Qualitative Preferences Research Articles

Recycling Plants Layout Design by Means of an Interactive Genetic Algorithm

Facility Layout Design is known to be very important for attaining production efficiency because it directly influences manufacturing costs, lead times, work in process and productivity. Facility Layout problems have been addressed using several approaches. Unfortunately, these approaches only take into account quantitative criteria. However, there are qualitative preferences referred to the knowledge and experience of the designer, which should also be considered in facility layout design. These preferences can be subjective, not known in advance and changed during the design process, so that, it is difficult to include them using a classic optimization approach. For that reason, we propose the use of an Interactive Genetic Algorithm (IGA) for designing the layout of two real recycling plants taking into consideration subjective features from the designer. The designer's knowledge guides the evolution of the algorithm evaluating facility layouts in each generation adjusting the search to his/her preferen...

Clinical Evaluation of Single-Shot and Readout-Segmented Diffusion-Weighted Imaging in Stroke Patients at 3 T

Diffusion-weighted imaging (DWI) magnetic resonance imaging (MRI) is most commonly performed utilizing a single-shot echo-planar imaging technique (ss-EPI). Susceptibility artifact and image blur are severe when this sequence is utilized at 3 T. To evaluate a readout-segmented approach to DWI MR in comparison with single-shot echo planar imaging for brain MRI. Eleven healthy volunteers and 14 patients with acute and early subacute infarctions underwent DWI MR examinations at 1.5 and 3T with ss-EPI and readout-segmented echo-planar (rs-EPI) DWI at equal nominal spatial resolutions. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) calculations were made, and two blinded readers ranked the scans in terms of high signal intensity bulk susceptibility artifact, spatial distortions, image blur, overall preference, and motion artifact. SNR and CNR were greatest with rs-EPI (8.1 ± 0.2 SNR vs. 6.0 ± 0.2; P <10(-4) at 3T). Spatial distortions were greater with single-shot (0.23 ± 0.03 at 3T; P <0.001) than with rs-EPI (0.12 ± 0.02 at 3T). Combined with blur and artifact reduction, this resulted in a qualitative preference for the readout-segmented scans overall. Substantial image quality improvements are possible with readout-segmented vs. single-shot EPI - the current clinical standard for DWI - regardless of field strength (1.5 or 3 T). This results in improved image quality secondary to greater real spatial resolution and reduced artifacts from susceptibility in MR imaging of the brain.

Combining approaches for solving satisfiability problems with qualitative preferences

The ability to effectively reason in the presence of qualitative preferences on literals or formulas is a central issue in Artificial Intelligence. In the last few years, two procedures have been presented in order to reason with propositional satisfiability SAT problems in the presence of additional, partially ordered qualitative preferences on literals or formulas: the first requires a modification of the branching heuristic of the SAT solver in order to guarantee that the first solution is optimal, while the second computes a sequence of solutions, each guaranteed to be better than the previous one. The two approaches have their own advantages and disadvantages and when compared on specific classes of instances --each having an empty partial order --the second seems to have superior performance.In this paper we show that the above two approaches for reasoning with qualitative preferences can be combined yielding a new effective procedure. In particular, in the new procedure we modify the branching heuristic --as in the first approach --by possibly changing the polarity of the returned literal, and then we continue the search --as in the second approach --looking for better solutions. We extended the experimental analysis conducted in previous papers by considering a wide variety of problems, having both an empty and a non-empty partial order: the results show that the new procedure performs better than the two previous approaches on average, and especially on the “hard” problems. As a preliminary result, we show that the framework of qualitative preferences on literals is more general and expressive than the framework on quantitative preferences.

Synthesizing iterators from abstraction functions

A technique for synthesizing iterators from declarative abstraction functions written in a relational logic specification language is described. The logic includes a transitive closure operator that makes it convenient for expressing reachability queries on linked data structures. Some optimizations, including tuple elimination, iterator flattening, and traversal state reduction, are used to improve performance of the generated iterators. A case study demonstrates that most of the iterators in the widely used JDK Collections classes can be replaced with code synthesized from declarative abstraction functions. These synthesized iterators perform competitively with the hand-written originals. In a user study the synthesized iterators always passed more test cases than the hand-written ones, were almost always as efficient, usually took less programmer effort, and were the qualitative preference of all participants who provided free-form comments.

