7,188 research outputs found
Abductive knowledge induction from raw data
For many reasoning-heavy tasks with raw inputs, it is challenging to design an appropriate end-to-end pipeline to formulate the problem-solving process. Some modern AI systems, e.g., Neuro-Symbolic Learning, divide the pipeline into sub-symbolic perception and symbolic reasoning, trying to utilise data-driven machine learning and knowledge-driven problem-solving simultaneously. However, these systems suffer from the exponential computational complexity caused by the interface between the two components, where the sub-symbolic learning model lacks direct supervision, and the symbolic model lacks accurate input facts. Hence, they usually focus on learning the sub-symbolic model with a complete symbolic knowledge base while avoiding a crucial problem: where does the knowledge come from? In this paper, we present Abductive Meta-Interpretive Learning (MetaAbd) that unites abduction and induction to learn neural networks and logic theories jointly from raw data. Experimental results demonstrate that MetaAbd not only outperforms the compared systems in predictive accuracy and data efficiency but also induces logic programs that can be re-used as background knowledge in subsequent learning tasks. To the best of our knowledge, MetaAbd is the first system that can jointly learn neural networks from scratch and induce recursive first-order logic theories with predicate invention
Inductive logic programming at 30
Inductive logic programming (ILP) is a form of logic-based machine learning.
The goal of ILP is to induce a hypothesis (a logic program) that generalises
given training examples and background knowledge. As ILP turns 30, we survey
recent work in the field. In this survey, we focus on (i) new meta-level search
methods, (ii) techniques for learning recursive programs that generalise from
few examples, (iii) new approaches for predicate invention, and (iv) the use of
different technologies, notably answer set programming and neural networks. We
conclude by discussing some of the current limitations of ILP and discuss
directions for future research.Comment: Extension of IJCAI20 survey paper. arXiv admin note: substantial text
overlap with arXiv:2002.11002, arXiv:2008.0791
Inductive logic programming at 30: a new introduction
Inductive logic programming (ILP) is a form of machine learning. The goal of
ILP is to induce a hypothesis (a set of logical rules) that generalises
training examples. As ILP turns 30, we provide a new introduction to the field.
We introduce the necessary logical notation and the main learning settings;
describe the building blocks of an ILP system; compare several systems on
several dimensions; describe four systems (Aleph, TILDE, ASPAL, and Metagol);
highlight key application areas; and, finally, summarise current limitations
and directions for future research.Comment: Paper under revie
Constructive approaches to Program Induction
Search is a key technique in artificial intelligence, machine learning and Program Induction. No
matter how efficient a search procedure, there exist spaces that are too large to search effectively
and they include the search space of programs. In this dissertation we show that in the context
of logic-program induction (Inductive Logic Programming, or ILP) it is not necessary to search
for a correct program, because if one exists, there also exists a unique object that is the most
general correct program, and that can be constructed directly, without a search, in polynomial
time and from a polynomial number of examples. The existence of this unique object, that we
term the Top Program because of its maximal generality, does not so much solve the problem
of searching a large program search space, as it completely sidesteps it, thus improving the
efficiency of the learning task by orders of magnitude commensurate with the complexity of a
program space search.
The existence of a unique Top Program and the ability to construct it given finite resources
relies on the imposition, on the language of hypotheses, from which programs are constructed,
of a strong inductive bias with relevance to the learning task. In common practice, in machine
learning, Program Induction and ILP, such relevant inductive bias is selected, or created,
manually, by the human user of a learning system, with intuition or knowledge of the problem
domain, and in the form of various kinds of program templates. In this dissertation we show
that by abandoning the reliance on such extra-logical devices as program templates, and instead
defining inductive bias exclusively as First- and Higher-Order Logic formulae, it is possible to
learn inductive bias itself from examples, automatically, and efficiently, by Higher-Order Top
Program construction.
In Chapter 4 we describe the Top Program in the context of the Meta-Interpretive Learning
approach to ILP (MIL) and describe an algorithm for its construction, the Top Program
Construction algorithm (TPC). We prove the efficiency and accuracy of TPC and describe
its implementation in a new MIL system called Louise. We support theoretical results with
experiments comparing Louise to the state-of-the-art, search-based MIL system, Metagol, and
find that Louise improves Metagolās efficiency and accuracy. In Chapter 5 we re-frame MIL as
specialisation of metarules, Second-Order clauses used as inductive bias in MIL, and prove that
problem-specific metarules can be derived by specialisation of maximally general metarules, by
MIL. We describe a sub-system of Louise, called TOIL, that learns new metarules by MIL and
demonstrate empirically that the metarules learned by TOIL match those selected manually,
while maintaining the accuracy and efficiency of learning.
iOpen Acces
Vision-language models boost food composition compilation
Nutrition information plays a pillar role in clinical dietary practice,
precision nutrition, and food industry. Currently, food composition compilation
serves as a standard paradigm to estimate food nutrition information according
to food ingredient information. However, within this paradigm, conventional
approaches are laborious and highly dependent on the experience of data
managers, they cannot keep pace with the dynamic consumer market and resulting
in lagging and missing nutrition data and earlier machine learning methods
unable to fully understand food ingredient statement information or ignored the
characteristic of food image. To this end, we developed a novel vision-language
AI model, UMDFood-VL, using front-of-package labeling and product images to
accurately estimate food composition profiles. In order to drive such large
model training, we established UMDFood-90k, the most comprehensive multimodal
food database to date. The UMDFood-VL model significantly outperformed
convolutional neural networks (CNNs) and recurrent neural networks (RNNs) on a
variety of nutrition value estimations. For instance, we achieved macro-AUCROC
up to 0.921 for fat value estimation, which satisfied the practice requirement
of food composition compilation. This performance shed the light to generalize
to other food and nutrition-related data compilation and catalyzed the
evolution of other food applications.Comment: 31 pages, 5 figure
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