Skip to main content
SpringerLink
Log in

Qualitative probabilistic network-based fusion of time-series uncertain knowledge

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

In time-series environments, uncertain knowledge among variables in a time slice can be represented and modeled by a Bayesian network (BN). In this paper, we are to achieve the global uncertain knowledge during a period of time for decision-making or action selection by fussing or combining the participating uncertainties of multiple time slices consistently while satisfying the demands of high efficiency and instantaneousness. We adopt qualitative probabilistic network (QPN), the qualitative abstraction of BN, as the underlying framework of modeling and fusing time-series uncertain knowledge. The BNs in continuous time slices constitute time-series BNs, from which we derive time-series QPNs. Taking time-series BNs as input, we propose a QPN-based approach to fuse time-series uncertainties in line with temporal specialties. First, for each time slice, we enhance the implied QPN by augmenting interval-valued weights derived from the corresponding BN, and then obtain the QPN with weighted influences, denoted EQPN (Enhanced Qualitative Probabilistic Network), which provides a quantitative and conflict-free basis for fusing uncertain knowledge. Then, we give the method for fusing the graphical structures of time-series EQPNs based on the concept of Markov equivalence. Following, we give a superposition method for fusing qualitative influences of time-series EQPNs. Experimental results show that our method is not only efficient, but also effective. Meanwhile, the simulation results when applying time-series EQPNs and the fusion algorithm to a robotic system show that our method is applicable in realistic intelligent situations.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21

Similar content being viewed by others

Notes

  1. An undirected graph is chordal if every cycle of length \(n\;=\;4\) possesses a chord (Andersson et al. 1997; Pearl 1988).

  2. A component is the connected subgraph generated by removing the orientations in a DAG (Pearl 1988).

References

  • Andersson S, Madigan D, Perlman M (1997) A characterization of Markov equivalence classes for acyclic digraphs. Ann Stat 25:505–541

    Article  MATH  MathSciNet  Google Scholar 

  • Bolt J, Van der Gaag L, Renooij S (2005) Introducing situational signs in qualitative probabilistic networks. Int J Approx Reason 38(3):333–354

    Article  MATH  Google Scholar 

  • Buntine W (1996) A guide to the literature on learning probabilistic networks from data. IEEE Trans Knowl Data Eng 8(2):195–210

    Article  Google Scholar 

  • Cheng J (1998) PowerConstructor system. http://www.cs.ualberta.ca/~jcheng/bnpc.htm

  • Cheng J, Bell D, Liu W (1997) Learning Bayesian networks from data: an efficient approach based on information theory. In: Proceeding of conference CIKM, pp 325–331

  • Chickering D (1995) A transformational haracterization of Bayesian network structures. In: Proceeding of conference UAI, pp 87–98

  • Chickering D (2002) Learning equivalence classes of Bayesian-network structures. J Mach Learn Res 2:445–498

    MATH  MathSciNet  Google Scholar 

  • Cooper G (1990) The computational complexity of probabilistic inference using Bayesian belief networks. Artif Intell 42(2–3):393–405

    Article  MATH  Google Scholar 

  • de Campos C, Cozman F (2005) Belief updating and learning in semi-qualitative probabilistic networks. In: Proceeding of conference UAI, pp 153–160

  • de Campos C, Cozman F (2013) Complexity of Inferences in polytree-shaped semi-qualitative probabilistic networks. In: Proceeding of conference AAAI, pp 217–223

  • Dor D, Tarsi M (1992) A simple algorithm to construct a consistent extension of a partially oriented graph. Technical Report R-185, Cognitive Systems Laboratory, UCLA Computer Science Department

  • Druzdzel M (1993) Probabilistic reasoning in decision support systems: from computation to common sense. Ph.D. thesis, Department of Engineering and Public Policy, Carnegie Mellon University, Pennsylvania

  • Druzdzel M, Henrion M (1993) Efficient reasoning in qualitative probabilistic networks. In: Proceeding of national conference artificial intelligence, pp 548–553

  • Geiger D, Heckerman D (1996) Knowledge representation and inference in similarity networks and Bayesian multinets. Artif Intell 82:45–74

    Article  MathSciNet  Google Scholar 

  • Guo H, Hsu W (2002) A survey of algorithms for real-time Bayesian network inference. In: Proceeding of the joint AAAI-02/KDD-02/UAI-02 workshop on real-time decision support and diagnosis systems

  • Hwang K, Park H, Cho S (2009) Robotic intelligence with behavior selection network for Bayesian network ensemble. In: Proceeding of RIISS workshop, pp 151–154

  • Ibrahim Z, Ngom A, Tawfik A (2011) Using qualitative probability in reverse-engineering gene regulatory networks. IEEE/ACM Trans Comput Biol Bioinform 8(12):326–334

    Article  Google Scholar 

  • Lazkano E, Sierra B, Astigarraga A, Martinez-Otzeta J (2007) On the use of Bayesian networks to develop behaviours for mobile robots. Robot Auton Syst 55(3):253–265

