An Asymptotic Analysis of Probabilistic Logic Programming, with Implications for Expressing Projective Families of Distributions

Probabilistic logic programming is a major part of statistical relational artificial intelligence, where approaches from logic and probability are brought together to reason about and learn from relational domains in a setting of uncertainty. However, the behaviour of statistical relational representations across variable domain sizes is complex, and scaling inference and learning to large domains remains a significant challenge. In recent years, connections have emerged between domain size dependence, lifted inference and learning from sampled subpopulations. The asymptotic behaviour of statistical relational representations has come under scrutiny, and projectivity was investigated as the strongest form of domain size dependence, in which query marginals are completely independent of the domain size. In this contribution we show that every probabilistic logic program under the distribution semantics is asymptotically equivalent to an acyclic probabilistic logic program consisting only of determinate clauses over probabilistic facts. We conclude that every probabilistic logic program inducing a projective family of distributions is in fact everywhere equivalent to a program from this fragment, and we investigate the consequences for the projective families of distributions expressible by probabilistic logic programs.

Generating contrastive explanations for inductive logic programming based on a near miss approach

In recent research, human-understandable explanations of machine learning models have received a lot of attention. Often explanations are given in form of model simplifications or visualizations. However, as shown in cognitive science as well as in early AI research, concept understanding can also be improved by the alignment of a given instance for a concept with a similar counterexample. Contrasting a given instance with a structurally similar example which does not belong to the concept highlights what characteristics are necessary for concept membership. Such near misses have been proposed by Winston (Learning structural descriptions from examples, 1970) as efficient guidance for learning in relational domains. We introduce an explanation generation algorithm for relational concepts learned with Inductive Logic Programming (GeNME). The algorithm identifies near miss examples from a given set of instances and ranks these examples by their degree of closeness to a specific positive instance. A modified rule which covers the near miss but not the original instance is given as an explanation. We illustrate GeNME with the well-known family domain consisting of kinship relations, the visual relational Winston arches domain, and a real-world domain dealing with file management. We also present a psychological experiment comparing human preferences of rule-based, example-based, and near miss explanations in the family and the arches domains.

Morphological analysis of mosaic shapes with directed relationships based on attribute and relational model representations

We introduce attribute and relational representations of mosaic image models with directed relationships between regions. Attribute representations of asymmetric relational models based on stacking, ranking and integral descriptions are considered. We propose some morphological shape similarity measures based on relational models. We show that using the same oriented relational model, various morphological operators can be constructed, in particular, of Serra- or Pyt’ev type. Some constructive methods for the design of such morphological operators in an attribute and relational domains are proposed. From this consideration we also extract a new morophlogical scheme for two-stage mutual adaptive image-and-shape joint filtering: at the first step, the shape is simplified (projected) with regard to the image to be projected, and at the second step, the image is simplified (projected) with regard to the simplified (projected) shape.

Interactive Learning with Mutual Explanations in Relational Domains

With the growing number of applications of machine learning in complex real-world domains machine learning research has to meet new requirements to deal with the imperfections of real world data and the legal as well as ethical obligations to make classifier decisions transparent and comprehensible. In this contribution, arguments for interpretable and interactive approaches to machine learning are presented. It is argued that visual explanations are often not expressive enough to grasp critical information which relies on relations between different aspects or sub-concepts. Consequently, inductive logic programming (ILP) and the generation of verbal explanations from Prolog rules is advocated. Interactive learning in the context of ILP is illustrated with the Dare2Del system which helps users to manage their digital clutter. It is shown that verbal explanations overcome the explanatory one-way street from AI system to user. Interactive learning with mutual explanations allows the learning system to take into account not only class corrections but also corrections of explanations to guide learning. We propose mutual explanations as a building-block for human-like computing and an important ingredient for human AI partnership.

Learning hierarchical probabilistic logic programs

Probabilistic logic programming (PLP) combines logic programs and probabilities. Due to its expressiveness and simplicity, it has been considered as a powerful tool for learning and reasoning in relational domains characterized by uncertainty. Still, learning the parameter and the structure of general PLP is computationally expensive due to the inference cost. We have recently proposed a restriction of the general PLP language called hierarchical PLP (HPLP) in which clauses and predicates are hierarchically organized. HPLPs can be converted into arithmetic circuits or deep neural networks and inference is much cheaper than for general PLP. In this paper we present algorithms for learning both the parameters and the structure of HPLPs from data. We first present an algorithm, called parameter learning for hierarchical probabilistic logic programs (PHIL) which performs parameter estimation of HPLPs using gradient descent and expectation maximization. We also propose structure learning of hierarchical probabilistic logic programming (SLEAHP), that learns both the structure and the parameters of HPLPs from data. Experiments were performed comparing PHIL and SLEAHP with PLP and Markov Logic Networks state-of-the art systems for parameter and structure learning respectively. PHIL was compared with EMBLEM, ProbLog2 and Tuffy and SLEAHP with SLIPCOVER, PROBFOIL+, MLB-BC, MLN-BT and RDN-B. The experiments on five well known datasets show that our algorithms achieve similar and often better accuracies but in a shorter time.

