Graphically structured value-function compilation

AbstractClassical work on eliciting and representing preferences over multi-attribute alternatives has attempted to recognize conditions under which value functions take on particularly simple and compact form, making their elicitation much easier. In this paper we consider preferences over discrete domains, and show that for a certain class of simple and intuitive qualitative preference statements, one can always generate compact value functions consistent with these statements. These value functions maintain the independence structure implicit in the original statements. For discrete domains, these representation theorems are much more general than previous results. However, we also show that it is not always possible to maintain this compact structure if we add explicit ordering constraints among the available outcomes

Determining key research areas for healthier diets and sustainable food systems in Viet Nam

Vietnamese food systems are undergoing rapid transformation, with important implications for human and environmental health and economic development. Poverty has decreased, and diet quality and undernutrition have improved significantly since the end of the Doi Moi reform period (1986-1993) as a result of Viet Nam opening its economy and increasing its regional and global trade. Yet poor diet quality is still contributing the triple burden of malnutrition, with 25 percent stunting among children under age 5, 26 percent and 29 percent of women and children, respectively, anemic, and 21 percent of adults overweight. Agricultural production systems have shifted from predominantly diverse smallholder systems to larger more commercialized and specialized systems, especially for crops, while the ‘meatification’ of the Vietnamese diet is generating serious trade-offs between improved nutrition and sustainability of the Vietnamese food systems. The food processing industry has developed rapidly, together with food imports, resulting in new and processed food products penetrating the food retail outlets, trending towards an increase in the Westernized consumption patterns that are shifting nutrition-related problems towards overweight and obesity and, with it, an increase of non-communicable disease-related health risks. While regulatory policies exist across the food system, these are not systematically implemented, making food safety a major concern for consumers and policy makers alike. Where data exists, it is not easy to aggregate with data from across food system dimensions, making it difficult for Viet Nam to make an informed analysis of current and potential food system trade-offs. In our research, we reviewed existing literature and data, and applied a food systems framework to develop an initial food systems profile for Viet Nam and to identify a comprehensive set a of research questions to fill current data gaps identified through the review. Insights on these would provide the comprehensive evidence needed to inform policy makers on how to develop new food systems policies for Viet Nam, and further refine and improve existing policies to achieve better quality diets and more sustainable food systems in Viet Nam. Based on these, we then engaged with stakeholders to develop research priorities in the Viet Nam context and identified 25 priority research questions. This paper aims to stimulate such reflections by clearly outlining key areas for research, government policy, and development programs on priority investment to build the evidence base around inclusive food systems interventions that aim to result in healthier diets and more sustainable food systems for Viet Nam

Conditional preference networks: efficient dominance testing and learning

Modelling and reasoning about preference is necessary for applications such as recommendation and decision support systems. Such systems are becoming increasingly prevalent in all aspects of our daily lives as technology advances. Thus, preference representation is a wide area of interest within the Artificial Intelligence community. Conditional preference networks, or CP-nets, are one of the most popular models for representing a person's preference structure. In this thesis, we address two issues with this model that make it difficult to utilise in practice. First, answering dominance queries efficiently. Dominance queries ask for the relative preference between a given pair of outcomes. Such queries are natural and essential for effectively reasoning about a person's preferences. However, they are complex to answer given a CP-net representation of preference. Second, learning a person's CP-net from observational data. In order to utilise a CP-net representation of a person's preferences, we must first determine the correct model. As direct elicitation is not always possible or practical, we must be able to learn CP-nets passively from the data we can observe. We provide two distinct methods of improving dominance testing efficiency for CP-nets. The first utilises a quantitative representation of preference in order to prune the associated search tree. The second reduces the size of a dominance testing problem by preprocessing the CP-net. Both methods are shown experimentally to significantly improve dominance testing efficiency. Furthermore, both are shown to outperform existing methods. These techniques can be combined with one another, and with the existing methods, in order to further improve efficiency. We also introduce a new, score-based learning technique for CP-nets. Most existing work on CP-net learning uses pairwise outcome preferences as data. However, such preferences are often impossible to observe passively from user actions, particularly in online settings, where users typically choose from a variety of options. Contrastingly, our method assumes a history of user choices as data, which is observable in a wide variety of contexts. Experimental evaluation of this method finds that the learned CP-nets show high levels of agreement with the true preference structures and with previously unseen (future) data

