Abductive Knowledge Induction from Raw Data

1 INTRODUCTION . Inductive bias , background knowledge , is an essential component in machine learning . Despite the success of data-driven end-to-end deep learning in many traditional machine learning tasks , it has been shown that incorporating domain knowledge is still necessary for some complex learning problems ( Dhingra et al. , 2020 ; Grover et al. , 2019 ; Trask et al. , 2018 ) . In order to leverage complex domain knowledge that is discrete and relational , end-to-end learning systems need to represent it with a differentiable module that can be embedded in the deep learning context . For example , graph neural networks ( GNN ) use relational graphs as an external knowledge base ( Zhou et al. , 2018 ) ; some works even considers more specific domain knowledge such as differentiable primitive programs ( Gaunt et al. , 2017 ) . However , the design of these modules is usually ad hoc . Sometimes , it is not easy to find an appropriate approximation that is suited for single-model based end-to-end learning ( Glasmachers , 2017 ; Garcez et al. , 2019 ) . Therefore , many researchers propose to break the end-to-end learning pipeline apart and build a hybrid model that consists of smaller modules where each of them only accounts for one specific function ( Glasmachers , 2017 ) . A representative branch in this line of research is Neural-Symbolic ( NeSy ) AI ( De Raedt et al. , 2020 ; Garcez et al. , 2019 ) aiming to bridge System 1 and System 2 AI ( Kahneman , 2011 ; Bengio , 2017 ) , i.e. , neural-network-based machine learning and symbolicbased relational inference . In NeSy models , the neural network extracts high-level symbols from noisy raw data and the symbolic model performs relational inference over the extracted symbols . However , the non-differentiable interface between neural and symbolic systems ( i.e. , the facts extracted from raw data and their truth values ) leads to high computational complexity in learning . For example , due to the lack of direct supervision to the neural network and reliable inputs to the symbolic model , some works have to use Markov Chain Monte Carlo ( MCMC ) sampling or zeroorder optimisation to train the model ( Li et al. , 2020 ; Dai et al. , 2019 ) , which could be inefficient in practice . Consequently , almost all hybrid models assume the existence of a very strong predefined domain knowledge base and focus on using it to train neural networks . It limits the expressive power of the hybrid-structured model and sacrifices many benefits of symbolic learning ( e.g. , predicate invention , learning recursive theories , and re-using learned models as background knowledge ) . In this paper , we integrate neural networks with Inductive Logic Programming ( ILP ) ( Muggleton & de Raedt , 1994 ) —a general framework for symbolic machine learning—to enable first-order logic theory induction from raw data . More specifically , we present Abductive Meta-Interpretive Learning ( Metaabd ) which extends the Abductive Learning ( ABL ) framework ( Dai et al. , 2019 ; Zhou , 2019 ) by combining logical induction and abduction ( Flach et al. , 2000 ) with neural networks in MetaInterpretive Learning ( MIL ) ( Muggleton et al. , 2014 ) . MetaAbd employs neural networks to extract probabilistic logic facts from raw data , and induces an abductive logic program ( Kakas et al. , 1992 ) that can efficiently infer possible truth values of the probabilistic facts to train the neural model . On the one hand , the abductive logic program learned by MetaAbd can largely prune the search space of the truth value assignments to the logical facts extracted by an under-trained neural model . On the other hand , the extracted probabilistic facts , although noisy , provide a distribution on the possible worlds ( Nilsson , 1986 ) reflecting the raw data distribution , which helps logical induction to identify the most probable hypothesis . The two systems inMetaAbd are integrated by a probabilistic model that can be optimised with Expectation Maximisation ( EM ) . To the best of our knowledge , Metaabd is the first system that can simultaneously ( 1 ) train neural models , ( 2 ) learn recursive logic theories and ( 3 ) perform predicate invention from domains with sub-symbolic representation . In the experiments we compare MetaAbd to the compared state-ofthe-art end-to-end deep learning models on two complex learning tasks . The results show that , given the same amount of background knowledge , Metaabd outperforms the end-to-end models significantly in terms of predictive accuracy and data efficiency , and learns human interpretable models that could be re-used in subsequent learning tasks . 2 RELATED WORK . Solving “ System 2 ” problems require the ability of relational and logical reasoning instead of “ intuitive and unconscious thinking ” ( Kahneman , 2011 ; Bengio , 2017 ) . Due to the complexity of this type of tasks , many researchers have tried to embed intricate background knowledge in end-to-end deep learning models . For example , Trask et al . ( 2018 ) propose the differentiable Neural Arithmetic Logic Units ( NALU ) to model basic arithmetic functions ( e.g. , addition , multiplication , etc . ) in neural cells ; Grover et al . ( 2019 ) encode permutation operators with a stochastic matrix and present a continuous and differentiable approximation to the sort operation ; Wang et al . ( 2019 ) introduce a differentiable SAT solver to enable gradient-based constraint solving . However , most of these specially designed differentiable modules are ad hoc approximations to the original inference mechanisms , which can not represent the inductive bias in a general form such as formal languages . In order to directly exploit the complex background knowledge expressed by formal languages , Statistical Relational ( StarAI ) and Neural Symbolic ( NeSy ) AI ( De Raedt et al. , 2020 ; Garcez et al. , 2019 ) are proposed . Some works try to approximate logical inference with continuous functions or use probabilistic logical inference to enable the end-to-end training ( Cohen et al. , 2020 ; Manhaeve et al. , 2018 ; Donadello