Nowadays web users actively generate content on different social media platforms. The large number of users requiring personalized services creates a unique opportunity for researchers to explore user modelling. Substantial research has been done by utilizing user generated content to model users by applying different classification or regression techniques. These techniques are powerful types of machine learning approaches, however they only partially model social media users. In this work, we introduce a new statistical relational learning (SRL) framework suitable for this purpose, which we call PSL Q . PSL Q is the first SRL framework that supports reasoning with soft quantifiers, such as “most” and “a few”. Indeed, in models for social media it is common to assume that friends are influenced by each other’s behavior, beliefs, and preferences. Thus, having a trait only becomes probable once most or some of one’s friends have that trait. Expressing this dependency requires a soft quantifier, which can be modeled with PSL^Q. Our experimental results for link prediction in social trust networks demonstrate that the use of soft quantifiers not only allows for a natural and intuitive formulation of domain knowledge, but also improves the accuracy of inferred results.

The latest research on prediction of the outcome of elections using Twitter data, the election tweets labeling area has hardly been explored. Therefore, the authors of this paper propose to develop a semi-automated model for labeling political tweets. The expected result of this study is to contribute to enhance the quality of the choice of messages used in the labeling process by reducing the time selection of messages and the efficiency of classifying the messages and, thus, to increase the accuracy of the models using this approach. The proposed method could label 2200 messages from the analysis of only 60 messages by 20 users. The first results obtained by the method were higher than the process carried out manually by humans.

Most logic-based machine learning algorithms rely on an Occamist bias where textual simplicity of hypotheses is optimised. This approach, however, fails to distinguish between the efficiencies of hypothesised programs, such as quick sort (O(n log n)) and bubble sort (O(n^2)). We address this issue by considering techniques to minimise both the resource complexity and textual complexity of hypothesised programs. We describe an algorithm proven to learn optimal resource complexity robot strategies, and we propose future work to generalise this approach to a broader class of logic programs.

This paper presents two frameworks that generalize Conditional Preference networks (CP-nets). The first generalization is the LCP-theory, first order logic theory that provides a rich framework to express preferences. The the second generalization, the PCP-networks, is a probabilistic generalization of CP-nets that models conditional preferences with uncertainty.

Reinforcement learning algorithms typically require too many `trial-and-error' experiences before reaching a desirable behaviour. A considerable amount of ongoing research is focused on speeding up this learning process by using external knowledge. We contribute in several ways, proposing novel approaches to transfer learning and learning from demonstration, as well as an ensemble approach to combine knowledge from various sources.

Stream reasoning is the task of continuously deriving conclusions on streaming data. As a research theme, it is targeted by different communities which emphasize different aspects, e.g., throughput vs. expressiveness. This thesis aims to advance the theoretical foundations underlying diverse stream reasoning approaches and to convert obtained insights into a prototypical expressive rule-based reasoning system that is lacking to date.

Most work in heuristic search considers problems where a low cost solution is preferred (MIN problems). In this paper, we investigate the complementary setting where a solution of high reward is preferred (MAX problems). Example MAX problems include finding a longest simple path in a graph, maximal coverage, and various constraint optimization problems. We examine several popular search algorithms for MIN problems and discover the curious ways in which they misbehave on MAX problems. We propose modifications that preserve the original intentions behind the algorithms but allow them to solve MAX problems, and compare them theoretically and empirically. Interesting results include the failure of bidirectional search and close relationships between Dijkstra's algorithm, weighted A*, and depth-first search.

Modeling real world domains requires ever more frequently to represent uncertain information. The DISPONTE semantics for probabilistic description logics allows to annotate axioms of a knowledge base with a value that represents their probability. In this paper we discuss approaches for performing inference from probabilistic ontologies following the DISPONTE semantics. We present the algorithm BUNDLE for computing the probability of queries. BUNDLE exploits an underlying Description Logic reasoner, such as Pellet, in order to find explanations for a query. These are then encoded in a Binary Decision Diagram that is used for computing the probability of the query.

This model is used to automatically synthetise a controller that maps executions to the next action to perform. Many heuristics for cost-optimal planning are The problem is thus cast as a synthesis problem from a based on linear programming. We cover several given specification. Two obstacles for this approach are that interesting heuristics of this type by a common a suitable model for the task is needed, and that the synthesis framework that fixes the objective function of the problem is intractable in general. But, this intractability does linear program. Within the framework, constraints not preclude the approach from being effective in meaningful from different heuristics can be combined in one cases. Planning is the model-based approach to autonomous heuristic estimate which dominates the maximum behaviour.

Personalization aims to tailor content to a person’s individual tastes. As a result, the tasks that benefit from personalization are inherently subjective. Many of the most robust approaches to personalization rely on large sets of other people’s preferences. However, existing preference data is not always available. In these cases, we propose leveraging online crowds to provide on-demand personalization. We introduce and evaluate two methods for personalized crowdsourcing: taste-matching for finding crowd workers who are similar to the requester, and taste-grokking , where crowd workers explicitly predict the requester’s tastes. Both approaches show improvement over a non-personalized baseline, with taste-grokking performing well in simpler tasks and taste-matching performing well with larger crowds and tasks with latent decision-making variables.