Lifted graphical models: a survey

Lifted graphical models provide a language for expressing dependencies between different types of entities, their attributes, and their diverse relations, as well as techniques for probabilistic reasoning in such multi-relational domains. In this survey, we review a general form for a lifted graphical model, a par-factor graph, and show how a number of existing statistical relational representations map to this formalism. We discuss inference algorithms, including lifted inference algorithms, that efficiently compute the answers to probabilistic queries over such models. We also review work in learning lifted graphical models from data. There is a growing need for statistical relational models (whether they go by that name or another), as we are inundated with data which is a mix of structured and unstructured, with entities and relations extracted in a noisy manner from text, and with the need to reason effectively with this data. We hope that this synthesis of ideas from many different research groups will provide an accessible starting point for new researchers in this expanding field

Enhancing maritime defence and security through persistently autonomous operations and situation awareness systems

This thesis is concerned with autonomous operations with Autonomous Underwater Vehicles(AUVs) and maritime situation awareness in the context of enhancing maritime defence and security. The problem of autonomous operations with AUVs is one of persistence. That is, AUVs get stuck due to a lack of cognitive ability to deal with a situation and require intervention from a human operator. This thesis focuses on addressing vehicle subsystem failures and changes in high level mission priorities in a manner that preserves autonomy during Mine Counter measures (MCM) operations in unknown environments. This is not a trivial task. The approach followed utilizes ontologies for representing knowledge about the operational environment, the vehicle as well as mission planning and execution. Reasoning about the vehicle capabilities and consequently the actions it can execute is continuous and occurs in real time. Vehicle component faults are incorporated into the reasoning process as a means of driving adaptive planning and execution. Adaptive planning is based on a Planning Domain Definition Language (PDDL) planner. Adaptive execution is prioritized over adaptive planning as mission planning can be very demanding in terms of computational resources. Changes in high level mission priorities are also addressed as part of the adaptive planning behaviour of the system. The main contribution of this thesis regarding persistently autonomous operations is an ontological framework that drives an adaptive behaviour for increasing persistent autonomy of AUVs in unexpected situations. That is, when vehicle component faults threaten to put the mission at risk and changes in high level mission priorities should be incorporated as part of decision making. Building maritime situation awareness for maritime security is a very difficult task. High volumes of information gathered from various sources as well as their efficient fusion taking into consideration any contradictions and the requirement for reliable decision making and (re)action under potentially multiple interpretations of a situation are the most prominent challenges. To address those challenges and help alleviate the burden from humans which usually undertake such tasks, this thesis is concerned with maritime situation awareness built with Markov Logic Networks(MLNs) that support humans in their decision making. However, commonly maritime situation awareness systems rely on human experts to transfer their knowledge into the system before it can be deployed. In that respect, a promising alternative for training MLNs with data is presented. In addition, an in depth evaluation of their performance is provided during which the significance of interpreting an unfolding situation in context is demonstrated. To the best of the author’s knowledge, it is the first time that MLNs are trained with data and evaluated using cross validation in the context of building maritime situation awareness for maritime security

Efficient instance and hypothesis space revision in Meta-Interpretive Learning

Inductive Logic Programming (ILP) is a form of Machine Learning. The goal of ILP is to induce hypotheses, as logic programs, that generalise training examples. ILP is characterised by a high expressivity, generalisation ability and interpretability. Meta-Interpretive Learning (MIL) is a state-of-the-art sub-field of ILP. However, current MIL approaches have limited efficiency: the sample and learning complexity respectively are polynomial and exponential in the number of clauses. My thesis is that improvements over the sample and learning complexity can be achieved in MIL through instance and hypothesis space revision. Specifically, we investigate 1) methods that revise the instance space, 2) methods that revise the hypothesis space and 3) methods that revise both the instance and the hypothesis spaces for achieving more efficient MIL. First, we introduce a method for building training sets with active learning in Bayesian MIL. Instances are selected maximising the entropy. We demonstrate this method can reduce the sample complexity and supports efficient learning of agent strategies. Second, we introduce a new method for revising the MIL hypothesis space with predicate invention. Our method generates predicates bottom-up from the background knowledge related to the training examples. We demonstrate this method is complete and can reduce the learning and sample complexity. Finally, we introduce a new MIL system called MIGO for learning optimal two-player game strategies. MIGO learns from playing: its training sets are built from the sequence of actions it chooses. Moreover, MIGO revises its hypothesis space with Dependent Learning: it first solves simpler tasks and can reuse any learned solution for solving more complex tasks. We demonstrate MIGO significantly outperforms both classical and deep reinforcement learning. The methods presented in this thesis open exciting perspectives for efficiently learning theories with MIL in a wide range of applications including robotics, modelling of agent strategies and game playing.Open Acces

