Statistical Relational Artificial Intelligence. Luc De Raedt

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      no matter how many blocks there are.

      Example 1.6 Robotics As robots start to perform everyday manipulation tasks, such as cleaning up, setting a table or preparing simple meals, they must become much more knowledgeable than they are today. Typically, everyday tasks are specified vaguely and the robot must therefore infer what are the appropriate actions to take and which are the appropriate individuals involved in order to accomplish these tasks. These inferences can only be done if the robot has access to a compact and general knowledge base [Beetz et al., 2010, Tenorth et al., 2011]. StarAI provides the means for action-centered representation, for the automated acquisition of concepts through observation and experience, for reasoning about and managing uncertainty, and for fast inference.

      These are only few examples for the many StarAI applications. It is clear that they all fit the definition of StarAI given above, that is, there are individuals that are connected to one another through relations and there is uncertainty that needs to be taken into account.

      There are many more applications of StartAI. For instance, Getoor et al. [2001c] used statistical relational models to estimate the result size of complex database queries. Segal et al. [2001] employed probabilistic relational models for clustering gene expression data and to discover cellular processes from gene expression data [Segal et al., 2003]. Getoor et al. [2004] used probabilistic relational models to understand tuberculosis epidemiology. McGovern et al. [2003] estimated probabilistic relational trees to discover publication patterns in high-energy physics. Neville et al. [2005] used probabilistic relational trees to learn to rank brokers with respect to the probability that they would commit a serious violation of securities regulations in the near future. Anguelov et al. [2005] used relational Markov networks for segmentation of 3D scan data. Relational Markov networks have also been used to compactly represent object maps and to estimate trajectories of people by Limketkai et al. [2005]. Kersting et al. [2006] employed relational hidden Markov models for protein fold recognition. Singla and Domingos [2006] proposed a Markov logic model for entity resolution (ER), the problem of finding records corresponding to the same real-world entity. Markov logic has also been used for joint inference for event extraction [Poon and Domingos, 2007, Riedel and McCallum, 2011]. Poon and Domingos [2008] showned how to use Markov logic to perform joint unsupervised coreference resolution. Nonparametric relational models have been used for analyzing social networks [Xu et al., 2009a]. Kersting and Xu [2009] and Xu et al. [2010] used relational Gaussian processes for learning to rank search results. Poon and Domingos [2009] showned how to perform unsupervised semantic parsing using Markov logic networks, and Davis and Domingos [2009] used MLNs to successfully transfer learned knowledge among molecular biology, social network and Web domains. Yoshikawa et al. [2009] used Markov logic for identifying temporal relations in text, Meza-Ruíz and Riedel [2009] for semantic role labeling, and Kiddon and Domingos [2011] for biomolecular event prediction tasks. Niepert et al. [2010] used Markov logic for ontology matching. Tenenbaum et al. [2011] demonstrated that relational models can address some of the deepest questions about the nature and origins of human thought. Verbeke et al. [2012] used statistical relational learning for identifying evidence based medicine categories. Schiegg et al. [2012] segmented motion capture data using Markov logic mixture of Gaussian processes making use of both continuous and declarative observations. Song et al. [2013] presented a Markov logic framework for recognizing complex events from multimodal data. Statistical relational learning has also been employed for learning to predict heart attacks [Weiss et al., 2012], onset of Alzheimer’s [Natarajan et al., 2012a], for analyzing pathways and omics experiments [De Maeyer et al., 2013], and extracting adverse drug events from text [Odom et al., 2015]. Lang et al. [2012] applied statistical relational learning to robot manipulations tasks, Nitti et al. [2014] to robot tracking tasks, and Moldovan and De Raedt [2014] to affordance learning. Generally, statistical relational methods are essential for enabling autonomous robots to do the right thing to the right object in the right way [Tenorth and Beetz, 2013]. Hadiji et al. [2015] used relational label propagation to track migration among computer scientists. Kersting et al. [2015] used relational linear programs for topic classification in bibliographic networks, and Toussaint [2015] used relational mathematical programs for combined robot task and motion planning. Foulds et al. [2015] showcased the benefit of relational probabilistic languages for general-purpose topic modeling. Khot et al. [2015b] used Markov logic to answer elementary-level science questions using knowledge extracted automatically from textbooks, and Mallory et al. [2016] used DeepDive to extract both protein–protein and transcription factor interactions from over 100,000 full-text PLOS articles.