    Article  Google Scholar 

  • Liu W, Guo L, Song N (2001) Fuzzy association degree with delayed time in temporal data model. J Comput Sci Technol 16(1):86–91

    Article  MATH  Google Scholar 

  • Liu W, Yue K, Liu S, Sun Y (2008) Qualitative-probabilistic-network-based modeling of temporal causalities and its application to feedback loop identification. Inform Sci 178(7):1803–1824

    Article  MATH  Google Scholar 

  • Matzkvich I, Abramson B (1992) The topological fusion of Bayes nets. In: Proceeding of conference UAI, pp 191–198

  • Maynard-Reid II P, Chajewska U (2001) Aggregating learned probabilistic beliefs. In: Proceeding of conference UAI, pp 354–361

  • Meek C (1995) Causal inference and causal explanation with background knowledge. In: Proceeding of conference UAI, pp 403–410

  • Nielsen S, Parsons S (2007) An application of formal argumentation: Fusing Bayesian networks in multi-agent systems. Artif Intell 171:754–775

    Article  MATH  MathSciNet  Google Scholar 

  • Norsys Software Corp (2007) Netica 3.17 Bayesian network software from Norsys. http://www.norsys.com

  • Pearl J (1988) Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann, San, Mateo

  • Pennock D, Wellman M (1999) Graphical representations of consensus belief. In: Proceeding of conference UAI, pp 531–540

  • Purwanto C, Rajasvaran L (2012) An enhanced hybrid method for time series prediction using linear and neural network models. Appl Intell 37(4):511–519

    Article  Google Scholar 

  • Renooij S, Van der Gaag LC (2008) Enhanced qualitative probabilistic networks for Resolving trade-offs. Artif Intell 172(12–13):1470–1494

    Article  MATH  Google Scholar 

  • Renooij S, Parsons S, Pardieck P (2003) Using kappas as indicators of strength in qualitative probabilistic networks. In: Proceeding of conference symbolic and qualitative approaches to reasoning with uncertainty, LNCS 2711, pp 87–99

  • Renooij S, Van der Gaag LC (2002) From qualitative to quantitative probabilistic networks. In: Proceeding of conference UAI, pp 422–429

  • Richardson M, Domingos P (2003) Learning with knowledge from multiple experts. In: Proceeding of conference ICML, pp 624–631

  • Russell S, Norvig P (2002) Artificial intelligence—a modern approach. Pearson Education, Publishing as Prentice-Hall, Boston

    Google Scholar 

  • Sagrado J, Moral S (2003) Qualitative combination of Bayesian networks. Int J Intell Syst 18:237–249

    Article  MATH  Google Scholar 

  • Shafer G (1986) The combination of evidence. Int J Intell Syst 1:155–179

    Article  MATH  MathSciNet  Google Scholar 

  • Tahboub K (2006) Intelligent human-machine interaction based on dynamic Bayesian networks probabilistic intention recognition. J Intell Robot Syst 45:31–52

    Article  Google Scholar 

  • Verma T, Pearl J (1990) Equivalence and synthesis of causal models. In: Proceeding of conference UAI, pp 220–227

  • Wellman M (1990a) Fundamental concepts of qualitative probabilistic networks. Artif Intell 44:257–303

    Article  MATH  MathSciNet  Google Scholar 

  • Wellman M (1990b) Graphical inference in qualitative probabilistic networks. Networks 20:687–701

    Article  MATH  MathSciNet  Google Scholar 

  • Yue K, Liu W (2009) Qualitative representation, inference and their application of uncertain knowledge: a survey on qualitative probabilistic networks. J Yunnan Univ (Nat Sci Ed) 31(6):560–570

    MathSciNet  Google Scholar 

  • Yue K, Yao Y, Li J, Liu W (2010) Qualitative probabilistic network with reduced ambiguities. Appl Intell 33(2):159–178

    Article  Google Scholar 

  • Yue K, Liu W, Yue M (2011) Quantifying influences in the qualitative probabilistic network with interval probability parameters. Appl Soft Comput 11(1):1135–1143

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgments

This work was supported by the National Natural Science Foundation of China (Nos. 61163003, 61232002, 71161015), the National Basic Research (973) Program of China (No. 2010CB328106), the Yunnan Provincial Foundation for Leaders of Disciplines in Science and Technology (No. 2012HB004), the Natural Science Foundation of Yunnan Province (No. 2013FB010), and the Research Foundation of Key Laboratory of Software Engineering of Yunnan Province (No. 2012SE013).

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming, China

    Kun Yue, Wenhua Qian & Weiyi Liu

  2. Key Laboratory of Software Engineering of Yunnan Province, Kunming, China

    Kun Yue & Jin Li

  3. Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China

    Xiaodong Fu

Authors
  1. Kun Yue
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wenhua Qian
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiaodong Fu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Weiyi Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kun Yue.

Additional information

Communicated by V. Loia.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yue, K., Qian, W., Fu, X. et al. Qualitative probabilistic network-based fusion of time-series uncertain knowledge. Soft Comput 19, 1953–1972 (2015). https://doi.org/10.1007/s00500-014-1381-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-014-1381-y

Keywords

Search

Navigation