Conditional Preference Networks Research Articles

Conditional Preference Networks in Multiobjective Evolutionary Algorithms

Conditional Preference Networks in Multiobjective Evolutionary Algorithms

Approximation Algorithms for Preference Aggregation Using CP-Nets

This paper studies the design and analysis of approximation algorithms for aggregating preferences over combinatorial domains, represented using Conditional Preference Networks (CP-nets). Its focus is on aggregating preferences over so-called swaps, for which optimal solutions in general are already known to be of exponential size. We first analyze a trivial 2-approximation algorithm that simply outputs the best of the given input preferences, and establish a structural condition under which the approximation ratio of this algorithm is improved to 4/3. We then propose a polynomial-time approximation algorithm whose outputs are provably no worse than those of the trivial algorithm, but often substantially better. A family of problem instances is presented for which our improved algorithm produces optimal solutions, while, for any ε, the trivial algorithm cannot attain a (2- ε)-approximation. These results may lead to the first polynomial-time approximation algorithm that solves the CP-net aggregation problem for swaps with an approximation ratio substantially better than 2.

Handling qualitative conditional preference queries in SPARQL: possibilistic logic approach

PurposeThe purpose of this paper is to address the need for new approaches in locating items that closely match user preference criteria due to the rise in data volume of knowledge bases resulting from Open Data initiatives. Specifically, the paper focuses on evaluating SPARQL qualitative preference queries over user preferences in SPARQL.Design/methodology/approachThe paper outlines a novel approach for handling SPARQL preference queries by representing preferences through symbolic weights using the possibilistic logic (PL) framework. This approach allows for the management of symbolic weights without relying on numerical values, using a partial ordering system instead. The paper compares this approach with numerous other approaches, including those based on skylines, fuzzy sets and conditional preference networks.FindingsThe paper highlights the advantages of the proposed approach, which enables the representation of preference criteria through symbolic weights and qualitative considerations. This approach offers a more intuitive way to convey preferences and manage rankings.Originality/valueThe paper demonstrates the usefulness and originality of the proposed SPARQL language in the PL framework. The approach extends SPARQL by incorporating symbolic weights and qualitative preferences.

How to model contrary-to-duty with GCP-nets

Preferences are ubiquitous in our everyday life. They are essential in the decision making process of individuals. Recently, they have also been employed to represent ethical principles, normative systems or guidelines. In this work we focus on a ceteris paribus semantics for deontic logic: a state of affairs where a larger set of respected prescriptions is preferable to a state of affairs where some are violated. Conditional preference networks (CP-nets) are a compact formalism to express and analyse ceteris paribus preferences, with some desirable computational properties. In this paper, we show how deontic concepts (such as contrary-to-duty obligations) can be modeled with generalized CP-nets (GCP-nets) and how to capture the distinction between strong and weak permission in this formalism. To do that, we leverage on an existing restricted deontic logic that will be mapped into conditional preference nets.

Reputation Bootstrapping for Composite Services Using CP-Nets

We propose a novel framework to bootstrap the reputation of on-demand service compositions. On-demand compositions are usually context-aware and have little or no direct consumer feedback. The reputation bootstrapping of single or atomic services do not consider the topology of the composition and relationships among reputation-related factors. We apply Conditional Preference Networks (CP-nets) of reputation-related factors for each of component services in a composition. The reputation of a composite service is bootstrapped by the composition of CP-nets. We consider the history of invocation among component services to determine reputation-interdependence in a composition. The composition rules are constructed using the composition topology and four types of reputation-influence among component services. A heuristic-based Q-learning approach is proposed to select the optimal set of reputation-related CP-nets. Experimental results prove the efficiency of the proposed approach.

Modelling Ceteris Paribus Preferences with Deontic Logic

Abstract We present a formal semantics for deontic logic based on the concept of ceteris paribus preferences. We introduce notions of unconditional obligation and permission as well as conditional obligation and permission that are interpreted relative to this semantics. We show that these notions satisfy some intuitive properties and, at the same time, do not encounter some problems and paradoxes that have been extensively discussed in the deontic logic literature. We prove that the satisfiability problem for our logic is in NP. Finally, we show that the fragment of our logic in which the content of a deontic operator is a literal has an equivalent representation based on conditional preference networks (CP-nets).

