Recent Advances in Markov Logic Networks

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.