Research On Structure Learning Methods Of Acyclic CP-nets Based On Preference Database

Modeling,learning and reasoning of preference are the research fields involved in multidisciplinary research in computer science.They have become a basic theme in artificial intelligence.Moreover,they have considerable academic value and practical value which can be used in recommendation systems,social networks,national security,design and marketing of product,personalized customization,etc.One of the main graphical models used to represent preferences is Conditional Preference Networks(CP-nets),which can succinctly represent qualitative preferences among attributes.CP-nets have been thoroughly researched and applied to a variety of issues involving preference processing which including representation,reasoning,aggregation,and learning.Among of them,the learning problems of the CP-nets have attracted more attention and have achieved a series of results,such as learning from inconsistent data,learning with noise data,and approximate learning of optimal results.However,the theory of CP-nets learning has not been perfected.In particular,the learning problem such as large-scale attributes,dynamic data and the multi-agents preference need to be further studied.In view of the problems existing in the previous research work,this thesis starts from the basic methods of CP-nets learning,and studies the structural learning of CP-nets,mainly carries out the following research work.Aiming at the key problems of data form,determination of parent set of attribute and acyclic structure learning of CP-nets,this thesis studies the basic structure learning methods of CP-nets.Firstly,the design of preference database is introduced,and the common data set can be transformed into the form of preference database.Secondly,the judgment and support degree of the conditional attribute parent set solution is given based on conditional preference,and the scoring function is designed based on the minimum description length.Finally,the structure learning of CP-nets is provided based on the above mentioned scoring function,including the structure learning algorithms,acyclic algorithms,conditional preference table algorithms,and algorithms analysis.The experimental results on the simulated data and the real data set demonstrate the higher performance of the proposed method.Aiming at the large number of attributes in the CP-nets,the number of structure changes increases exponentially,and the structure of CP-nets cannot be learned by traversing method.The A*algorithm is used to learn the structure of CP-nets and approximate its optimal structure.In this thesis,the structure learning problem of CP-nets is considered as the problem of finding the shortest path by using the scoring function for a given data set.The acyclic object is added by constraining and improving the heuristic function.The solution space of the learning problem is represented by the state space search graph.The shortest path between the starting node and the target node is taken as a reference,and the acyclic CP-nets structure can be obtained directly.In this thesis,the time of structure learning of CP-nets is reduced to polynomial time,and the experimental results show the proposed method can shorten the calculation time with affecting the similarity and agreement obviously.In view of the fact that user preference usually varies with time and preference data exists in the form of data stream,the thesis changes the existing structure learning of CP-nets which focuses on static scenes,and designs a stream preference database model based on sliding window,then gives the incremental CP-nets structure learning method based on the preference data stream to solve the learning problem of streaming preference data.The experimental results on the simulated data and the real data set show that the incremental method learning CP-nets is effective for data stream.In order to learn preference of multi-agents,this thesis proposes the learning framework based on probabilistic CP-nets(PCP-nets).The thesis takes advantage of Max aggregation method to learn PCP-nets and express preference of multiple agents upon learning of a single agent's preference.The experimental results on the simulated data and the real data set show that the aggregation method of learning can obtain effective CP-nets structure,and has high similarity and compatibility.It is a useful exploration for multi-agents aggregation learning.In addition,the research idea of parallelized CP-nets for multi-agent is proposed,which is of great significance for distributed computing.In summary,this thesis has obtained relevant research results for the structural learning problems of CP-nets in basic methods and frameworks,structure learning of large-scale attributes,structure learning of data stream,and structure learning of multiple CP-nets.The above mentioned methods are verified in different data.Finally,the thesis explores the future research directions of structure learning of CP-nets,including methods and techniques such as attribute reduction,deep learning and bayesian network migration learning.