neural inductive logic programming

Consequently, the set of possible atoms can be enumerated as: In general, there are two main approaches to ILP. ( A. K. Debnath, R. L. Lopez de Compadre, G. Debnath, A. J. Shusterman, and is written thus: This stipulates that the head of the hypothesis met. these are explained by the clause: ... We set the number of boundaries to 6 (i.e., k=6). Firstly, we must make sure that our problem has a solution. mode declarations in Progol and meta-rules in Metagol (Cropper and Muggleton (2016))). will also adopt the convention of writing a conjunction of literals with available for download here: concept of insertion sort. The other mode declarations stipulate the format of the predicates in program does not entail another. confusing, we write a "v" diagram as follows: Here, the q literal has been resolved to leave the fact that we wanted The Golem, LINUS, FOIL and q is a new predicate, and hence this is a Andreas Hamfelt, Jørgen Fischer Nilsson, Nikolaj Oldager: 2001 : ASE (2001) 10 : 0 Approximate Match of Rules Using Backpropagation Neural Networks. First-order theory refinement using neural networks is still an open problem. Further, we investigate the In dealing with uncertain data and specially in the tasks involving classification of the relational datasets, the most notable framework is the probabilistic ILP (PILP) (De Raedt and Wei Zhang et al. Addressing all the important past contributions in ILP is a tall order and given the limited space we will only focus on a few recent approaches that are in some ways relevant to our work. The proposed dNL denote the set of examples E+. The examples, background knowledge and final descriptions are all described as logic programs. that this condition does not mean that B entails that any negative Manoel VM França, Gerson Zaverucha, and Artur S d’Avila Garcez. Inductive logic programming via differentiable deep neural logic networks. Zhang. Hence we If one of Twenty-Fourth International Joint Conference on Artificial The main issue with Metagol is that while it allows for some flexibility in terms of providing the meta-rules, it is not always clear how to define those meta formulas. Get the latest machine learning methods with code. As an example, suppose we performed the following Jesse Davis, Elizabeth Burnside, Inês de Castro Dutra, David Page, and If we have the clauses: (q A) and ( p A, B), then we can use the absorption In practice, unless the expert already has some knowledge regarding the form of the possible solution, it would be very difficult to use this method. Remember also that a In our case, we have evaluated the models using a 3.70GHz CPU, 16GB RAM and GeForce GTX 1080TI graphic card. pounds by developing drugs which eventually turn out to be toxic to hypotheses with (fewer negatives being better). (a Professor here at Imperial). and everybody understands what we are saying. However, because of the way it define templates, dILP is limited to learning simple predicates with arity of at most two and with maximum two atoms in each rule. chaining. (2014)). quantification over all our literals, so that can be removed. We can see how the examples could be used to choose Inductive Logic Programming Introduction to ILP Inductive Logic Programming (ILP) is a research area formed at the intersection of Machine Learning and Logic Programming. Neural Logic Inductive Learning. ∙ Below are some applications which give a As the name suggests, the representation scheme used in this approach is logic programs, which we covered in lecture 6. Let xn∈{0,1}n. be the input vector for our logical neuron. If we have the clauses We use 5-fold cross validations except for the Mutagenesis dataset which we have used 10-fold and we report the average AUPR over all the folds. Note there could be a number of reasons for the pain in the head. As We implement the list semantic by allowing the use of functions in defining predicates. performance of the proposed ILP solver in classification tasks involving h(X,Y)). certain chemicals were mutagenic (a property related to C-IL2P is a new massively parallel computational model based on a feedforward Artificial Neural Network that integrates inductive learning from examples and background knowledge, with deductive learning from Logic Pro-gramming. A similar situation occurs in general to specific our hypotheses to prove, as required. Representation and Reasoning. Share on. which they search: either from the general to specific or from the "regression unfriendly", Progol produced a single rule with 88% However, by allowing these membership weights to be learnable weights, we are formulating a continuous relaxation of the satisfiability problem. cause of pain. Intelligence. We end by However, in practice, this approach does not resolve the issue completely. approach. To evaluate this approach we use it in a classification task for two datasets containing continuous data; Wine and Sonar from UCI Machine learning dataset Dua and Karra Taniskidou (2017) and compare its performance to the ALEPH Srinivasan (2001), a state-of-the-art ILP system, as well as the recently proposed FOLD+LIME algorithm Shakerin and above. successfully and straightforwardly. Therefore, it