Total views : 97

Recent Advances in Markov Logic Networks

Affiliations

  • Faculty of Science, New Valley - Assiut University, Egypt
  • Computer and Information Technology College, Northern Border University, Saudi Arabia

Abstract


Objectives: To identify recent progress and areas of application for one technique in soft computing, specifically. This technique is known as Markov Logic Networks. Methods/Statistical Analysis: Soft computing combines machine learning and fuzzy logic in order to tackle problems that appear to have no definite solution. In doing so, soft computing approaches a human style of thought, and lends itself well to data-rich, heterogeneous and fast-changing scenarios. The success of soft computing has only fueled to drive for better, more powerful, and faster algorithms. Findings: Soft computing has already revolutionized a number of fields, including artificial intelligence, robotics, voice recognition, and areas of biomedicine. It has the potential to continue doing so, but this future success depends heavily on making more ambitious soft-computing algorithms tractable and scalable to Big Data - sized problems. One promising technique that has come to the forefront of soft computing research in recent years is the heavily probabilistic-reasoning-based Markov Logic Network (MLNs). MLNs combine the efficiency of the Markov Model with the power of first-order logical reasoning. MLNs have already proven themselves adept at such futuristic implementation as smart homes, voice recognition, situations awareness, prediction of marine phenomena, and weather assessment. In order to make MLNs more tractable, research has recently turned towards normalizing progressively by time-slice to assure convergence, and "lifting" structural motifs from similar, already-computed networks. Progressive efforts in these areas should deliver a next-generation of situation awareness in "smart" electronics and predictive tools, one more step towards true artificial intelligence. Application/Improvements: Soft computing has already revolutionized a number of fields, including artificial intelligence, robotics, voice recognition, and areas of biomedicine. It has the potential to continue doing so.

Keywords

Evolutionary Algorithms, Fuzzy Logic, Machine Learning, Markov Logic, Soft Computing.

Full Text:

 |  (PDF views: 35)

References


  • Bonissone PP. Soft computing: the convergence of emerging reasoning technologies. Soft computing. 1997; 1(1):6-18. Crossref
  • Zadeh LA. Fuzzy logic, neural networks, and soft computing. Communications of the ACM. 1994; 37(3):77-84. Crossref
  • Zadeh LA. Discussion: Probability theory and fuzzy logic are complementary rather than competitive. Technometrics. 1995; 37(3):271-76. Crossref
  • Yao Y. Perspectives of granular computing. 2005: IEEE International Conference on Granular Computing. IEEE. 2005; 1: 85-90. Crossref
  • Park HJ, Kim BK, Lim KY. Measuring the machine intelligence quotient (MIQ) of human-machine cooperative systems. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans. 2001; 31(2):89-96.
  • Zadeh LA. BISC: The Berkeley Initiative in Soft Computing. 2009; 44:693.
  • Klir G, Yuan B. Fuzzy sets and fuzzy logic. New Jersey: Prentice Hall. 1995; 4:1-88.
  • Ganter V, Strube M. Finding hedges by chasing weasels: Hedge detection using Wikipedia tags and shallow linguistic features. Proceedings of the ACL-IJCNLP 2009 Conference Short Papers. Association for Computational Linguistics. 2009 August; p. 173-76. Crossref
  • Zadeh LA. Is there a need for fuzzy logic? Information sciences. 2008; 178(13); 2751-79. Crossref
  • Alpaydin E. Introduction to machine learning. MIT press. 2014; p. 1-579.
  • Markoff J. Google Lobbies Nevada to Allow Self-Driving Cars. The New York Times. 2011; P. 10.
  • Anusuya MA, Katti SK. 2010: Speech recognition by machine, a review. arXiv preprint arXiv. 2010; 6(3):1-25.
  • Fiser J, Berkes P, Orbán G, Lengyel M. Statistically optimal perception and learning: from behavior to neural representations, Trends in cognitive sciences. 2010; 14(3):119-30. Crossref PMid:20153683 PMCid:PMC2939867
  • Modayil J, White A, Sutton RS. Multi-timescale nexting in a reinforcement learning robot, Adaptive Behavior. 2014; 22(2):146-60. Crossref
  • Pearl J. Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann. 2014; p. 1-2.
  • Domingos P, Web WA. A Tractable First-Order Probabilistic Logic. In AAAI. 2012; p. 1-8.
  • Robertson R, Combs A. Chaos theory in psychology and the life sciences. Psychology Press. 2014; p. 416.
  • Zelinka I, Celikovsky S, Richter H, Chen G. Evolutionary algorithms and chaotic systems. Springer Science and Business Media. 2010; 267:560
  • Richardson M, Domingos P. Markov logic networks. Machine learning. 2006; 62(1-2):107-36. Crossref
  • Gayathri KS, Elias S, Ravindran B. Hierarchical activity recognition for dementia care using markov logic network, Personal and Ubiquitous Computing. 2014; p. 1-15.
  • Snidaro L, Visentini I, Bryan K. Fusing uncertain knowledge and evidence for maritime situational awareness via Markov Logic Networks, Information Fusion. 2015; 21:15972. Crossref
  • Chahuara P, Fleury A, Portet F, Vacher M. Using markov logic network for on-line activity recognition from non-visual home automation sensors. Springer Berlin Heidelberg: Ambient intelligence. 2012; p. 177-92. Crossref
  • Chahuara P, Portet F, Vacher M. Context aware decision system in a smart home: knowledge representation and decision making using uncertain contextual information. The 4th International Workshop on Acquisition, Representation and Reasoning with Contextualized Knowledge (ARCOE-12). 2012; p. 52-64.
  • Tenorth M, Beetz M. KnowRob: A knowledge processing infrastructure for cognition-enabled robots, The International Journal of Robotics Research. 2013; 32(5):566-90. Crossref
  • Papai T, Kautz H, Stefankovic D. Slice normalized dynamic markov logic networks. In Advances in Neural Information Processing Systems. 2012; p. 1907-15.
  • Kiddon C, Domingos P. AAAI: Coarse-to-Fine Inference and Learning for First-Order Probabilistic Models. 2011; p. 1-8.
  • Kok S, Domingos P. Learning Markov logic networks using structural motifs. Proceedings of the 27th International Conference on Machine Learning (ICML-10). 2010; p. 55158.
  • Ahmadi B, Kersting K, Mladenov M, Natarajan S. Exploiting symmetries for scaling loopy belief propagation and relational training, Machine learning. 2013; 92(1):91132. Crossref

Refbacks

  • »
  • »
  • »
  • »
  • »
  • »
  • »
  • »
  • »
  • »
  • »
  • »
  • »
  • »


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.