E-marketing under the adverse selection environment: model and case study

Information asymmetry in the e-commerce market causes an adverse selection phenomenon ('lemons' problem) that has a negative impact on e-marketing. Based on the classic adverse selection model, this paper set up e-marketing models. The models fully took into account the qualitative preference of e-consumers and the role of quality intermediaries under adverse selection environments. Actual data from Taobao serves as a case study to analyse the characteristics of adverse selection. The results show that reputation evaluation systems and business alliance system can reduce the adverse selection problem in e-commerce market. The quality intermediary as a trusted third party or market quality expert can help eliminate cyber lemons. However, along with the e-marketing strategy counteracting adverse selection, there are some phenomena strengthening consumers' distrust. This may result in a vicious cycle and affect the function of e-marketing. This paper analyses the existing issues and suggests solutions to enable e-sellers and e-buyers to trade credibly.

Dealing with explicit preferences and uncertainty in answer set programming

In this paper, we show how the formalism of Logic Programs with Ordered Disjunction (LPODs) and Possibilistic Answer Set Programming (PASP) can be merged into the single framework of Logic Programs with Possibilistic Ordered Disjunction (LPPODs). The LPPODs framework embeds in a unified way several aspects of common-sense reasoning, nonmonotonocity, preferences, and uncertainty, where each part is underpinned by a well established formalism. On one hand, from LPODs it inherits the distinctive feature of expressing context-dependent qualitative preferences among different alternatives (modeled as the atoms of a logic program). On the other hand, PASP allows for qualitative certainty statements about the rules themselves (modeled as necessity values according to possibilistic logic) to be captured. In this way, the LPPODs framework supports a reasoning which is nonmonotonic, preference- and uncertainty-aware. The LPPODs syntax allows for the specification of (1) preferences among the exceptions to default rules, and (2) necessity values about the certainty of program rules. As a result, preferences and uncertainty can be used to select the preferred uncertain default rules of an LPPOD and, consequently, to order its possibilistic answer sets. Furthermore, we describe the implementation of an ASP-based solver able to compute the LPPODs semantics.

Directness as a key performance indicator for freight transport chains

From the perspective of a transport service buyer and at the abstraction level of material flows, all transports travel directly from product supplier to product customer. In reality, however, the directness of transport services depends on factors such as geography, available infrastructure, temporary conditions, shippers' qualitative preferences, the economy of and practical possibilities for consolidation and access to return flows. This work examines directness by structuring and elaborating upon the causes of freight transport detours and briefly analysing their effect. The article also includes a discussion about the prospects of capturing directness in a KPI, and how such a measure can be designed, measured, monitored and used, as well as a brief analysis of the consequences of using it for monitoring and controlling supply chain performance.Detours are divided into supply chain, logistics and freight transport detours respectively and most attention is paid to the last kind of detour. Freight transport detours are divided into physical, political, commercial, operational and non-planned causes for detours. The first two stipulate the system environment in which the focused actors, transport service providers, decide upon detours. Operational causes are subject to internal decision making whereas commercial and non-planned causes are both external and internal to transport service providers.

A Collaborative QoS-Aware Service Evaluation Method Among Multi-Users for a Shared Service

In service selection, an end user often has his or her personal preferences imposing on a candidate service’s non-functional properties. For a service selection process promoted by a group of users, candidate services are often evaluated by a group of end users who may have different preferences or priorities. In this situation, it is often a challenging effort to make a tradeoff among various preferences or priorities of the users. In view of this challenge, a multi-criteria decision-making method, named AHP (Analytic Hierarchy Process), is introduced to transform both qualitative personal preferences and users’ priorities into numeric weights. Furthermore, a QoS-aware service evaluation method is presented for a shared service’s co-selection taking advantage of AHP theory. At last, a case study is presented to demonstrate the feasibility of the method.

