Preferences in AI: An overview

Domshlak, Carmel; Hüllermeier, Eyke; Kaci, Souhila; Prade, Henri

doi:10.1016/j.artint.2011.03.004

Cited by 160 publications

(111 citation statements)

References 122 publications

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“…In database research, there are also several solutions, for instance, using top-k or skyline algorithms to obtain the best search items according to a stated preference [22,20]. These preference models usually offer qualitative facilities to define preferences, though their implementation usually leads to large result sets that do not discriminate well between items to be ordered [15]. We have chosen SOUP [4] because it is a hybrid approach, as it combines quantitative and qualitative facilities to define preferences.…”

Section: Related Workmentioning

confidence: 99%

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Priority-Based Human Resource Allocation in Business Processes

Cabanillas

García

Resinas

et al. 2013

Service-Oriented Computing

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Abstract. In Business Process Management Systems, human resource management typically covers two steps: resource assignment at design time and resource allocation at run time. Although concepts like rolebased assignment often yield several potential performers for an activity, there is a lack of mechanisms for prioritizing them, e.g., according to their skills or current workload. In this paper, we address this research gap. More specifically, we introduce an approach to define resource preferences grounded on a validated, generic user preference model initially developed for semantic web services. Furthermore, we show an implementation of the approach demonstrating its feasibility.

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Section: Related Workmentioning

confidence: 99%

“…There are several formalisms that can be used to represent preferences in different fields [15]. Quantitative preferences modeled as utility or scoring functions have been widely used in economics and operations research [16,17], as well as in web systems [18,19].…”

Section: Related Workmentioning

confidence: 99%

Priority-Based Human Resource Allocation in Business Processes

Cabanillas

García

Resinas

et al. 2013

Service-Oriented Computing

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“…As we know, we shall be able to achieve optimised results only when we would be able to integrate data from a variety of different sources and then devise an automated learning algorithm to analyse and infer prediction based on previous learning experiences. Machines are thus able to serve request from users as well as from other servers more efficiently utilising minimum computational power [107][108][109][110][111][112][113][114].…”

Section: Integration Of Biological Databases With Aimentioning

confidence: 99%

Artificial Intelligence in Biological Data

Chakraborty¹,

Choudhury²

2017

J Inform Tech Softw Eng

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Artificial Intelligence (AI) or Machine learning in present era, serves as the primary choice for data mining and big data analysis. With effective learning and adaptation model, it provides solutions to several engineering applications. These include techniques such as Artificial Neural Network modelling, Reasoning based decision algorithms, Simulation models, DNA computing and Quantum computing among several others. With the application of AI in Biomedical research, the fuzziness and randomness in handling such type of data has significantly reduced. Rapid technological advancements have helped AI techniques evolve in manner which promotes handling such fuzzy data effectively and much more conveniently. The review presents a comprehensive view of machine learning and AI computing models, advanced data analytics and optimisation approaches used in Bioengineering such as Drug Designing and Analysis, Medical imaging, biologically inspired learning and adaption for analytics, etc.

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“…Additional applications include: meta-learning (Vilalta and Drissi 2002), where, given a new data set, the task is to induce a total rank of available algorithms according to their suitability based on the data set properties; ranking of movie suggestions for new members of a movie website based on user features; determining an order of questions in a survey for a specific user based on respondent's attributes. See Domshlak et al (2011) for an overview of label ranking applications in economics, operations research, and databases.…”

Section: Introductionmentioning

confidence: 99%

Supervised clustering of label ranking data using label preference information

et al. 2013

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This paper studies supervised clustering in the context of label ranking data. The goal is to partition the feature space into K clusters, such that they are compact in both the feature and label ranking space. This type of clustering has many potential applications. For example, in target marketing we might want to come up with K different offers or marketing strategies for our target audience. Thus, we aim at clustering the customers' feature space into K clusters by leveraging the revealed or stated, potentially incomplete customer preferences over products, such that the preferences of customers within one cluster are more similar to each other than to those of customers in other clusters. We establish several baseline algorithms and propose two principled algorithms for supervised clustering. In the first baseline, the clusters are created in an unsupervised manner, followed by assigning a representative label ranking to each cluster. In the second baseline, the label ranking space is clustered first, followed by partitioning the feature space based on the central rankings. In the third baseline, clustering is applied on a new feature space consisting of both features and label rankings, followed by mapping back to the original feature and ranking space. The RankTree principled approach is based on a Ranking Tree algorithm previously proposed for label ranking prediction. Our modification starts with K random label rankings and iteratively splits the feature space to minimize the ranking loss, followed by re-calculation of the K rankings based on cluster assignments. The MM-PL approach is a Learn (2013) 93:191-225 multi-prototype supervised clustering algorithm based on the Plackett-Luce (PL) probabilistic ranking model. It represents each cluster with a union of Voronoi cells that are defined by a set of prototypes, and assign each cluster with a set of PL label scores that determine the cluster central ranking. Cluster membership and ranking prediction for a new instance are determined by cluster membership of its nearest prototype. The unknown cluster PL parameters and prototype positions are learned by minimizing the ranking loss, based on two variants of the expectation-maximization algorithm. Evaluation of the proposed algorithms was conducted on synthetic and real-life label ranking data by considering several measures of cluster goodness: (1) cluster compactness in feature space, (2) cluster compactness in label ranking space and (3) label ranking prediction loss. Experimental results demonstrate that the proposed MM-PL and RankTree models are superior to the baseline models. Further, MM-PL is has shown to be much better than other algorithms at handling situations with significant fraction of missing label preferences.

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Preferences in AI: An overview

Cited by 160 publications

References 122 publications

Priority-Based Human Resource Allocation in Business Processes

Priority-Based Human Resource Allocation in Business Processes

Artificial Intelligence in Biological Data

Supervised clustering of label ranking data using label preference information

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