Preference evaluation techniques of preference queries in database

Preference queries are considered as a major necessity tool in today’s database management system (DBMS). Adopting preference queries in the database application systems enable users to determine more than one objective in the submitted query which result into more accurate results compared to the traditional queries. Preference queries prefer one data item (tuple) p over the other data item (tuple) q if and only if p is better than q in all dimensions (attributes) and not worse than q in at least one dimension (attribute). Several preference evaluation techniques for preference queries have been proposed which aimed at finding the “best” results that meet the user preferences. These include but not limited to top-k, skyline, ranked skylines, k-representative dominance, k-dominance,top-k dominating, and k-frequency. This paper attempts to survey and analyze the following preference evaluation techniques of query processing in database systems: top-k, skyline, top-k dominating, k-dominance, and k-frequency by highlighting the strengths and the weaknesses of each technique

A Possibilistic Logic Approach to Conditional Preference Queries

International audienceThe paper presents a new approach to deal with database preference queries, where preferences are represented in the style of possibilistic logic, using symbolic weights. The symbolic weights may be processed without the need of a numerical assignment of priority. Still, it is possible to introduce a partial ordering among the symbolic weights if necessary. On this basis, four methods that have an increasing discriminating power for ranking the answers to conjunctive queries, are proposed. The approach is compared to different lines of research in preference queries including skyline-based methods and fuzzy set-based queries. With the four proposed ranking methods the first group of best answers is made of non dominated items. The purely qualitative nature of the approach avoids the commensurability requirement of elementary evaluations underlying the fuzzy logic methods

Learning Reward Machines through Preference Queries over Sequences

Reward machines have shown great promise at capturing non-Markovian reward functions for learning tasks that involve complex action sequencing. However, no algorithm currently exists for learning reward machines with realistic weak feedback in the form of preferences. We contribute REMAP, a novel algorithm for learning reward machines from preferences, with correctness and termination guarantees. REMAP introduces preference queries in place of membership queries in the L* algorithm, and leverages a symbolic observation table along with unification and constraint solving to narrow the hypothesis reward machine search space. In addition to the proofs of correctness and termination for REMAP, we present empirical evidence measuring correctness: how frequently the resulting reward machine is isomorphic under a consistent yet inexact teacher, and the regret between the ground truth and learned reward machines.Comment: 24 pages, 10 figure

Learning Formal Specifications from Membership and Preference Queries

Active learning is a well-studied approach to learning formal specifications, such as automata. In this work, we extend active specification learning by proposing a novel framework that strategically requests a combination of membership labels and pair-wise preferences, a popular alternative to membership labels. The combination of pair-wise preferences and membership labels allows for a more flexible approach to active specification learning, which previously relied on membership labels only. We instantiate our framework in two different domains, demonstrating the generality of our approach. Our results suggest that learning from both modalities allows us to robustly and conveniently identify specifications via membership and preferences.Comment: 6 pages, Presented at ICML 2023 Workshop on The Many Facets of Preference-Based Learnin

Noisy Submodular Maximization via Adaptive Sampling with Applications to Crowdsourced Image Collection Summarization

We address the problem of maximizing an unknown submodular function that can only be accessed via noisy evaluations. Our work is motivated by the task of summarizing content, e.g., image collections, by leveraging users' feedback in form of clicks or ratings. For summarization tasks with the goal of maximizing coverage and diversity, submodular set functions are a natural choice. When the underlying submodular function is unknown, users' feedback can provide noisy evaluations of the function that we seek to maximize. We provide a generic algorithm -- \submM{} -- for maximizing an unknown submodular function under cardinality constraints. This algorithm makes use of a novel exploration module -- \blbox{} -- that proposes good elements based on adaptively sampling noisy function evaluations. \blbox{} is able to accommodate different kinds of observation models such as value queries and pairwise comparisons. We provide PAC-style guarantees on the quality and sampling cost of the solution obtained by \submM{}. We demonstrate the effectiveness of our approach in an interactive, crowdsourced image collection summarization application.Comment: Extended version of AAAI'16 pape