Representing and reasoning with qualitative preferences for compositional systems

Many applications call for techniques for representing and reasoning about preferences, i.e., relative desirability over a set of alternatives. Preferences over the alternatives are typically derived from preferences with respect to the various attributes of the alternatives (e.g., a student\u27s preference for one course over another may be influenced by his preference for the topic, the time of the day when the course is offered, etc.). Such preferences are often qualitative and conditional. When the alternatives are expressed as tuples of valuations of the relevant attributes, preferences between alternatives can often be expressed in the form of (a) preferences over the values of each attribute, and (b) relative importance of certain attributes over others. An important problem in reasoning with multi-attribute qualitative preferences is dominance testing, i.e., to find if one alternative (assignment to all attributes) is preferred over another. This problem is hard (PSPACE-complete) in general for well known qualitative conditional preference languages such as TCP-nets. We provide two practical approaches to dominance testing. First, we study a restricted unconditional preference language, and provide a dominance relation that can be computed in polynomial time by evaluating the satisfiability of an appropriately constructed logic formula. Second, we show how to reduce dominance testing for TCP-nets to reachability analysis in an induced preference graph. We provide an encoding of TCP-nets in the form of a Kripke structure for CTL. We show how to compute dominance using NuSMV, a model checker for CTL. We address the problem of identifying a preferred outcome in a setting where the outcomes or alternatives to be compared are composite in nature (i.e., collections of components that satisfy certain functional requirements). We define a dominance relation that allows us to compare collections of objects in terms of preferences over attributes of the objects that make up the collection, and show that the dominance relation is a strict partial order under certain conditions. We provide algorithms that use this dominance relation to identify only (sound), all (complete), or at least one (weakly complete) of the most preferred collections. We establish some key properties of the dominance relation and analyze the quality of solutions produced by the algorithms. We present results of simulation experiments aimed at comparing the algorithms, and report interesting conjectures and results that were derived from our analysis. Finally, we show how the above formalism and algorithms can be used in preference-based service composition, substitution, and adaptation

Towards autonomous decision-making: A probabilistic model for learning multi-user preferences

Information systems have revolutionized the provisioning of decision-relevant information, and decision support tools have improved human decisions in many domains. Autonomous decision- making, on the other hand, remains hampered by systems’ inability to faithfully capture human preferences. We present a computational preference model that learns unobtrusively from lim- ited data by pooling observations across like-minded users. Our model quantifies the certainty of its own predictions as input to autonomous decision-making tasks, and it infers probabilistic segments based on user choices in the process. We evaluate our model on real-world preference data collected on a commercial crowdsourcing platform, and we find that it outperforms both individual and population-level estimates in terms of predictive accuracy and the informative- ness of its certainty estimates. Our work takes an important step toward systems that act autonomously on their users’ behalf

Efficient operation of recharging infrastructure for the accommodation of electric vehicles: a demand driven approach