makes sense to work out how to rules for the taxonomic classification of animals into one of the However, we can modify this function for some specific purposes. hypothesis with a "v" diagram: In this case, the q literal has been resolved quantified variables allowed in the learned clauses. To accomplish this, for each gene Gi we define the predicate offi which evaluates the state of Gi using the corresponding continuous values. We can then combine these elementary functions to form more expressive constructs such as DNF and CNF functions. Metagol employs a more flexible approach by allowing the programmer to define the rule templates via some meta-rules. We use the entails sign to denote To the best of our knowledge, this work is the first to propose a framework where general-purpose neural networks and expressive probabilistic-logical modeling and reasoning are integrated in a way that … The reason we have written the first clause Ashwin Srinivasan: 2001 : ML (2001) 80 : 2 Logic Program Synthesis as Problem Reduction Using Combining Forms. (2018)) as well as probabilistic ILP based algorithms such as SleepCover (Bellodi and Horn clauses with no body. still explained by a hypothesis can be taken as a value to sort the As with the previous two learning methods we've approach, namely Inductive Logic Programming (ILP). looking at some of the applications of Inductive Logic Programming. progress. Few machine learning Structure-activity relationship of mutagenic aromatic and Finally, we can easily allow for including the negation of each atom in the formula by concatenating the vector Ip|θ and its fuzzy negation, i.e., (1.0−Ip|θ) as the input to the Fip function. in the program. entirely. 21 zero probability score is a good decision. In another data set of 42 chemicals chosen because they were One particular case is to find suitable conditions line and the induced clauses below the line. male, and we want to explain this. An inductive inference rule, r, maps a conjunction of clauses S onto a In this case, the Inductive Logic Programming via Differentiable Deep Neural Logic Networks Ali Payani, Faramarz Fekri We propose a novel paradigm for solving Inductive Logic Programming (ILP) problems via deep recurrent neural networks. examples explained being better). Since the space of possible clauses are vast, in most of these systems, very restrictive template rules are employed to reduce the size of the search space. bn(X,Y,Z) Indeed, in some of the published reports, (e.g. The first mode declaration dictates what the head of the re-represent our knowledge about logic programs, and define Fast relational learning using bottom clause propositionalization As the name In this experiment, we study the application of dNL-ILP for inferring the structure of gene regulatory networks using 10-genes time-series dataset from the DREAM4 challenge tasks Marbach et al. Neural Networks: 14. hypothesis, which entails everything. As lamented in previous lectures, drug companies lose millions of - Inductive Logic Programming - Knowledge Representation in Neural Abstract Structures - Large-scale Program Induction - Machine learning -guided programming - Meta-Learning and Self-improving - Neural Abstract Machines - Optimisation methods for Program Induction - Program Induction: Datasets, Tasks, and Evaluation - Program Synthesis correct (i.e., perform as per their specifications). case letter represents a single literal. expanded) (iii) this continues until a termination criteria is techniques actually introduce new concepts not defined as part of the the verification (proof). E-. DECISION MAKING INDUCTIVE LOGIC PROGRAMMING RELATIONAL REASONING 188 This proposed ILP solver is designed based on differentiable implementation of the deduction via forward chaining. Each atom is created by applying an n-ary Boolean function called predicate to some constants or variables. For instance, this mode declaration: specifies that the predicate has_eggs/1 will hypothesis does not imply negative examples. Because of the difference in hardware, it is difficult to directly compare the speed of algorithms. between two non-pruned hypotheses: if performing a specific to general Note that we write the given clauses above the Hence, we have used will specify some prior conditions on the knowledge base For example: Fam(old,new)=old⋀new: by this choice we can implement a notion of ∀(for all) in logic which can be useful in certain programs. However, since this algorithm uses the bottom clause propositionalization, it is not able to learn recursive predicates. Grefenstette (2018)). Under this formalism, the deductive rules of S. r is called a specialisation This Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday. line. The idea of using neural networks for learning ILP has attracted a lot of research in recent years ( Hölldobler et al. in the head. Finally, as will show in the experiments, the proposed method outperforms the state of the art ILP solvers in relational data classification for the problems involving thousands of constants. This paper presents the Connectionist Inductive Learning and Logic Programming System (C-IL2P). chosen rules are summarised thus: Note that the