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Preference programming: Advanced problem solving for configuration

2003 Artificial intelligence for engineering design, analysis and manufacturing  
Preference programming provides a new paradigm for expressing (default) decisions, preferences between decisions, and search strategies in a declarative and unified way and for embedding them in a constraint  ...  Business experts can thus directly specify preferences and search directives in form of rules without needing to program search strategies as required by constraint programming based configuration tools  ...  For helpful remarks, I also want to thank Xavier Ceugniet, Daniel Mailharro, and Jean-François Puget.  ... 
doi:10.1017/s089006040317103x fatcat:2drga6t4inhudj63lg324dt2py

Constructive Preference Elicitation

Paolo Dragone, Stefano Teso, Andrea Passerini
2018 Frontiers in Robotics and AI  
In this article, we describe recent approaches especially designed for constructive problems, outlining the underlying ideas and their differences as well as their limitations.  ...  They do so by interactively learning the DM's preferences through appropriately chosen queries and suggesting high-quality outcomes based on the preference estimates.  ...  This allows to encode arbitrary inference problems using arbitrary modeling frameworks, e.g., constraint programming in our case, to solve various constructive applications, as shown in the next sections  ... 
doi:10.3389/frobt.2017.00071 fatcat:kjp2a2e6sjchzausnyqeur6tym

Fuzzy Preference Programming Framework for Functional Assessment of Subway Networks

Mona Abouhamad, Tarek Zayed
2020 Algorithms  
The model uses the Fuzzy Analytical Network Process with application to Fuzzy Preference Programming to calculate the weights for seven failure impact attributers and seven criticality attributes.  ...  With multiple assets competing for the limited fund, new methodologies are required to prioritize assets for rehabilitation.  ...  It can be concluded that FPP transforms the prioritization problem into a linear program that can easily be solved by standard simplex technique.  ... 
doi:10.3390/a13090220 fatcat:m7za7ysewnezxlsjvrnwtaon34

Trends in preference, programming and design of concert halls for symphonic music

Anders C. Gade
2008 Journal of the Acoustical Society of America  
Using this impedance condition a finite element problem can be solved by standard methods.  ...  Listeners' ability to parse concurrent sounds is a prerequisite in solving the cocktail party problem.  ...  In this paper, a low-damping vibration absorber is studied for solving noise radiation problems from a resonant, light-gauge structure driven by a force whose frequency and amplitude are constant and whose  ... 
doi:10.1121/1.2932471 fatcat:hhka5ml2fvcszklkzau2lw3gka

Learning Modulo Theories for preference elicitation in hybrid domains [article]

Paolo Campigotto, Roberto Battiti, Andrea Passerini
2015 arXiv   pre-print
While no competitors exist in the hybrid setting, CLEO outperforms a state-of-the-art Bayesian preference elicitation algorithm when applied to a purely discrete task.  ...  This paper introduces CLEO, a novel preference elicitation algorithm capable of recommending complex objects in hybrid domains, characterized by both discrete and continuous attributes and constraints  ...  Figure 2 : 2 Performance of CLEO while solving the housing problem.  ... 
arXiv:1508.04261v3 fatcat:zagrnbte3zfmljfbzq4ub3t4ru

Decomposition Strategies for Constructive Preference Elicitation [article]

Paolo Dragone, Stefano Teso, Mohit Kumar, Andrea Passerini
2018 arXiv   pre-print
We propose a decomposition technique for large preference-based decision problems relying exclusively on inference and feedback over partial configurations.  ...  In this setting, the suggested configuration is synthesized on-the-fly by solving a constrained optimization problem, while the preferences are learned itera tively by interacting with the user.  ...  Copyright c 2018, Association for the Advancement of Artificial Intelligence ( All rights reserved. customer's preferences.  ... 
arXiv:1711.08247v2 fatcat:bcelxdnjcbc3jcmejjbbwffipq

Preferences and Nonmonotonic Reasoning

Gerhard Brewka, Ilkka Niemela, Miroslaw Truszczynski
2008 The AI Magazine  
Conflicts among program rules (more generally, defaults) give rise to alternative preferred belief sets.  ...  Selecting extended logic programming with the answer-set semantics as a "generic" nonmonotonic logic, we show how that logic defines preferred belief sets and how preferred belief sets allow us to represent  ...  Acknowledgments We thank the reviewers for helpful comments. Ilkka Niemelä acknowledges the support of Academy of Finland grant 122399.  ... 
doi:10.1609/aimag.v29i4.2179 fatcat:2qrcipfj3fd3lirr24go6smm5m