Complexity results for preference aggregation over (m)CP-nets: Max and rank voting

Aggregating preferences over combinatorial domains has a plethora of applications in AI. Due to the exponential nature of combinatorial preferences, compact representations are needed, and conditional ceteris paribus preference networks (CP-nets) are among the most studied compact representation formalisms. Unlike the problem of outcome dominance over individual CP-nets, which received an extensive complexity analysis in the literature, mCP-nets (and global voting/preference aggregation over CP-nets) lacked such a thorough complexity characterization, despite this being reported multiple times in the literature as an open problem. An initial complexity analysis for mCP-nets was carried out only recently, where Pareto and majority dominance semantics were studied. In this paper, we further explore the complexity of mCP-nets, giving a precise complexity analysis of the dominance semantics in mCP-nets when the max and rank voting schemes are considered. In particular, we show that deciding dominance under max voting is Θ2P-complete, while deciding optimal outcomes and their existence under max voting is complete for Π2P and Σ3P, respectively. We also show that, under max voting, deciding optimum outcomes is Π2P-complete, and deciding their existence is Π2P-hard and in Σ3P. As for rank voting, apart from deciding whether mCP-nets have rank optimal outcomes, which is a trivial problem, as all mCP-nets have rank optimal outcomes, all the other rank voting tasks considered are tractable and in P. Interestingly, we show here that these problems are not only in P, but also P-hard (and hence P-complete). Furthermore, we show that deciding whether mCP-nets have Pareto optimum outcomes, which was known to be feasible in polynomial time, is actually P-complete, as well as that various tasks for CP-nets are P-complete. These results provide interesting insights, as P-complete problems are (currently believed to be) inherently sequential, and hence they cannot benefit from highly parallel computations.

Sequential Learning-based IaaS Composition

We propose a novel Infrastructure-as-a-Service composition framework that selects an optimal set of consumer requests according to the provider’s qualitative preferences on long-term service provisions. Decision variables are included in the temporal conditional preference networks to represent qualitative preferences for both short-term and long-term consumers. The global preference ranking of a set of requests is computed using a k -d tree indexing-based temporal similarity measure approach. We propose an extended three-dimensional Q-learning approach to maximize the global preference ranking. We design the on-policy-based sequential selection learning approach that applies the length of request to accept or reject requests in a composition. The proposed on-policy-based learning method reuses historical experiences or policies of sequential optimization using an agglomerative clustering approach. Experimental results prove the feasibility of the proposed framework.

Conditional Preference Networks for Cloud Service Selection and Ranking With Many Irrelevant Attributes

Cloud service selection and ranking are two different but related important tasks in the realm of cloud computing. Given the rapid growth in the number of available services offered by cloud providers with different quality of service, these tasks are becoming more and more challenging to be managed by the end customers. Furthermore, many services in the cloud are defined over a large set of attributes where the potential customer is interested on only a small subset of them. The other attributes are viewed as irrelevant to the customer selection and ranking strategies. In this work, we show how to employ conditional preferential dependencies in the domain of cloud service selection and ranking. In particular, we use Conditional Preference Networks (CP-nets) as a compact model to represent and reason with customer’s preferences and criteria interdependencies. We show how to select the best service and how to rank, in non-increasing order, a set of services given possibly incomplete information in the customer’s CP-net. Our experimental results prove the feasibility of CP-nets as a mechanism for selecting and ranking hundreds of services defined on hundreds of attributes with complex interdependencies.

Conditional Preference Networks with User's Genuine Decisions

AbstractUser's choices involve habitual behavior and genuine decision. Habitual behavior is often expressed using preferences. In a multiattribute case, the Conditional Preference Network (CP‐net) is a graphical model to represent user's conditional ceteris paribus (all else being equal) preference statements. Indeed, the CP‐net induces a strict partial order over the outcomes. By contrast, we argue that genuine decisions are environmentally influenced and introduce the notion of “comfort” to represent this type of choices. In this article, we propose an extension of the CP‐net model that we call the CP‐net with Comfort (CPC‐net) to represent a user's comfort with preferences. Given that preference and comfort might be two conflicting objectives, we define the Pareto optimality of outcomes when achieving outcome optimization with respect to a given CPC‐net. Then, we propose a backtrack search algorithm to find the Pareto optimal outcomes. On the other hand, two outcomes can stand in one of six possible relations with respect to a CPC‐net. The exact relation can be obtained by performing dominance testing in the corresponding CP‐net and comparing the numeric comforts.