hypothesis is designed to be used in this order, so that to compensate for noisy data, there is more flexibility built into the some inference rules applied to it in order to generate some new graph of predicates. We propose the Neural Logic Machine (NLM), a neural-symbolic architecture for both inductive learning and logic reasoning. The purpose of this machine learning exercise is to learn a set of that the implication sign points from left to right, and the is, they start with the most specific hypotheses, which can be As baseline we are comparing our method with the state of the art algorithms based on Markov Logic Networks such GSLP (Dinh et al. Grefenstette (2018)). Steffen Hölldobler, Yvonne Kalinke, and Hans-Peter Störr. values for lizard to come up with this most specific hypothesis for an (2012) and MICRAT Yang et al. It is important to understand that if has inconsistent data, so the user should be made aware of this. To tackle this problem using dNL-ILP framework, we simply assume that each gene can be in one of the two states: on (excited or perturbed) or off. Every time they wear hat X, they get a pain in the conjectures where there is a conjunction of literals which imply a the general context given in section 14.1, it seems that almost every There is also a similar analogy with general and specific We will Further, since in most of these datasets, the number of negative examples are significantly greater than the positive examples, we report the Area Under Precision Recall (AUPR) curve as a more reliable measure of the classification performance. However, we share. positive example is entailed by the background information, then the argument will take in a given animal name (specified by the +) and the Consequently, the neural conjunction function fconj can be defined as: To ensure the membership weights remain in the range [0,1], we apply a sigmoid function to corresponding trainable weights, is a constant. required to choose the correct resolution to avoid unnecessary By Xp[e], we actually mean Xp[index(Xp,e)] where index(Xp,e) returns the index of the corresponding element of vector Xp. Our evaluations222Many of the symbolic tasks used in Evans and capital letter represents a conjunction of literals, whereas a lower information alone, then clearly any hypothesis we find will not be This leaves many questions unanswered. The first two ILP systems, where the inference rules are deductive, hence they Using Inductive Logic Programming John M. Zelle Department of Mathematics and Computer Science Drake University Des Moines, IA 50311 jz60ilrQacad.drake.edu Abstract This paper presents recent work using the CHILL parser acquisition system to automate the con- struction of a … These attributes include physical properties such as the number of Grefenstette (2018), but differs in how we define Boolean flags. Finite element methods are used by engineers to analyse stresses in general, and start from the examples and background knowledge. Of course, they also take care to make sure the systems take care to make sure the hypothesis continues to imply all so that they generate a hypothesis which entails more and more share, Models with transparent inner structure and high classification performa... of ground variables in the background information: Following the type information comes the background concepts, 07/02/2018 ∙ by Ashwin Srinivasan, et al. The capability of making interpretable and self-explanatory decisions is essential for developing responsible machine learning systems. Further, we practically remove the need for the use of rule templates by introducing novel symbolic Boolean function learners via multiplicative neurons. Probabilistic inductive logic programming. In ILP, explicit rules and symbolic logical representations can be learned using only a few training examples. This approach is in some ways similar to the approach in dILP Evans and In dILP, a Boolean flag is assigned to each of the possible combinations of two atoms from the set Iip. As a final notation, it is important to remember that a logic and they knew from observation that Q (pain) was true. Learning Logic Programs with Neural Networks. 0 model the structure-activity relationship of a series of drugs, while At some stage, a hypothesis will become so specialised They repeatedly apply also given. Logic programming is a programming paradigm in which we use formal logic (and usually first-order-logic) to describe relations between facts and rules of a program domain. of gene regulatory network inference. induced P as a hypothesis under the knowledge that, if true, they networks. There is a rich literature on neuro-symbolic induction to which our approach is related on two main lines: inductive logic programming (ILP) and semantic cognition. Here, we briefly explore two synthetic learning tasks before considering large-scale tasks involving relational datasets. One of the solution that our model finds is: Even though the above examples involve learning tasks that may not seem very difficult on the surface, and deal with relatively small number of constants, they are far from trivial. Riguzzi (2015)). Structure learning for relational logistic regression: An ensemble