Representing and reasoning with qualitative preferences for compositional systems

Many applications, e.g., Web service composition, complex system design, team formation, etc., rely on methods for identifying collections of objects or entities satisfying some functional requirem...

PREFERENCE DOMINANCE REASONING FOR CONVERSATIONAL RECOMMENDER SYSTEMS: A COMPARISON BETWEEN A COMPARATIVE PREFERENCES AND A SUM OF WEIGHTS APPROACH

A conversational recommender system iteratively shows a small set of options for its user to choose between. In order to select these options, the system may analyze the queries tried by the user to derive whether one option is dominated by others with respect to the user's preferences. The system can then suggest that the user try one of the undominated options, as they represent the best options in the light of the user preferences elicited so far. This paper describes a framework for preference dominance. Two instances of the framework are developed for query suggestion in a conversational recommender system. The first instance of the framework is based on a basic quantitative preferences formalism, where options are compared using sums of weights of their features. The second is a qualitative preference formalism, using a language that generalises CP-nets, where models are a kind of generalised lexicographic order. A key feature of both methods is that deductions of preference dominance can be made efficiently, since this procedure needs to be applied for many pairs of options. We show that, by allowing the recommender to focus on undominated options, which are ones that the user is likely to be contemplating, both approaches can dramatically reduce the amount of advice the recommender needs to give to a user compared to what would be given by systems without this kind of reasoning.

Achieving green outsourcing performance in uncertainty

The international business environments advocate green outsourcing performance (GOP) as fundamental enablers and criteria for the competitiveness of original equipment manufacturing (OEM) firms. Few studies providing integrated competitive advantages, enablers and criteria (called measures), exist, and are suitable for practical adoption to enhance GOP. This study presents an original approach to identify the most appropriate method that firms should implement, starting from competitive advantages to internal processes in an intensive market. The approach is based on quality function deployment (QFD), and in particular, all measures with interdependence relationships are given qualitative preferences, which are rarely applied in the literature. The whole scaffold exploits fuzzy set theory and analytical network processes to translate linguistic preferences required for interdependence relationships into numerical values; the QFD translates the result into a final criteria ranking system. A case study, grounded on data available from an OEM electronic firm, is proposed and discussed to show the application of the developed tool. Key words: Quality function deployment, green outsourcing performance, analytical network process, fuzzy set theory.

Complex optimization in answer set programming

AbstractPreference handling and optimization are indispensable means for addressing nontrivial applications in Answer Set Programming (ASP). However, their implementation becomes difficult whenever they bring about a significant increase in computational complexity. As a consequence, existing ASP systems do not offer complex optimization capacities, supporting, for instance, inclusion-based minimization or Pareto efficiency. Rather, such complex criteria are typically addressed by resorting to dedicated modeling techniques, likesaturation. Unlike the ease of common ASP modeling, however, these techniques are rather involved and hardly usable by ASP laymen. We address this problem by developing a general implementation technique by means of meta-prpogramming, thus reusing existing ASP systems to capture various forms of qualitative preferences among answer sets. In this way, complex preferences and optimization capacities become readily available for ASP applications.

Development and Strengthening of Health Clusters in European Health Regions

In recent years, health clusters have been limited in their ability to meet the demands and serve the interests of population groups and social segments in a comprehensive manner at healthcare sites within health regions. The reasons for this lay in the low degree of willingness of the stakeholders to deal with the growing challenges and resulting outlooks in healthcare. Questions concerning quality in health clusters were left unasked to give weight to the local and regional effectiveness regarding employment and growth. With the focus on ensuring long-term survival and creating value, the strategies for efficiency were given precedence over the strategies for effectiveness in health regions. For instance, the following questions remained unanswered: How can rural areas be included in the regional area of existing health clusters? To what extent are health clusters dependent on the inter-regional periphery and on intra-regional positioning and self-presentation of the individual stakeholders? Which quality indicators are suitable for health regions to reflect the positioning and self-presentation in order to promote their role in healthcare in order to attract clients? This paper aims to clarify which regional qualitative circumstances, preferences and restrictions exist in the immediate environment of the stakeholders; how dependencies can be used to an advantage; and how the appearance and presentation of a region can be strengthened. Health clusters can create quality structures and processes that allow them to offer high-quality health services on site or at a neighbourhood location. To strengthen the structures and processes offered daily, these structures and processes are to be researched and developed through the involvement of customers and regional health partners. Through networking the existing regional health initiatives, an overall strategy and profile are created and developed into an overall health cluster of health services in a region.