Expressing advanced user preferences in component installation [article]

Ralf Treinen
2009 arXiv   pre-print
We present an architecture that allows to express advanced user preferences about package selection in FOSS distributions.  ...  The architecture is composed by a distribution-independent format for describing available and installed packages called CUDF (Common Upgradeability Description Format), and a foundational language called  ...  While suitable and complete techniques to provide dependency solving completeness are now well-known [9] and "just" lack widespread adoption, handling of complex user preferences is a novel problem for  ... 
arXiv:0909.5091v1 fatcat:54owtua4i5d5th46culwabubvm

Sequential decision making with partially ordered preferences

Daniel Kikuti, Fabio Gagliardi Cozman, Ricardo Shirota Filho
2011 Artificial Intelligence  
Sequential decision making under uncertainty Partially ordered preferences Sets of probability measures Criteria of choice Consequentialist and resolute norms Linear and multilinear programming This paper  ...  presents new insights and novel algorithms for strategy selection in sequential decision making with partially ordered preferences; that is, where some strategies may be incomparable with respect to expected  ...  To find a * we must solve n optimization programs; to determine the set of admissible actions we must solve n − 1 additional optimization programs.  ... 
doi:10.1016/j.artint.2010.11.017 fatcat:jsjoszponrfxdiw4daummduwpq

Predicting Academic Course Preference using Hadoop

2019 International journal of recent technology and engineering  
To avoid this problem we are using Mapreduce for decision making of students in order to choose their preferred course for industrial training purpose for their effective learning techniques to increase  ...  And this leads to huge amounts of data which makes a big challenge for the students to store the preferred course.  ...  The advanced algorithms have been coded by the knowledge scientists for frameworks so that it becomes easy to use for the programmers.  ... 
doi:10.35940/ijrte.d7123.118419 fatcat:h7m7eakqz5eybmpsyjsabwultu

Evolving Random Forest for Preference Learning [chapter]

Mohamed Abou-Zleikha, Noor Shaker
2015 Lecture Notes in Computer Science  
The combination of these two efficient methods for evolution and modelling yields a powerful technique for learning pairwise preferences.  ...  This paper introduces a novel approach for pairwise preference learning through a combination of an evolutionary method and random forest.  ...  Martinez for valuable discussions.  ... 
doi:10.1007/978-3-319-16549-3_26 fatcat:bkklabw3gfdbdmd7ljdakxqnfm

Preferences in artificial intelligence

Gabriella Pigozzi, Alexis Tsoukiàs, Paolo Viappiani
2015 Annals of Mathematics and Artificial Intelligence  
of constraint programming in order to solve preference aggregation and recommendation problems.  ...  can be solved (see for instance [257] ).  ... 
doi:10.1007/s10472-015-9475-5 fatcat:3wspv5ycirgapjv6u2bxpcd6p4

Arc Consistency for Constrained Lexicographic Preference Trees

Eisa Alanazi
2020 IEEE Access  
The empirical results conducted on the new structure show the feasibility of applying AC to further simplify the problem and solve it.  ...  For more information, see VOLUME 8, 2020 INDEX TERMS  ...  For instance, in product configuration, the user has preferences on different components of the product but some components are not compatible with the each other.  ... 
doi:10.1109/access.2020.2983283 fatcat:5ymtye4vmvdkzpu3tsxa4owifu

Weight Constraints with Preferences in ASP [chapter]

Stefania Costantini, Andrea Formisano
2011 Lecture Notes in Computer Science  
Preference-based reasoning is a form of commonsense reasoning that makes many problems easier to express and sometimes more likely to have a solution.  ...  In this paper, we present an approach to introducing preferences in the weight constraint construct, which is a very useful programming construct widely adopted in Answer Set Programming (ASP).  ...  For instance, the motivating example discussed in [8] concerns scheduling problems to be solved according to both preferences and priorities.  ... 
doi:10.1007/978-3-642-20895-9_24 fatcat:syqs4pifvfgfbblnfdkxidz3ny


2019 2019 IEEE Global Conference on Internet of Things (GCIoT)  
The existing proposals to solve the Massive Access Problem model the traffic generation pattern of each IoT device via random arrivals.  ...  In contrast, our JFS system forecasts the traffic generation pattern of each IoT device and schedules the transmissions of these devices in advance.  ...  Thus it can be used to derive security configurations for each individual embedded device, it's services and the network as a whole.  ... 
doi:10.1109/gciot47977.2019.9058389 fatcat:z62yf6bds5flhdatcvsuzzkj5q
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