Figure 1.7: The robot’s relational probabilistic model is populated with the data produced by its perception and experience (robot log data, human motion tracking, environment information, etc.) as well as with facts (or assertions) extracted from other dark data.

      It is our belief that there will be many more exciting techniques and applications of StarAI in the future.

1.5 BRIEF HISTORICAL OVERVIEW

      Before starting the technical introduction to StarAI, we briefly sketch some key streams of research that have led to the field of StarAI. Note that a full historical overview of this rich research field is out of the scope of this book.

      StarAI and SRL (Statistical Relational Learning) basically integrate three key technical ingredients of AI: logic, probability, and learning. While each of these constituents has a rich tradition in AI, there has been a strong interest in pairwise combinations of these techniques since the early 1990s.

      - Indeed, various techniques for probabilistic learning such as gradient-based methods, the family of EM algorithms or Markov Chain Monte Carlo methods have been developed and exhaustively investigated in different communities, such as in the Uncertainty in AI community for Bayesian networks and in the Computational Linguistics community for Hidden Markov Models. These techniques are not only theoretically sound, they have also resulted in entirely new technologies for, and revolutionary novel products in computer vision, speech recognition, medical diagnostics, troubleshooting systems, etc. Overviews of probabilistic learning can be found in Koller and Friedman [2009], Bishop [2006], Murphy [2012].

      - Inductive logic programming and relational learning techniques studied logic learning, i.e., learning using first order logical or relational representations. Inductive Logic Programming has significantly broadened the application domain of data mining especially in bio- and chemo-informatics and now represent some of the best-known examples of scientific discovery by AI systems in the literature. Overviews of inductive logic learning and relational learning can be found in this volume and in Muggleton and De Raedt [1994], Lavrac and Dzeroski [1994], De Raedt [2008].

      - Probabilistic logics have been also studied from a knowledge representational perspective [Nilsson, 1986, Halpern, 1990, Poole, 1993b]. The aim of this research was initially more a probabilistic characterization of logic than suitable representations for learning.

      In the late 1990s and early 2000s, researchers working on these pairwise combinations started to realize that they also need the third component. Influential approaches of this period include the Probabilistic Relational Models [Friedman et al., 1999], which extended the probabilistic graphical model approach toward relations, the probabilistic logic programming approach of Sato [1995], which extended the knowledge representation approach of Poole [1993b] with a learning mechanism, and approaches such as Bayesian and stochastic logic programs [Kersting and De Raedt, 2001, Cussens, 2001] working within an inductive logic programming tradition. Around 2000, Lise Getoor and David Jensen organized the first workshop on “Statistical Relational Learning” at AAAI which gathered researchers active in the area for this time. This was the start of a successful series of events that continues till today (since 2010 under the header “StarAI”).

      In the early days, many additional formalisms were contributed such as RBNs [Jäger, 1997], MMMNs [Taskar et al., 2004], SRMs [Getoor et al., 2001c], MLNs [Richardson and Domingos, 2006], BLOG [Milch et al., 2005], RDNs [Neville and Jensen, 2004], and IBAL [Pfeffer, 2001], many of which are described in the book edited by Getoor and Taskar [2007]. Especially influential was the Markov Logic approach of Richardson and Domingos [2006], as it was an elegant combination of undirected graphical models with relational logic. In this early period, research focused a lot on representational and expressivity issues, often referring to the “alphabet-soup” of systems, reflecting the fact that many systems have names that use three or four letters from the alphabet. Around 2005, it was realized that the commonalities between the different systems are more important than their (often syntactic) differences, and research started to focus on key open issues surrounding learning and inference. One central question is that of lifted inference [Poole, 2003], that is, whether one can perform probabilistic inference without grounding out the probabilistic relational model first. Other questions concern the relationship to existing probabilistic and logical solvers and the continuing quest for efficient inference techniques. Finally, research on SRL and StarAI has also inspired the addition of probabilistic primitives to programming languages, leading to what is now called probabilistic programming. Although some of the early formalisms [Poole, 1993b, Sato, 1995, Pfeffer, 2001] already extend an existing programming language with probabilities, and hence, possess the power of a universal Turing machine, this stream of research became popular since BLOG [Milch et al., 2005] and Church [Goodman et al., 2008].