412 research outputs found
Modular Design Patterns for Hybrid Learning and Reasoning Systems: a taxonomy, patterns and use cases
The unification of statistical (data-driven) and symbolic (knowledge-driven)
methods is widely recognised as one of the key challenges of modern AI. Recent
years have seen large number of publications on such hybrid neuro-symbolic AI
systems. That rapidly growing literature is highly diverse and mostly
empirical, and is lacking a unifying view of the large variety of these hybrid
systems. In this paper we analyse a large body of recent literature and we
propose a set of modular design patterns for such hybrid, neuro-symbolic
systems. We are able to describe the architecture of a very large number of
hybrid systems by composing only a small set of elementary patterns as building
blocks.
The main contributions of this paper are: 1) a taxonomically organised
vocabulary to describe both processes and data structures used in hybrid
systems; 2) a set of 15+ design patterns for hybrid AI systems, organised in a
set of elementary patterns and a set of compositional patterns; 3) an
application of these design patterns in two realistic use-cases for hybrid AI
systems. Our patterns reveal similarities between systems that were not
recognised until now. Finally, our design patterns extend and refine Kautz'
earlier attempt at categorising neuro-symbolic architectures.Comment: 20 pages, 22 figures, accepted for publication in the International
Journal of Applied Intelligenc
Analyzing Differentiable Fuzzy Implications
Combining symbolic and neural approaches has gained considerable attention in
the AI community, as it is often argued that the strengths and weaknesses of
these approaches are complementary. One such trend in the literature are weakly
supervised learning techniques that employ operators from fuzzy logics. In
particular, they use prior background knowledge described in such logics to
help the training of a neural network from unlabeled and noisy data. By
interpreting logical symbols using neural networks (or grounding them), this
background knowledge can be added to regular loss functions, hence making
reasoning a part of learning.
In this paper, we investigate how implications from the fuzzy logic
literature behave in a differentiable setting. In such a setting, we analyze
the differences between the formal properties of these fuzzy implications. It
turns out that various fuzzy implications, including some of the most
well-known, are highly unsuitable for use in a differentiable learning setting.
A further finding shows a strong imbalance between gradients driven by the
antecedent and the consequent of the implication. Furthermore, we introduce a
new family of fuzzy implications (called sigmoidal implications) to tackle this
phenomenon. Finally, we empirically show that it is possible to use
Differentiable Fuzzy Logics for semi-supervised learning, and show that
sigmoidal implications outperform other choices of fuzzy implications.Comment: 10 pages, 10 figures, accepted to 17th International Conference on
Principles of Knowledge Representation and Reasoning (KR 2020). arXiv admin
note: substantial text overlap with arXiv:2002.0610
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Towards Informed Exploration for Deep Reinforcement Learning
In this thesis, we discuss various techniques for improving exploration for deep reinforcement learning. We begin with a brief review of reinforcement learning (RL) and the fundamental v.s. exploitation trade-off. Then we review how deep RL has improved upon classical and summarize six categories of the latest exploration methods for deep RL, in the order increasing usage of prior information. We then explore representative works in three categories discuss their strengths and weaknesses. The first category, represented by Soft Q-learning, uses regularization to encourage exploration. The second category, represented by count-based via hashing, maps states to hash codes for counting and assigns higher exploration to less-encountered states. The third category utilizes hierarchy and is represented by modular architecture for RL agents to play StarCraft II. Finally, we conclude that exploration by prior knowledge is a promising research direction and suggest topics of potentially impact
On Differentiable Interpreters
Neural networks have transformed the fields of Machine Learning and Artificial Intelligence with the ability to model complex features and behaviours from raw data. They quickly became instrumental models, achieving numerous state-of-the-art performances across many tasks and domains. Yet the successes of these models often rely on large amounts of data. When data is scarce, resourceful ways of using background knowledge often help. However, though different types of background knowledge can be used to bias the model, it is not clear how one can use algorithmic knowledge to that extent. In this thesis, we present differentiable interpreters as an effective framework for utilising algorithmic background knowledge as architectural inductive biases of neural networks. By continuously approximating discrete elements of traditional program interpreters, we create differentiable interpreters that, due to the continuous nature of their execution, are amenable to optimisation with gradient descent methods. This enables us to write code mixed with parametric functions, where the code strongly biases the behaviour of the model while enabling the training of parameters and/or input representations from data. We investigate two such differentiable interpreters and their use cases in this thesis. First, we present a detailed construction of ∂4, a differentiable interpreter for the programming language FORTH. We demonstrate the ability of ∂4 to strongly bias neural models with incomplete programs of variable complexity while learning missing pieces of the program with parametrised neural networks. Such models can learn to solve tasks and strongly generalise to out-of-distribution data from small datasets. Second, we present greedy Neural Theorem Provers (gNTPs), a significant improvement of a differentiable Datalog interpreter NTP. gNTPs ameliorate the large computational cost of recursive differentiable interpretation, achieving drastic time and memory speedups while introducing soft reasoning over logic knowledge and natural language
On the Evolution of Knowledge Graphs: A Survey and Perspective
Knowledge graphs (KGs) are structured representations of diversified
knowledge. They are widely used in various intelligent applications. In this
article, we provide a comprehensive survey on the evolution of various types of
knowledge graphs (i.e., static KGs, dynamic KGs, temporal KGs, and event KGs)
and techniques for knowledge extraction and reasoning. Furthermore, we
introduce the practical applications of different types of KGs, including a
case study in financial analysis. Finally, we propose our perspective on the
future directions of knowledge engineering, including the potential of
combining the power of knowledge graphs and large language models (LLMs), and
the evolution of knowledge extraction, reasoning, and representation
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