Using Constraint Satisfaction Techniques and Variational Methods for Probabilistic Reasoning

RÉSUMÉ Cette thèse présente un certain nombre de contributions à la recherche pour la création de systèmes efficaces de raisonnement probabiliste sur les modèles graphiques de problèmes issus d'une variété d'applications scientifiques et d'ingénierie. Ce thème touche plusieurs sous-disciplines de l'intelligence artificielle. Généralement, la plupart de ces problèmes ont des modèles graphiques expressifs qui se traduisent par de grands réseaux impliquant déterminisme et des cycles, ce qui représente souvent un goulot d'étranglement pour tout système d'inférence probabiliste et affaiblit son exactitude ainsi que son évolutivité. Conceptuellement, notre recherche confirme les hypothèses suivantes. D'abord, les techniques de satisfaction de contraintes et méthodes variationnelles peuvent être exploitées pour obtenir des algorithmes précis et évolutifs pour l'inférence probabiliste en présence de cycles et de déterminisme. Deuxièmement, certaines parties intrinsèques de la structure du modèle graphique peuvent se révéler bénéfiques pour l'inférence probabiliste sur les grands modèles graphiques, au lieu de poser un défi important pour elle. Troisièmement, le re-paramétrage du modèle graphique permet d'ajouter à sa structure des caractéristiques puissantes qu'on peut utiliser pour améliorer l'inférence probabiliste. La première contribution majeure de cette thèse est la formulation d'une nouvelle approche de passage de messages (message-passing) pour inférer dans un graphe de facteurs étendu qui combine des techniques de satisfaction de contraintes et des méthodes variationnelles. Contrairement au message-passing standard, il formule sa structure sous forme d'étapes de maximisation de l'espérance variationnelle. Ainsi, on a de nouvelles règles de mise à jour des marginaux qui augmentent une borne inférieure à chaque mise à jour de manière à éviter le dépassement d'un point fixe. De plus, lors de l'étape d'espérance, nous mettons à profit les structures locales dans le graphe de facteurs en utilisant la cohérence d'arc généralisée pour effectuer une approximation de champ moyen variationnel. La deuxième contribution majeure est la formulation d'une stratégie en deux étapes qui utilise le déterminisme présent dans la structure du modèle graphique pour améliorer l'évolutivité du problème d'inférence probabiliste. Dans cette stratégie, nous prenons en compte le fait que si le modèle sous-jacent implique des contraintes inviolables en plus des préférences, alors c'est potentiellement un gaspillage d'allouer de la mémoire pour toutes les contraintes à l'avance lors de l'exécution de l'inférence. Pour éviter cela, nous commençons par la relaxation des préférences et effectuons l'inférence uniquement avec les contraintes inviolables. Cela permet d'éviter les calculs inutiles impliquant les préférences et de réduire la taille effective du réseau graphique. Enfin, nous développons une nouvelle famille d'algorithmes d'inférence par le passage de messages dans un graphe de facteurs étendus, paramétrées par un facteur de lissage (smoothing parameter). Cette famille permet d'identifier les épines dorsales (backbones) d'une grappe qui contient des solutions potentiellement optimales. Ces épines dorsales ne sont pas seulement des parties des solutions optimales, mais elles peuvent également être exploitées pour intensifier l'inférence MAP en les fixant de manière itérative afin de réduire les parties complexes jusqu'à ce que le réseau se réduise à un seul qui peut être résolu avec précision en utilisant une méthode MAP d'inférence classique. Nous décrivons ensuite des variantes paresseuses de cette famille d'algorithmes. Expérimentalement, une évaluation empirique approfondie utilisant des applications du monde réel démontre la précision, la convergence et l'évolutivité de l'ensemble de nos algorithmes et stratégies par rapport aux algorithmes d'inférence existants de l'état de l'art.----------ABSTRACT This thesis presents a number of research contributions pertaining to the theme of creating efficient probabilistic reasoning systems based on graphical models of real-world problems from relational domains. These models arise in a variety of scientific and engineering applications. Thus, the theme impacts several sub-disciplines of Artificial Intelligence. Commonly, most of these problems have expressive graphical models that translate into large probabilistic networks involving determinism and cycles. Such graphical models frequently represent a bottleneck for any probabilistic inference system and weaken its accuracy and scalability. Conceptually, our research here hypothesizes and confirms that: First, constraint satisfaction techniques and variational methods can be exploited to yield accurate and scalable algorithms for probabilistic inference in the presence of cycles and determinism. Second, some intrinsic parts of the structure of the graphical model can turn out to be beneficial to probabilistic inference on large networks, instead of posing a significant challenge to it. Third, the proper re-parameterization of the graphical model can provide its structure with characteristics that we can use to improve probabilistic inference. The first major contribution of this thesis is the formulation of a novel message-passing approach to inference in an extended factor graph that combines constraint satisfaction techniques with variational methods. In contrast to standard message-passing, it formulates the Message-Passing structure as steps of variational expectation maximization. Thus it has new marginal update rules that increase a lower bound at each marginal update in a way that avoids overshooting a fixed point. Moreover, in its expectation step, we leverage the local structures in the factor graph by using generalized arc consistency to perform a variational mean-field approximation. The second major contribution is the formulation of a novel two-stage strategy that uses the determinism present in the graphical model's structure to improve the scalability of probabilistic inference. In this strategy, we take into account the fact that if the underlying model involves mandatory constraints as well as preferences then it is potentially wasteful to allocate memory for all constraints in advance when performing inference. To avoid this, we start by relaxing preferences and performing inference with hard constraints only. This helps avoid irrelevant computations involving preferences, and reduces the effective size of the graphical network. Finally, we develop a novel family of message-passing algorithms for inference in an extended factor graph, parameterized by a smoothing parameter. This family allows one to find the ”backbones” of a cluster that involves potentially optimal solutions. The cluster's backbones are not only portions of the optimal solutions, but they also can be exploited for scaling MAP inference by iteratively fixing them to reduce the complex parts until the network is simplified into one that can be solved accurately using any conventional MAP inference method. We then describe lazy variants of this family of algorithms. One limiting case of our approach corresponds to lazy survey propagation, which in itself is novel method which can yield state of the art performance. We provide a thorough empirical evaluation using real-world applications. Our experiments demonstrate improvements to the accuracy, convergence and scalability of all our proposed algorithms and strategies over existing state-of-the-art inference algorithms

