Learning Modulo Theories for constructive preference elicitation

Peintner, 2008, Preferences in interactive systems: technical challenges and case studies, AI Mag., 29, 13 March, 1978, Bounded rationality, ambiguity, and the engineering of choice, Bell J. Econ., 9, 587, 10.2307/3003600 Domshlak, 2011, Preferences in AI: an overview, Artif. Intell., 175, 1037, 10.1016/j.artint.2011.03.004 Pigozzi, 2016, Preferences in artificial intelligence, Ann. Math. Artif. Intell., 77, 361, 10.1007/s10472-015-9475-5 Guo, 2010, Real-time multiattribute Bayesian preference elicitation with pairwise comparison queries, J. Mach. Learn. Res. - Proc. Track, 9, 289 Braziunas, 2007, Minimax regret based elicitation of generalized additive utilities, 25 Boutilier, 2010, Simultaneous elicitation of preference features and utility, 1160 Boutilier, 2006, Constraint-based optimization and utility elicitation using the minimax decision criterion, Artif. Intell., 170, 686, 10.1016/j.artint.2006.02.003 Bonilla, 2010, Gaussian process preference elicitation, 262 Viappiani, 2012, Monte Carlo methods for preference learning Birlutiu, 2012, Efficiently learning the preferences of people, Mach. Learn., 1 Gelain, 2010, Elicitation strategies for soft constraint problems with missing preferences: properties, algorithms and experimental studies, Artif. Intell., 174, 270, 10.1016/j.artint.2009.11.015 Yu, 2011, Make it home: automatic optimization of furniture arrangement, ACM Trans. Graph., 30, 86:1, 10.1145/2010324.1964981 Merrell, 2010, Computer-generated residential building layouts, ACM Trans. Graph., 29, 181:1, 10.1145/1882261.1866203 Yang, 2013, Urban pattern: layout design by hierarchical domain splitting, ACM Trans. Graph., 32, 181:1, 10.1145/2508363.2508405 Nieuwenhuis, 2006, On SAT modulo theories and optimization problems, 156 Sebastiani, 2015, Optimization Modulo Theories with Linear Rational Costs, ACM Trans. Comput. Log., 16, 10.1145/2699915 Miller, 1956, The magical number seven, plus or minus two: some limits on our capacity for processing information, Psychol. Rev., 63, 81, 10.1037/h0043158 Tibshirani, 1996, Regression shrinkage and selection via the lasso, J. R. Stat. Soc., Ser. E, 58, 267 Teso, 2017, Structured learning modulo theories, Artif. Intell., 244, 166, 10.1016/j.artint.2015.04.002 Campigotto, 2011, Active learning of combinatorial features for interactive optimization Barrett, 2009, Satisfiability modulo theories, 825 Sebastiani, 2007, Lazy satisfiability modulo theories, J. Satisf. Boolean Model. Comput., 3, 141 Barrett, 2009, Satisfiability modulo theories, 825 Nieuwenhuis, 2006, On SAT modulo theories and optimization problems, vol. 4121 Cimatti, 2010, Satisfiability modulo the theory of costs: foundations and applications, vol. 6015, 99 Cimatti, 2013, A modular approach to MaxSAT modulo theories, vol. 7962 Keeney, 1976 Joachims, 2002, Optimizing search engines using clickthrough data, 133 Friedman, 2004, Discussion of boosting papers, Ann. Stat., 32, 102 Zhu, 2004, 1-norm support vector machines, 49 Pu, 2008, User-involved preference elicitation for product search and recommender systems, AI Mag., 29, 93 Conitzer, 2009, Eliciting single-peaked preferences using comparison queries, J. Artif. Intell. Res., 35, 161, 10.1613/jair.2606 Baddeley, 1974, Working memory, Psychol. Learn. Motiv., 8, 47, 10.1016/S0079-7421(08)60452-1 2001, Behav. Brain Sci., 24, 87, 10.1017/S0140525X01003922 Braziunas, 2006 Pigozzi, 2016, Preferences in artificial intelligence, Ann. Math. Artif. Intell., 77, 361, 10.1007/s10472-015-9475-5 Savage, 1951, The theory of statistical decision, J. Am. Stat. Assoc., 46, 55, 10.1080/01621459.1951.10500768 Viappiani, 2007, Conversational recommenders with adaptive suggestions, 89 Viappiani, 2009, Regret-based optimal recommendation sets in conversational recommender systems, 101 Zhang, 2006, A comparative study of compound critique generation in conversational recommender systems, 234 Sabin, 1998, Product configuration frameworks-a survey, IEEE Intell. Syst. Appl., 13, 42, 10.1109/5254.708432 Felfernig, 2014 Boutilier, 1997, A constraint-based approach to preference elicitation and decision making, 19 Boutilier, 2004, Cp-nets: a tool for representing and reasoning with conditional ceteris paribus preference statements, J. Artif. Intell. Res., 21, 135, 10.1613/jair.1234 Allen, 2015, Cp-nets: from theory to practice, 555 Ricci, 2014, Recommender systems: models and techniques, 1511 Viappiani, 2011, Recommendation sets and choice queries: there is no exploration/exploitation tradeoff! Bollen, 2010, Understanding choice overload in recommender systems, 63 Settles, 2009 Settles, 2012, Active learning, 1, 10.1007/978-3-031-01560-1_1 Kremer, 2014, Active learning with support vector machines, Wiley Interdiscip. Rev. Data Min. Knowl. Discov., 4, 313, 10.1002/widm.1132 David, 1963 Bradley, 1952, Rank analysis of incomplete block designs: I. The method of paired comparisons, Biometrika, 39, 324 Luce, 2005 Plackett, 1975, The analysis of permutations, Appl. Stat., 24, 193, 10.2307/2346567 2008 Radlinski, 2007, Active exploration for learning rankings from clickthrough data, 570 Xu, 2007, Incorporating diversity and density in active learning for relevance feedback, vol. 4425, 246 Fan, 2008, LIBLINEAR: a library for large linear classification, J. Mach. Learn. Res., 9, 1871 Weng, 2011, A Bayesian approximation method for online ranking, J. Mach. Learn. Res., 12, 267 Tsukida, 2011 Mcfadden, 2001, Economic choices, Am. Econ. Rev., 91, 351, 10.1257/aer.91.3.351 Fränzle, 2007, Efficient solving of large non-linear arithmetic constraint systems with complex Boolean structure, JSAT, 1, 209 Jovanovic, 2012, Solving non-linear arithmetic, vol. 7364, 339 Larraz, 2014, Minimal-model-guided approaches to solving polynomial constraints and extensions, vol. 8561, 333 Chakrabarti, 2008, Structured learning for non-smooth ranking losses, 88 Dragone, 2018, Constructive preference elicitation, Frontiers Robotics AI, 4, 71, 10.3389/frobt.2017.00071 Yan, 2011, Active learning from crowds, 1161 Campigotto, 2010, Handling concept drift in preference learning for interactive decision making Paschos, 2014, Applications of Combinatorial Optimization, 10.1002/9781119005384 Weston, 2003, Use of the zero norm with linear models and kernel methods, J. Mach. Learn. Res., 3, 1439 Teso, 2016, Constructive preference elicitation by setwise max-margin learning, 2067 Teso, 2017, Coactive critiquing: elicitation of preferences and features, 2639 Dragone, 2018, Constructive preference elicitation over hybrid combinatorial spaces, 2943