Unpacking Community-Based Youth Mentoring Relationships: An Integrative Review

Formal community-based youth mentoring relationships (CBM) are a popular form of intervention worldwide in which caring, non-parental adult figures are matched with at-risk children (i.e., children who experience an intense and/or chronic risk factor, or a combination of risk factors in personal, environmental and/or relational domains that prevent them from pursuing and fulfilling their potential) to promote development and health. Common models suggest that a close mentoring relationship is needed for the success of the intervention. However, it remains unclear which key relational processes and variables promote relationship quality to generate the most significant benefits. Using the PRISMA framework, 123 articles were identified as relevant for this review which explores the state of the literature on CBM relationships describing the main findings regarding the characteristics of the relationship and the mediating and moderating variables. An essential ingredient that consistently emerged for generating mentoring outcomes is characterized by feelings of support, sensitivity, and trust and accompanied by a purposeful approach to shaping the goals of the relationship. A balanced approach comprised of recreational, emotional, and catalyzing aspects has been reported as essential for mentoring success. Mentors’ positive attitudes toward underprivileged youth, maturity in terms of age and experience are essential in forging positive relationships. Mentees who have better relational histories and more positive personality traits exhibited higher relationship quality. However, data imply the possibility of addressing mentees from moderate risk status. Preliminary evidence on thriving as a mediating variable was found. Program practices, such as training, parental involvement, and matching based on perceived similarities and similar interests, emerged as important factors. Generating many research suggestions, the review identifies research questions and uncharted territories that require inquiry.

The roots of ecological dominance orientation: Assessing individual preferences for an anthropocentric and hierarchically organized world

Ecological dominance is a central concept in the study of interspecies and species- environment relations. Yet, although theoretical and empirical work on ecological dominance has progressed in many scientific disciplines, the psychology of ecological dominance remains understudied. The present research attempts to advance theoretical and empirical inquiry on ecological dominance as a psychological predisposition, examining how and why it influences humans’ perceptions, attitudes, and behaviors across different relational domains (i.e., intraspecies, interspecies, human-environment). To this end, we validate a novel measure, the Ecological Dominance Orientation (EDO) scale, based on the popular iconic depiction of eco-centric vs. anthropocentric arrangements of the relationship between humans, non-human animals, and the natural environment. In two pre-registered studies conducted across 2 countries (N = 1,312), we demonstrate that EDO a) shapes attitudes in a similar fashion both within and between different relational domains (i.e., intergroup, interspecies, human-environment relations), b) is uniquely predictive of numerous socially consequential attitudes across relational domains (i.e., modern sexism, modern racism, speciesism, anthropocentrism) over and above established measures of personal ideology and beliefs, and c) is reliable over time. This research extends classical Social Dominance Theory (Sidanius and Pratto, 1999) by theorizing about the socio-ecological roots of intergroup, interspecies, and human-environment relations as hierarchically structured power relations. Theoretical and practical implications of social and ecological dominance orientations are discussed.

Love at Home: Exploring Love in Religious Family Relationships

The purpose of the study was to qualitatively investigate love in religious family relationships. Participants were from the American Families of Faith Project, a qualitative study on religion and family life with participants from 198 Christian, Jewish, and Muslim families (N = 478) across the United States. The primary research questions of present study were (a) what does love mean for families? (b) why do individuals and couples in families love? (c) how is love experienced? (d) what are the related processes of love? (e) how does religion influence love in religious families? and (f) what are the reported outcomes of love for individuals and families? Interview data was analyzed through a three-phase approach: feasibility study, codebook development, and grounded theory coding. The first phase conducted by two coders, excluding the author, concluded that there was sufficient data to conduct further analysis. The second phase was conducted by four coders, excluding the author and the two previous coders, who developed a codebook and organized data into four relational domains (marital, parental, children’s, and divine) and six categories, which were based on the research questions (meaning, motivation, process, experience, influence, and outcome). In the third phase, the author analyzed the intersections between domains and categories through matrix coding and numeric content analysis. Then, using modified grounded theory approaches, themes were developed and presented as findings with illustrative participant quotations. Finally, findings, limitations, future directions, and implications for therapists and educators were discussed.