently allocate fire management funds. The objective of this study was to derive marginal social values for several non-market attributes at risk from wildfire in Flathead County, Montana, for inclusion in wildfire management decision-support models. This was achieved by conducting a choice modeling study in Flathead County. It was found that a typical resident of Flathead County has a value for structure protection of only$0.28 per home, compared to $1.90 for a one percentage point reduction in the chance that wildfire affects their recreation opportunities,$3.23 for a one day reduction in the number of moderate smoky days, $13.36 for a one day reduction in the number of unhealthy smoky days,$13.39 for a 1,000 acre reduction of timberland burned by wildfire, and $4.52 for a 1 percentage point reduction in the number of large (greater than 5,000 acres) fires that burn on the landscape. Responses to the questionnaire also revealed that 74.3% of respondents believe that it is the responsibility of the individual homeowner, not fire management agencies, if a home burns because of a wildfire. These findings reveal that there is a small social value for private property protection in Flathead County compared to other non-market attributes, and suggest that current fire management, which places emphasis on private property protection, is not socially efficient for Flathead County

Optimization for Decision Making II

In the current context of the electronic governance of society, both administrations and citizens are demanding the greater participation of all the actors involved in the decision-making process relative to the governance of society. This book presents collective works published in the recent Special Issue (SI) entitled “Optimization for Decision Making II”. These works give an appropriate response to the new challenges raised, the decision-making process can be done by applying different methods and tools, as well as using different objectives. In real-life problems, the formulation of decision-making problems and the application of optimization techniques to support decisions are particularly complex and a wide range of optimization techniques and methodologies are used to minimize risks, improve quality in making decisions or, in general, to solve problems. In addition, a sensitivity or robustness analysis should be done to validate/analyze the influence of uncertainty regarding decision-making. This book brings together a collection of inter-/multi-disciplinary works applied to the optimization of decision making in a coherent manner

CP-nets: From Theory to Practice

Conditional preference networks (CP-nets) exploit the power of ceteris paribus rules to represent preferences over combinatorial decision domains compactly. CP-nets have much appeal. However, their study has not yet advanced sufficiently for their widespread use in real-world applications. Known algorithms for deciding dominance---whether one outcome is better than another with respect to a CP-net---require exponential time. Data for CP-nets are difficult to obtain: human subjects data over combinatorial domains are not readily available, and earlier work on random generation is also problematic. Also, much of the research on CP-nets makes strong, often unrealistic assumptions, such as that decision variables must be binary or that only strict preferences are permitted. In this thesis, I address such limitations to make CP-nets more useful. I show how: to generate CP-nets uniformly randomly; to limit search depth in dominance testing given expectations about sets of CP-nets; and to use local search for learning restricted classes of CP-nets from choice data