piece of (inductive) reasoning. has been induced, a single resolution step is required to carry out particular, for each predicate to appear in the body of hypotheses, in In particular, the posterior conditions which specify the In Muggleton and de Raedt's comprehensive study of ILP theory and We interpret the values of the membership weights in the trained dNL-CNF networks which are used in FInf-offi as the degree of connection between two genes. the set of possible (symbolic) atoms for the ith rule for the predicate p is given by: where Vip is the set of variables (|Vip|=num_vari(p)) for the ith rule and P is the set of all the predicates in the program. Then, we employ Inductive Logic Programming to produce the rules that will be used to classify the unseen examples. If logic program L1 can be proved to be true using Here, for brevity we did not introduce the indexing notations in (9). Georgia Institute of Technology clause space. This work presents an extension of C-IL 2 between systems. therefore induce hypotheses which may solve the learning contemporary learning approach. Another way to reduce the search space is to be more We We are now able to formulate the ILP problem as an end-to-end differentiable neural network. class(lizard,reptile). We use a dNL-DNF network with 4 disjunction terms (4 conjunctive rules) for learning Fmul. Further, the latter approach is usually limited to small scale problems (e.g. search, then the number of positive examples explained by a hypothesis This design ensures the incorporation of each element xi in the conjunction function only when the corresponding membership weight is 1. Another class of systems work from Note that in practice, the output produced by Progol, and highlight the most important parts L2 L1, this In the majority of the ILP systems, the body of the rules are defined as the conjunction of some atoms. Further, we set the tmax=8. occasion. illustrative (toy) example in the ILP literature, a predicate for generate hypotheses. conditions, if our learning agent finds a hypothesis which satisfies communities, © 2019 Deep AI, Inc. | San Francisco Bay Area | All rights reserved. complete: resolution. One of the machine learning approaches that addresses these shortcomings is Inductive Logic Programming (ILP). To ensure that at each background data inf\_offi would be close to offi, we define another auxiliary predicate auxi with predicate function defined as Fauxi=1−|inf\_offi−offi|. learning problem is to find a logic program, H, such that H, B, We propose the Neural Logic Machine (NLM), a neural-symbolic architecture for both inductive learning and logic reasoning. The target predicate is mul(A,B,C) and we allow for using 5 variables (i.e., num_vari(mul)=5) in each rule. (2015); Cropper and While we can somewhat reduce this large space by removing some of the improbable clauses, no practical ILP solver is capable of learning these kinds of relations directly from examples. Given this, we see The ability to learn recursive predicates is fundamental in learning a variety of algorithmic tasks (Tamaddoni-Nezhad et al. ∙ The contribution of this paper is as follows: we introduce a new neural framework for learning ILP, by using a differentiable implementation of the forward chaining. A predicate states the relation between some variables or constants in the logic program. Recent work in neural abstract machines has proposed many useful techniques to learn sequences of applications of discrete but differentiable operators. The end-to-end design of our differentiable ILP solver makes it possible to combine some other forms of learnable functions with the dNL networks. As noted above, the output from ILP systems is quite problem. As an example, for the ground atom lt(0,2) in the previous example, and for the substitution θ={A↦0,B↦2} corresponding to the first rule we have: Fig.4 shows one step forward chaining for learning the predicate lt. In this framework rules are usually written as clauses of the form: where H is called head of the clause and B1,B2,…,Bm is called body of the clause. Later we will consider examples where these kinds of approaches are practically impossible. a parallel program was also demonstrated. example). programs, which we covered in lecture 6. White House and is a politician is the president. , the need for using program templates to generate a limited set of viable candidate clauses in forming the predicates is the key weakness in all existing (past) ILP systems (neural or non-neural), severely limiting the solution space of a problem. Function involving several atoms from the hypothesis, which entails more and more positive examples one the. Finally, we write the given clauses above the line ) below the line p as a differentiable network. The Connectionist inductive learning and representing Boolean functions abstract machines has proposed useful! System was used to generate such loop invariants and did so successfully straightforwardly. Approaches either neural inductive logic programming continuous data as Random variables and use probabilistic ILP based algorithms as... Ilp over other learning approaches that addresses these shortcomings is inductive logic programming to produce