The complexity of exact learning of acyclic conditional preference networks from swap examples

Learning of user preferences, as represented by, for example, Conditional Preference Networks (CP-nets), has become a core issue in AI research. Recent studies investigate learning of CP-nets from randomly chosen examples or from membership and equivalence queries. To assess the optimality of learning algorithms as well as to better understand the combinatorial structure of classes of CP-nets, it is helpful to calculate certain learning-theoretic information complexity parameters. This article focuses on the frequently studied case of exact learning from so-called swap examples, which express preferences among objects that differ in only one attribute. It presents bounds on or exact values of some well-studied information complexity parameters, namely the VC dimension, the teaching dimension, and the recursive teaching dimension, for classes of acyclic CP-nets. We further provide algorithms that exactly learn tree-structured and general acyclic CP-nets from membership queries. Using our results on complexity parameters, we prove that our algorithms, as well as another query learning algorithm for acyclic CP-nets presented in the literature, are near-optimal.

Rank Pruning for Dominance Queries in CP-Nets


 
 
 Conditional preference networks (CP-nets) are a graphical representation of a person’s (conditional) preferences over a set of discrete features. In this paper, we introduce a novel method of quantifying preference for any given outcome based on a CP-net representation of a user’s preferences. We demonstrate that these values are useful for reasoning about user preferences. In particular, they allow us to order (any subset of) the possible outcomes in accordance with the user’s preferences. Further, these values can be used to improve the efficiency of outcome dominance testing. That is, given a pair of outcomes, we can determine which the user prefers more efficiently. Through experimental results, we show that this method is more effective than existing techniques for improving dominance testing efficiency. We show that the above results also hold for CP-nets that express indifference between variable values.
 
 

CP-Nets Structure Learning Based on mRMCR Principle

As a graphical model, Conditional Preference Networks (CP-nets) are used to describe the qualitative conditional preferences of attributes, and the structure learning plays an important role in the research of CP-nets. Different from traditional CP-nets structure learning methods, a Maximum Relevance Minimum Common Redundancy (mRMCR) algorithm based on the information theory and feature selection is proposed and discussed detailedly. Firstly, a mutual information solution formula on the preference database is established, it regards mutual information as mutual relation between one attribute and its feasible father set, which also avoids the calculation of conditional mutual information. Secondly, in order to make our graphical model include relevant, exclude irrelevant and control the use of redundant features, a formula for calculating mRMCR is designed. The mRMCR algorithm can measure the dependent relationship effectively and determine the causal relationship between variables, and can get the structure of CP-nets. Finally, the effectiveness of the algorithm is verified on the movie recommendation datasets. The experimental results show that the proposed mRMCR algorithm can not only obtain the causal relationship between variables quickly and effectively but also extract the feasible father set of each attribute and then obtain the topological structure of CP-nets.

Extending Conditional Preference Networks to Handle Changes

Conditional Preference Networks (CP-nets) are a compact and natural model to represent conditional qualitative preferences. In CP-nets, the set of variables is fixed in advance. That is, the set of alternatives available during the decision process are always the same no matter how long the process is. In many configuration and interactive problems, it is expected that some variables are subject to be included or excluded during the configuration process due to users showing interest or boredom on some aspects of the problem. Representing and reasoning with such changes is important to the success of the application and therefore, it is important to have a model capable of dynamically including or excluding variables. In this work, we introduce active CP-nets (aCP-nets) as an extension of CP-nets where variable participation is governed by a set of activation requirements. In particular, we introduce an activation status to the CP-net variables and analyze two possible semantics of aCP-nets along with their consistency requirements.