Large deployment and adoption of electric vehicles in the forthcoming years can have significant environmental impact, like mitigation of climate change and reduction of traffic-induced air pollutants. At the same time, it can strain power network operations, demanding effective load management strategies to deal with induced charging demand. One of the biggest challenges is the complexity that electric vehicle (EV) recharging adds to the power system and the inability of the existing grid to cope with the extra burden. Charging coordination should provide individual EV drivers with their requested energy amount and at the same time, it should optimise the allocation of charging events in order to avoid disruptions at the electricity distribution level. This problem could be solved with the introduction of an intermediate agent, known as the aggregator or the charging service provider (CSP). Considering out-of-home charging infrastructure, an additional role for the CSP would be to maximise revenue for parking operators. This thesis contributes to the wider literature of electro-mobility and its effects on power networks with the introduction of a choice-based revenue management method. This approach explicitly treats charging demand since it allows the integration of a decentralised control method with a discrete choice model that captures the preferences of EV drivers. The sensitivities to the joint charging/parking attributes that characterise the demand side have been estimated with EV-PLACE, an online administered stated preference survey. The choice-modelling framework assesses simultaneously out-of-home charging behaviour with scheduling and parking decisions. Also, survey participants are presented with objective probabilities for fluctuations in future prices so that their response to dynamic pricing is investigated. Empirical estimates provide insights into the value that individuals place to the various attributes of the services that are offered by the CSP. The optimisation of operations for recharging infrastructure is evaluated with SOCSim, a micro-simulation framework that is based on activity patterns of London residents. Sensitivity analyses are performed to examine the structural properties of the model and its benefits compared to an uncontrolled scenario are highlighted. The application proposed in this research is practice-ready and recommendations are given to CSPs for its full-scale implementation.Open Acces

The local geometry of multiattribute tradeoff preferences

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.Includes bibliographical references (p. 125-129).Existing preference reasoning systems have been successful in simple domains. Broader success requires more natural and more expressive preference representations. This thesis develops a representation of logical preferences that combines numerical tradeoff ratios between partial outcome descriptions with qualitative preference information. We argue our system is unique among preference reasoning systems; previous work has focused on qualitative or quantitative preferences, tradeoffs, exceptions and generalizations, or utility independence, but none have combined all of these expressions under a unified methodology. We present new techniques for representing and giving meaning to quantitative tradeoff statements between different outcomes. The tradeoffs we consider can be multi-attribute tradeoffs relating more than one attribute at a time, they can refer to discrete or continuous domains, be conditional or unconditional, and quantified or qualitative. We present related methods of representing judgments of attribute importance. We then build upon a methodology for representing arbitrary qualitative ceteris paribus preference, or preferences "other things being equal," as presented in [MD04].(cont.) Tradeoff preferences in our representation are interpreted as constraints on the partial derivatives of the utility function. For example, a decision maker could state that "Color is five times as important as price, availability, and time," a sentiment one might express in the context of repainting a home, and this is interpreted as indicating that utility increases in the positive color direction five times faster than utility increases in the positive price direction. We show that these representations generalize both the economic notion of marginal rates of substitution and previous representations of preferences in AI.by Michael McGeachie.Ph.D

Local Geometry of Multiattribute Tradeoff Preferences

PhD thesisExisting preference reasoning systems have been successful insimple domains. Broader success requires more natural and moreexpressive preference representations. This thesis develops arepresentation of logical preferences that combines numericaltradeoff ratios between partial outcome descriptions withqualitative preference information. We argue our system is uniqueamong preference reasoning systems; previous work has focused onqualitative or quantitative preferences, tradeoffs, exceptions andgeneralizations, or utility independence, but none have combinedall of these expressions under a unified methodology.We present new techniques for representing and giving meaning toquantitative tradeoff statements between different outcomes. Thetradeoffs we consider can be multi-attribute tradeoffs relatingmore than one attribute at a time, they can refer to discrete orcontinuous domains, be conditional or unconditional, andquantified or qualitative. We present related methods ofrepresenting judgments of attribute importance. We then buildupon a methodology for representing arbitrary qualitative ceteris paribuspreference, or preferences ``other things being equal," aspresented in MD04. Tradeoff preferences inour representation are interpreted as constraints on the partialderivatives of the utility function. For example, a decision makercould state that ``Color is five times as important as price,availability, and time," a sentiment one might express in thecontext of repainting a home, and this is interpreted asindicating that utility increases in the positive color directionfive times faster than utility increases in the positive pricedirection. We show that these representations generalize both theeconomic notion of marginal rates of substitution and previousrepresentations of preferences in AI