Integration Of QPNs

Probabilistic network is the important approach to represent and deal with prob-abilistic knowledge in artificial intelligence. The integration of multiple-source prob-abilistic networks is crucial to research comprehensively on representation and rea-soning of probabilistic knowledge. However, in many previous researches, these inte-gration methods are proposed mostly for quantitative probabilistic networks, such asBayesian networks, influence diagrams and possibilistic networks, but less for takinginto account to integrate multiple Qualitative Probabilistic Networks (QPNs) by whichprobabilistic knowledge only can be represented or be needed to represent qualitatively.In this paper, the sign integration method and three structure integration meth-ods are proposed by combining domain expert knowledge and incomplete data. Mainresults are as follows.1. Ambiguity reduction method is proposed based on qualitative mutual information.The definition of qualitative mutual information is first given. Using the definitionan enhanced QPN is proposed. Specifically, symmetry, transitivity, parallel com-position of qualitative influences, geometric properties in the enhanced QPN areanalyzed. Furthermore, ambiguity reduction method in polynomial time for QPNinference is proposed.2. The sign integration method named QPNSI algorithm is designed and implementedbased on qualitative mutual information. The above ambiguity reduction method isfurther extended to the integration of multiple QPNs that have the same structures.QPNSI algorithm is proposed and its time complexity is analyzed.3. The structure integration method named SNQPNI algorithm is designed and im-plemented based on rough set theory. Probabilistic positive region is adopted tocompute attribute dependency degree that regarded as the strength of qualitative in-fluence. SNQPNI integration algorithm of multiple QPNs that have the same nodesis proposed by addressing some key problems and its time complexity is analyzed. 4. The structure integration method named TQPNI algorithm is designed and imple-mented based on rough set theory. The definition of temporal QPN (TQPN) is firstgiven, and then TQPNI algorithm of multiple TQPNs in time serial environment isproposed by removing self loop. The time complexity is analyzed.5. The structure integration method named DNQPNI algorithm is designed and im-plemented based on rough set theory. The initial union of QPNs is obtained basedon the idea of SNQPNI algorithm. Furthermore, the missing edges are added to itaccording to attribute dependency degree and the redundant edges are removed byattribute relative necessity and relative reduction. DNQPNI integration algorithm ofmultiple QPNs that have the diferent nodes is proposed and its time complexity isanalyzed.