Neural Diagrammatic Reasoning

Diagrams have been shown to be effective tools for humans to represent and reason about complex concepts. They have been widely used to represent concepts in science teaching, to communicate workflow in industries and to measure human fluid intelligence. Mechanised reasoning systems typically encode diagrams into symbolic representations that can be easily processed with rule-based expert systems. This relies on human experts to define the framework of diagram-to-symbol mapping and the set of rules to reason with the symbols. This means the reasoning systems cannot be easily adapted to other diagrams without a new set of human-defined representation mapping and reasoning rules. Moreover such systems are not able to cope with diagram inputs as raw and possibly noisy images. The need for human input and the lack of robustness to noise significantly limit the applications of mechanised diagrammatic reasoning systems. A key research question then arises: can we develop human-like reasoning systems that learn to reason robustly without predefined reasoning rules? To answer this question, I propose Neural Diagrammatic Reasoning, a new family of diagrammatic reasoning systems which does not have the drawbacks of mechanised reasoning systems. The new systems are based on deep neural networks, a recently popular machine learning method that achieved human-level performance on a range of perception tasks such as object detection, speech recognition and natural language processing. The proposed systems are able to learn both diagram to symbol mapping and implicit reasoning rules only from data, with no prior human input about symbols and rules in the reasoning tasks. Specifically I developed EulerNet, a novel neural network model that solves Euler diagram syllogism tasks with 99.5% accuracy. Experiments show that EulerNet learns useful representations of the diagrams and tasks, and is robust to noise and deformation in the input data. I also developed MXGNet, a novel multiplex graph neural architecture that solves Raven Progressive Matrices (RPM) tasks. MXGNet achieves state-of-the-art accuracies on two popular RPM datasets. In addition, I developed Discrete-AIR, an unsupervised learning architecture that learns semi-symbolic representations of diagrams without any labels. Lastly I designed a novel inductive bias module that can be readily used in today’s deep neural networks to improve their generalisation capability on relational reasoning tasks.EPSRC Studentship and Cambridge Trust Scholarshi

Causal Reinforcement Learning: A Survey

Reinforcement learning is an essential paradigm for solving sequential decision problems under uncertainty. Despite many remarkable achievements in recent decades, applying reinforcement learning methods in the real world remains challenging. One of the main obstacles is that reinforcement learning agents lack a fundamental understanding of the world and must therefore learn from scratch through numerous trial-and-error interactions. They may also face challenges in providing explanations for their decisions and generalizing the acquired knowledge. Causality, however, offers a notable advantage as it can formalize knowledge in a systematic manner and leverage invariance for effective knowledge transfer. This has led to the emergence of causal reinforcement learning, a subfield of reinforcement learning that seeks to enhance existing algorithms by incorporating causal relationships into the learning process. In this survey, we comprehensively review the literature on causal reinforcement learning. We first introduce the basic concepts of causality and reinforcement learning, and then explain how causality can address core challenges in non-causal reinforcement learning. We categorize and systematically review existing causal reinforcement learning approaches based on their target problems and methodologies. Finally, we outline open issues and future directions in this emerging field.Comment: 48 pages, 10 figure

Narratives of Existence and the Narrative Existence: Ontological Unity in the Border Trilogy and Quantum Theory

Because the humanities and the sciences approach philosophical questions in contrasting ways, the study of literature and the study of physical science are often viewed as unrelated realms of scholarly inquiry. Science aims to provide a methodological approach for gathering knowledge about the world, while the humanities focus on criticism or analysis of cultural artifacts. However, even though the conceptual frameworks applied in scientific study and literary study are often incompatible or remarkably divergent, their methods for conceptualizing and transmitting ideas are the same, for humanity understands the world and experience of this world through narratives composed of referential metaphors. Consequently, both realms of study serve as philosophical inquiries into the nature of reality and existence by constructing narratives that illustrate a story of the world, even if their ontological descriptions or frameworks are at odds. By exploring the ways that modern physics demonstrates the world’s wholistic and self-relational behavior, this project illustrates how both fictional stories and narratives from empirical science emanate from the behavior of the world. Analyzing the narrative presented in Cormac McCarthy’s Border Trilogy and demonstrating the ways in which its story is conceptually cohesive with modern physics, this project describes how McCarthy’s fiction and quantum theory are unified in their elaboration of a constitutional mono-ontology. In this way, the complementary connections between these disparate philosophical examples exemplifies a harmonious interpretation of ontology, where the world persists as a continuously fluctuating narrative of material construction

Exploiting multimodality and structure in world representations

An essential aim of artificial intelligence research is to design agents that will eventually cooperate with humans within the real world. To this end, embodied learning is emerging as one of the most important efforts contributed by the machine learning community towards this goal. Recently developing sub-fields concern various aspects of such systems---visual reasoning, language representations, causal mechanisms, robustness to out-of-distribution inputs, to name only a few. In particular, multimodal learning and language grounding are vital to achieving a strong understanding of the real world. Humans build internal representations via interacting with their environment, learning complex associations between visual, auditory and linguistic concepts. Since the world abounds with structure, graph-based encodings are also likely to be incorporated in reasoning and decision-making modules. Furthermore, these relational representations are rather symbolic in nature---providing advantages over other formats, such as raw pixels---and can encode various types of links (temporal, causal, spatial) which can be essential for understanding and acting in the real world. This thesis presents three research works that study and develop likely aspects of future intelligent agents. The first contribution centers on vision-and-language learning, introducing a challenging embodied task that shifts the focus of an existing one to the visual reasoning problem. By extending popular visual question answering (VQA) paradigms, I also designed several models that were evaluated on the novel dataset. This produced initial performance estimates for environment understanding, through the lens of a more challenging VQA downstream task. The second work presents two ways of obtaining hierarchical representations of graph-structured data. These methods either scaled to much larger graphs than the ones processed by the best-performing method at the time, or incorporated theoretical properties via the use of topological data analysis algorithms. Both approaches competed with contemporary state-of-the-art graph classification methods, even outside social domains in the second case, where the inductive bias was PageRank-driven. Finally, the third contribution delves further into relational learning, presenting a probabilistic treatment of graph representations in complex settings such as few-shot, multi-task learning and scarce-labelled data regimes. By adding relational inductive biases to neural processes, the resulting framework can model an entire distribution of functions which generate datasets with structure. This yielded significant performance gains, especially in the aforementioned complex scenarios, with semantically-accurate uncertainty estimates that drastically improved over the neural process baseline. This type of framework may eventually contribute to developing lifelong-learning systems, due to its ability to adapt to novel tasks and distributions. The benchmark, methods and frameworks that I have devised during my doctoral studies suggest important future directions for embodied and graph representation learning research. These areas have increasingly proved their relevance to designing intelligent and collaborative agents, which we may interact with in the near future. By addressing several challenges in this problem space, my contributions therefore take a few steps towards building machine learning systems to be deployed in real-life settings.DREAM CD