Personalized service selection using Conditional Preference Networks

Top-k and skyline techniques have been used to address preference based queries for effective service selection. However, they do not consider the dependencies between attributes in user preferences. In this paper, we focus on developing top-k indexing methods based on Conditional Preference Networks. We first determine whether the correlation among service attributes is clear and definite. After that, we employ dimensionality reduction to reduce the dimensionality of the service space. We then use top-k query to further improve the scalability. We conduct extensive experiment and compare with other competitive indexing mechanism to demonstrate the effectiveness of the proposed approach.

Cutting Cycles of Conditional Preference Networks with Feedback Set Approach.

As a tool of qualitative representation, conditional preference network (CP-net) has recently become a hot research topic in the field of artificial intelligence. The semantics of CP-nets does not restrict the generation of cycles, but the existence of the cycles would affect the property of CP-nets such as satisfaction and consistency. This paper attempts to use the feedback set problem theory including feedback vertex set (FVS) and feedback arc set (FAS) to cut cycles in CP-nets. Because of great time complexity of the problem in general, this paper defines a class of the parent vertices in a ring CP-nets firstly and then gives corresponding algorithm, respectively, based on FVS and FAS. Finally, the experiment shows that the running time and the expressive ability of the two methods are compared.

On the Use of Linear Combination in PWCP-Nets

Conditional preference networks (CP-nets) are a compact but powerful formalism to represent and reason with qualitative preferences using the notion of conditional preferential independence. However, they suffer from incomparabilities between possible outcomes. Several works have attempted to overcome this weakness by quantifying CP-nets. This paper proposes a new approach combining two of the most interesting extensions of CP-nets, namely Probabilistic CP-nets (PCP-nets) using probability distribution to model uncertainty in different preference statements and Weighted CP-nets (WCP-nets) adding weights to express the relative importance of some attribute values regarding others. The new model so-called PWCP-nets combines the two models by handling both uncertainty and weights. Experimental results show the efficiency of this rich extension of CP-nets compared to PCP-nets and WCP-nets.

Structure Learning of Conditional Preference Networks Based on Dependent Degree of Attributes From Preference Database

Conditional preference networks (CP-nets) are used as intuitive graphical tools to represent conditional preference statements regarding the values of a set of attributes. Making use of CP-nets to solve certain learning problems has attracted growing attention from scholars and become a hot topic in the field of artificial intelligence. Therefore, many methods have been proposed to solve these learning problems. However, these approaches suffer from two main disadvantages: the amount of time required and their lack in concrete structures. To overcome these limitations, in this paper, we first provide theoretical support for the use of a conditional independent test for learning the structure of CP-nets. Second, we propose the dependent degree to calculate the dependency relationship among attributes. Finally, we present an algorithm to obtain the structures of CP-nets. Beyond that, a number of database samples have been reduced by filtering out insignificant or noise data, and a concrete structure of learned CP-nets with 18 attributes is given. The experiments show that our approach can obtain a better structure of CP-nets without materially increasing the time required for the process and put forward contrast to methods presented antecedently.

Learning CP-Nets Structure From Preference Data Streams

With the sharp increase of digital data emerging at present, the data in new applications are generated fast. Continuous cumulative data have gradually become massive and difficult to be handled due to limited workspace and limited amount of time. The conventional learning conditional preference networks’ algorithm cannot successfully process the data streams. In this paper, we introduce the model of learning CP-nets from preference data streams and formalize the question. Then, an incremental approach is presented through which we can learn the CP-nets with gradually increasing data streams. The proposed method is verified on simulated data and real data, and it is also compared with other works.

Query-based learning of acyclic conditional preference networks from contradictory preferences

Conditional preference networks (CP-nets) provide a compact and intuitive graphical tool to represent the preferences of a user. However, learning such a structure is known to be a difficult problem due to its combinatorial nature. We propose, in this paper, a new, efficient, and robust query-based learning algorithm for acyclic CP-nets. In particular, our algorithm takes into account the contradictions between multiple users’ preferences by searching in a principled way the variables that affect the preferences. We provide complexity results of the algorithm, and demonstrate its efficiency through an empirical evaluation on synthetic and on real databases.