Efficient Learning and Evaluation of Complex Concepts in Inductive Logic Programming

Inductive Logic Programming (ILP) is a subfield of Machine Learning with foundations in logic programming. In ILP, logic programming, a subset of first-order logic, is used as a uniform representation language for the problem specification and induced theories. ILP has been successfully applied to many real-world problems, especially in the biological domain (e.g. drug design, protein structure prediction), where relational information is of particular importance. The expressiveness of logic programs grants flexibility in specifying the learning task and understandability to the induced theories. However, this flexibility comes at a high computational cost, constraining the applicability of ILP systems. Constructing and evaluating complex concepts remain two of the main issues that prevent ILP systems from tackling many learning problems. These learning problems are interesting both from a research perspective, as they raise the standards for ILP systems, and from an application perspective, where these target concepts naturally occur in many real-world applications. Such complex concepts cannot be constructed or evaluated by parallelizing existing top-down ILP systems or improving the underlying Prolog engine. Novel search strategies and cover algorithms are needed. The main focus of this thesis is on how to efficiently construct and evaluate complex hypotheses in an ILP setting. In order to construct such hypotheses we investigate two approaches. The first, the Top Directed Hypothesis Derivation framework, implemented in the ILP system TopLog, involves the use of a top theory to constrain the hypothesis space. In the second approach we revisit the bottom-up search strategy of Golem, lifting its restriction on determinate clauses which had rendered Golem inapplicable to many key areas. These developments led to the bottom-up ILP system ProGolem. A challenge that arises with a bottom-up approach is the coverage computation of long, non-determinate, clauses. Prolog’s SLD-resolution is no longer adequate. We developed a new, Prolog-based, theta-subsumption engine which is significantly more efficient than SLD-resolution in computing the coverage of such complex clauses. We provide evidence that ProGolem achieves the goal of learning complex concepts by presenting a protein-hexose binding prediction application. The theory ProGolem induced has a statistically significant better predictive accuracy than that of other learners. More importantly, the biological insights ProGolem’s theory provided were judged by domain experts to be relevant and, in some cases, novel