Enactive-Dynamic Social Cognition and Active Inference

The aim of this paper is twofold: it critically analyses and rejects accounts blending active inference as theory of mind and enactivism; and it advances an enactivist-dynamic account of social cognition that is compatible with active inference. While some inference models of social cognition seemingly take an enactive perspective on social cognition, they explain it as the attribution of mental states to other people, via representational machinery, in line with Theory of Mind (ToM). Holding both enactivism and ToM, we argue, entails contradiction and confusion due to two ToM assumptions rejected by enactivism: (1) that social cognition reduces to mental representation and (2) cognition must be hardwired with a social cognition contentful “toolkit” or “starter pack” for fueling the model-like theorising supposed in (1). The paper offers a positive alternative, one that avoids contradictions or confusions. After clarifying the profile of social cognition under enactivism, i.e. without assumptions (1) and (2), the last section advances an enactivist-dynamic model of cognition as dynamic, real time, fluid, dynamic, contextual social action, where we use the formalisms of dynamical systems theory to explain the origins of sociocognitive novelty in developmental change and active inference as a tool to explain social understanding as generalised synchronisation

Unmet goals of tracking: within-track heterogeneity of students' expectations for

Educational systems are often characterized by some form(s) of ability grouping, like tracking. Although substantial variation in the implementation of these practices exists, it is always the aim to improve teaching efficiency by creating homogeneous groups of students in terms of capabilities and performances as well as expected pathways. If students’ expected pathways (university, graduate school, or working) are in line with the goals of tracking, one might presume that these expectations are rather homogeneous within tracks and heterogeneous between tracks. In Flanders (the northern region of Belgium), the educational system consists of four tracks. Many students start out in the most prestigious, academic track. If they fail to gain the necessary credentials, they move to the less esteemed technical and vocational tracks. Therefore, the educational system has been called a 'cascade system'. We presume that this cascade system creates homogeneous expectations in the academic track, though heterogeneous expectations in the technical and vocational tracks. We use data from the International Study of City Youth (ISCY), gathered during the 2013-2014 school year from 2354 pupils of the tenth grade across 30 secondary schools in the city of Ghent, Flanders. Preliminary results suggest that the technical and vocational tracks show more heterogeneity in student’s expectations than the academic track. If tracking does not fulfill the desired goals in some tracks, tracking practices should be questioned as tracking occurs along social and ethnic lines, causing social inequality

Feral Ecologies: A Foray into the Worlds of Animals and Media

This dissertation wonders what non-human animals can illuminate about media in the visible contact zones where they meet. It treats these zones as rich field sites from which to excavate neglected material-discursive-semiotic relationships between animals and media. What these encounters demonstrate is that animals are historically and theoretically implicated in the imagination and materialization of media and their attendant processes of communication. Chapter 1 addresses how animals have been excluded from the cultural production of knowledge as a result of an anthropocentric perspective that renders them invisible or reduces them to ciphers for human meanings. It combines ethology and cinematic realism to craft a reparative, non-anthropocentric way of looking that is able to accommodate the plenitude of animals and their traces, and grant them the ontological heft required to exert productive traction in the visual field. Chapter 2 identifies an octopuss encounter with a digital camera and its chance cinematic inscription as part of a larger phenomenon of accidental animal videos. Because non-humans are the catalysts for their production, these videos offer welcome realist counterpoints to traditional wildlife imagery, and affirm cinemas ability to intercede non-anthropocentrically between humans and the world. Realism is essential to cinematic communication, and that realism is ultimately an achievement of non-human intervention. Chapter 3 investigates how an Internet hoax about a non-human ape playing with an iPad in a zoo led to the development of Apps for Apes, a real life enrichment project that pairs captive orangutans with iPads. It contextualizes and criticizes this projects discursive underpinnings but argues that the contingencies that transpire at the touchscreen interface shift our understanding of communication away from sharing minds and toward respecting immanence and accommodating difference. Finally, Chapter 4 examines a publicity stunt wherein a digital data-carrying homing pigeon races against the Internet to meet a computer. Rather than a competition, this is a continuation of a longstanding collaboration between the carrier pigeon and the infrastructure of modern communications. The carrier pigeon is not external but rather endemic to our understanding of communication as a material process that requires movement and coordination to make connections

Scientific Reasoning in Science Education: From Global Measures to Fine-Grained Descriptions of Students’ Competencies

This book is a reprint of the Special Issue "Scientific Reasoning in Science Education: From Global Measures to Fine-Grained Descriptions of Students’ Competencies" published in the journal Education Sciences. It compiles all manuscripts of the special issue