Preferences for relational style with mental health clinicians: A qualitative comparison of African American, Latino and Non‐Latino White patients

The goal of this study is to analyze preferences for relational styles in encounters with mental health providers across racial and ethnic groups. Four primary themes describe what patients want from a mental health provider: listening, understanding, spending time, and managing differences. However, using contextual comparative analysis, the findings explicate how these themes are described differently across African Americans, Latinos, and non-Latino Whites, uncovering important qualitative differences in the meaning of these themes across the groups. The article suggests that closer attention to qualitative preferences for style of interaction with providers may help address disparities in mental health care for racial and ethnic minorities.

Solving satisfiability problems with preferences

Propositional satisfiability (SAT) is a success story in Computer Science and Artificial Intelligence: SAT solvers are currently used to solve problems in many different application domains, including planning and formal verification. The main reason for this success is that modern SAT solvers can successfully deal with problems having millions of variables. All these solvers are based on the Davis–Logemann–Loveland procedure (dll). In its original version, dll is a decision procedure, but it can be very easily modified in order to return one or all assignments satisfying the input set of clauses, assuming at least one exists. However, in many cases it is not enough to compute assignments satisfying all the input clauses: Indeed, the returned assignments have also to be “optimal” in some sense, e.g., they have to satisfy as many other constraints—expressed as preferences—as possible. In this paper we start with qualitative preferences on literals, defined as a partially ordered set (poset) of literals. Such a poset induces a poset on total assignments and leads to the definition of optimal model for a formula ψ as a minimal element of the poset on the models of ψ. We show (i) how dll can be extended in order to return one or all optimal models of ψ (once converted in clauses and assuming ψ is satisfiable), and (ii) how the same procedures can be used to compute optimal models wrt a qualitative preference on formulas and/or wrt a quantitative preference on literals or formulas. We implemented our ideas and we tested the resulting system on a variety of very challenging structured benchmarks. The results indicate that our implementation has comparable performances with other state-of-the-art systems, tailored for the specific problems we consider.

Speaker coverage in active acoustic music practice rooms.

Larger music practice rooms (&amp;gt;25 m3) present unique challenges compared to smaller, traditional practice rooms (6–12 m3) when employing active acoustics technology and integrated digital recording. An experiment was conducted comparing a ceiling array of 32 speakers in a large music practice room (5.3×5.7×2.7 m3, 81 m3) with that of eight speakers located in corners of that room (one at the top and one at the bottom in each corner facing parallel to the walls). The goal was to determine which speaker configuration would be the most effective for creating even sound field coverage and uniform frequency response. Data were collected for both seated and standing positions in the room from a matrix of 42 locations. Frequencies from 40 Hz to 8 kHz were plotted for analysis. The data were compared to the qualitative preferences expressed by a number of musicians using the rooms for both practice sessions (with the active acoustics enabled) and listening to the playback of recorded practice sessions.

Improving the User Interface and Adoption of Online Personal Health Records

In winter 2008/2009, we conducted an independent comparative research study of two existing online Personal Health Record (PHR) applications: Google Health and Microsoft HealthVault. The goals of the study were to identify areas associated with the most errors or missteps and to determine which functions and features were most preferred by potential users. Thirty participants completed a set of tasks using each PHR application and provided qualitative feedback and preference data on five dimensions: usability, utility, security, privacy, and trust. Overall, participants navigated more efficiently and entered data more quickly using Google Health. Consequently, they indicated that they found Google Health more usable than Microsoft HealthVault. Participants also appreciated the fact that Google Health utilized more familiar medical terminology and provided a persistent health information profile summary. Although both PHR systems contain useful features, including the ability to share medical information with a physician, we found that Google Health had a slight edge in perceived overall utility because of important drug interaction information. Finally, in terms of security, privacy and trust, there was a slight preference for Microsoft HealthVault because of its strong brand presence, professional look-and-feel, and more friendly privacy and security language.