et al. , 2017 ) ; others try to combine neural networks and pure symbolic reasoning by performing a combinatorial search over the truth values of the output facts of the neural model ( Li et al. , 2020 ; Dai et al. , 2019 ) . Because of the highly complex statistical relational inference and combinatorial search , it is difficult for them to learn first-order logic theories . Therefore , most existing StarAI and NeSy systems focus on utilising a pre-defined symbolic knowledge base to help the parameter learning of the neural model and probabilistic model . One way to learn symbolic models is to use Inductive Logic Programming ( Muggleton & de Raedt , 1994 ) . Some early work on combining logical abduction and induction can learn logic theories even when input data is incomplete ( Flach et al. , 2000 ) . Recently , ∂ILP was proposed for learning first-order logic theories from noisy data ( Evans & Grefenstette , 2018 ) . However , these works are designed for learning from domains . Otherwise , they need to use a fully trained neural model to extract primitive facts from raw data before symbolic learning . Machine apperception ( Evans et al. , 2019 ) unifies reasoning and perception by combining logical inference and binary neural networks in Answer Set Programming , in which logic hypotheses and parameters of neural networks are all represented by logical groundings , making the system hard to optimise . For problems involving noisy inputs like MNIST images , it still requires a fully pre-trained neural net for pre-processing . Different to the previous work , our presented Abductive Meta-Interpretive Learning ( MetaAbd ) aims to combine symbolic and sub-symbolic learning in a mutually beneficial way , where the induced abductive logic program prunes the combinatorial search of the unknown labels for training the neural model ; and the probabilistic facts output by the neural model provide a distribution on the possible worlds of the symbolic domain to help logic theory induction . 3 ABDUCTIVE META-INTERPRETIVE LEARNING . 3.1 PROBLEM FORMULATION . A typical hybrid model bridging sub-symbolic and symbolic learning contains two major parts : a perception model and a reasoning model ( Dai et al. , 2019 ) . The perception model maps raw inputs x ∈ X—which are usually noisy and represented by sub-symbolic features—to some primitive symbols z ∈ Z , such as digits , objects , ground logical expressions , etc . The reasoning model takes the interpreted z as input and deduces the final output y ∈ Y according to a symbolic background knowledge base B . Because the primitive symbols z are uncertain and not observable from both training data and the background knowledge , we have named them as pseudo-labels of x . The perception model is parameterised with θ and outputs the conditional probability Pθ ( z|x ) = P ( z|x , θ ) ; the reasoning modelH ∈ H is a set of first-order logical clauses such thatB∪H∪z |= y , where “ |= ” means “ logically entails ” . Our target is to learn θ and H simultaneously from training data D = { 〈xi , yi〉 } ni=1 . For example , if we have one example with x = [ , , ] and y = 6 , given background knowledge about adding two numbers , the hybrid model should learn a perception model that recognises z = [ 1 , 2 , 3 ] and induce a program to add all numbers in z recursively . Assuming that D is an i.i.d . sample from the underlying distribution of ( x , y ) , the objective of our learning problem can be represented as follows : ( H∗ , θ∗ ) = argmax H , θ ∏ 〈x , y〉∈D ∑ z∈Z P ( y , z|B , x , H , θ ) , ( 1 ) where z is a hidden variable in this model . Theoretically , this problem can be solved by Expectation Maximisation ( EM ) algorithm . However , even if we can obtain the expectation of the hidden variable z and efficiently estimate the perception model ’ s parameter θ with numerical optimisation , the hypothesis H , which is a first-order logic theory , is still difficult to be optimised together with θ . We propose to solve this problem by treating H like z as an extra hidden variable , which gives us : θ∗ = argmax θ ∏ 〈x , y〉∈D ∑ H∈H ∑ z∈Z P ( y , H , z|B , x , θ ) ( 2 ) The hybrid-model learning problem in Equation 1 can be split into two EM steps : ( 1 ) Expectation : obtain the expected value of H and z by sampling them in their discrete hypothesis space from ( H , z ) ∼ P ( H , z|B , x , y , θ ) ; ( 2 ) Maximisation : estimate θ by maximising the likelihood of training data with efficient numerical optimisation approaches such as gradient descent . As one can imagine , the main challenge is to estimate the expectation of the hidden variables H ∪ z , i.e. , we need to search for the most probable H and z given the θ learned in the previous iteration . Challenges This search problem is nontrivial . Sampling the values of hidden variable z results in a search space growing exponentially with the number of training examples . Even when B is sound and complete , existing hybrid models that do not learn first-order hypotheses still have to use Zero-Order Optimisation ( ZOOpt ) or Markov Chain Monte Carlo ( MCMC ) sampling to estimate the expectation of z ( Dai et al. , 2019 ; Li et al. , 2020 ) , which could be quite inefficient in practice . Furthermore , the size and structure of hypothesis space H of first-order logic programs makes the search problem even more complicated . For example , given x = [ , , ] and y = 6 , when the perception model is accurate enough to output the most probable z = [ 1 , 2 , 3 ] , we have at least two choices for H : cumulative sum or cumulative product . When the perception model is under-trained and outputs the most probable z = [ 2 , 2 , 3 ] , then H could be a program that only multiplies the last two digits . Hence , H and z are entangled and can not be treated independently .

Summary: In this paper, the author proposes Meta_abd which is a hybrid model that learns a deep recognition model and FOL rules the same time. The goal of this work is to learn FOL rules from raw data such as digits presented in image patches in an end-to-end fashion. The model is evaluated with 3 induction benchmarks associated to the MINST digit dataset.