Identifying superior wheat cultivars in participatory research on resource poor farms

Improving livelihood of resource poor farmers is an important goal of wheat research in developing countries. Although remarkable success has been achieved to date in developing widely adapted wheat cultivars, many resource poor farmers in marginal areas in developing world have not benefited. Participatory research could greatly enhance identifying cultivars according to the choice of the poor farmers. This study was conducted to examine how farmers’ selection criteria could assist breeders in identifying superior wheat cultivars, and determine if a new statistical analysis tool, GGE biplot, could be effectively used in selection of improved cultivar based on quantitative (grain yield) and qualitative data (farmers’ preference score). The field experiments were conducted in 3 years (2003–2005) in three mid-hill districts in the central Nepal involving resource poor wheat farmers. Sixteen wheat genotypes, including a long-term and a current commercial cultivar, were used in the study. Data were collected on agronomic traits considered important by the participating farmers. These included days to heading and maturity, plant height, effective tiller number, spike length, kernel per spike, 1000-kernel weight and grain yield. Farmers also qualitatively scored each genotype for multiple traits based on their preference. In general, the farmers used the same traits in selecting a superior cultivar that are used by breeders. However, relative importance of different traits differed, not necessarily following in line with the breeder preference. The cultivar superiority based on quantitative agronomic data (breeders’ criteria) and qualitative preference scores (farmers’ criteria) often showed synergies, however, there were differences as well. This indicates farmers’ ability to choose superior cultivars based on qualitative observation compared to tedious quantitative data recording in the on-station testing. In the first year, a greater number of farmers selected improved check as a better choice than recent advanced breeding lines. In the 2nd and 3rd years, the farmers preferred genotypes other than the checks. This underlines the importance of testing of advanced materials in farmers’ fields in multiple years. Principal component analysis using GGE-biplot was useful in identifying superior genotypes based on both quantitative and qualitative data recorded across environments. This approach could be useful in analyzing data from participatory agricultural research conducted under highly diverse farmers’ field conditions where it is easier to record observations on qualitative than quantitative scale. This technique can also be extended to on-farm participatory testing of other technologies. The findings bear implications for a broad range of participatory research and technology evaluation and verification.

A heuristic search approach to planning with temporally extended preferences

Planning with preferences involves not only finding a plan that achieves the goal, it requires finding a preferred plan that achieves the goal, where preferences over plans are specified as part of the planner's input. In this paper we provide a technique for accomplishing this objective. Our technique can deal with a rich class of preferences, including so-called temporally extended preferences (TEPs). Unlike simple preferences which express desired properties of the final state achieved by a plan, TEPs can express desired properties of the entire sequence of states traversed by a plan, allowing the user to express a much richer set of preferences. Our technique involves converting a planning problem with TEPs into an equivalent planning problem containing only simple preferences. This conversion is accomplished by augmenting the inputed planning domain with a new set of predicates and actions for updating these predicates. We then provide a collection of new heuristics and a specialized search algorithm that can guide the planner towards preferred plans. Under some fairly general conditions our method is able to find a most preferred plan—i.e., an optimal plan. It can accomplish this without having to resort to admissible heuristics, which often perform poorly in practice. Nor does our technique require an assumption of restricted plan length or make-span. We have implemented our approach in the HPlan-P planning system and used it to compete in the 5th International Planning Competition, where it achieved distinguished performance in the Qualitative Preferences track.

Elicitation of Factored Utilities

The effective tailoring of decisions to the needs and desires of specific users requires automated mechanisms for preference assessment. We provide a brief overview of recent direct preference elicitation methods: these methods ask users to answer (ideally, a small number of) queries regarding their preferences and use this information to recommend a feasible decision that would be (approximately) optimal given those preferences. We argue for the importance of assessing numerical utilities rather than qualitative preferences and survey several utility elicitation techniques from artificial intelligence, operations research, and conjoint analysis.