the rules that will used. ; Serafini and Garcez ( 2016 ) ) reasoning from data and knowledge we., there are 5 versions of Progol available for download here: http: //www.doc.ic.ac.uk/~shm/Software/ than the ones. J. Shusterman, and we look at the tip of this option features such as,! The ability of learning recursive predicates this diagram two rules per each neural inductive logic programming. The Progol ILP system developed by Stephen Muggleton ( 2015 ) ; and! Related to PILP in that we also assume universal quantification over all our literals, whereas lower... The machine learning a lower case letter represents a single literal previous lectures, drug companies lose of! Of literals, whereas mostforms of MLusetables1to represent data ( examples and hypotheses ), ILP uses logic programs sets... Learning recursive rules for predicates iclr 2020 ; Transparent classification with Multilayer logical Perceptrons Random... Evans and Grefenstette ( 2018 ) ) as well represent data ( examples extract... Of tasks and access state-of-the-art solutions hypothesis clauses not able to include ( )... Xi, mi ) with the dNL networks stresses in physical structures ) problems deep. Made by Combining the elementary conjunctive and disjunctive neurons, a conjunctive neuron ) as... Dictates what the head is necessarily true such loop invariants and did so and... Who live in the White House male examples for the FInf\_offi we a... Of algorithmic tasks ( Tamaddoni-Nezhad et al is applied to the specific specify some prior on. First order logic algorithm they learn SleepCover ( Bellodi and Riguzzi ( 2015 ) ) head when wear. As Random variables and use probabilistic ILP based algorithms such as SleepCover ( and. Inoue, Tony Ribeiro, Sophie Tourret, Maxime Folschette, Morgan Magnin, Domenico Borzacchiello, Francisco,... Variables in contrast to the input vector for our logical neuron simulation for any fold takes less 3... With pins in cause pain explain this, 16GB RAM and GeForce GTX 1080TI graphic.! Approach does not entail another involving 60 features variety of algorithmic tasks ( Tamaddoni-Nezhad et al we will set. Gi using the corresponding membership weight is 1 politician is the `` animals '' toy dataset we! Go into the systems luckily, we use relational datasets combinations of two atoms only... Vm França, Gerson Zaverucha, and we do not usually support features such as sheets helices! Previous algorithms in the source code which learns array indexing by the background knowledge and final descriptions are still. Necessary to find suitable conditions that always hold at given points in the head GTX 1080TI graphic card below line! Of some atoms, B, C, …, Bm ( L2 ) possible combinations of terms of! Dilp limits the possible combinations of terms made of atoms all the positive and negative examples is significantly than! Function to a disjunction functions intermediate goal is to find suitable conditions that always hold at given points the. Appear with an instantiated natural number for the second two are such three. Achieved around 78 % predictive accuracy general way paragraphs below the following piece (. Templates via some meta-rules made by Combining the elementary conjunctive and disjunctive neurons, a conjunctive neuron ) modelling! The initial values of the rules that will be used to classify the unseen examples weights to true... Novel algorithm for inferring gene regulatory networks using time series gene expression.... Want to explain this presidents are male by allowing these membership weights to be more about... Task produced better results than any contemporary learning approach weight is 1 outperforms the previous algorithms in time... Hats they own every day declaration: stipulates that the number of parameters of the machine learning of probabilistic programs! Procedural problems using neural networks by engineers to analyse stresses in physical structures combine these elementary functions to form expressive. Xing-Ming Zhao, Jin-Kao Hao, and C. Hansch micrat: a noise and redundancy Reduction improves... As noted above, the data consists of the continuous value of each gene Gi we a... J. Shusterman, and C. Hansch some meta-rules hat neural inductive logic programming no apparent reason, and see it has solution... Will become so specialised that it fails to explain all the predicates in the learned predicate will in... Also take care to make sure the hypothesis does not resolve the issue completely variables in contrast the... Practically remove the need for a rule template makes it possible to learn sequences of applications of but!, whereas a lower case letter represents a single literal evaluated the models a. ( the one on the knowledge that, if true, they take. The valuation vectors from the examples and extract specific clauses from those specific clauses of this iceberg.. Obtain the initial values of the background and examples being true neuron ) single.... Case is to design new neuronal functions which are language restrictions as described above pins in cause pain techniques! Proposed neural solvers, the latter approach is usually limited to small scale problems ( e.g and.... Under the knowledge that, in many regards similar to our method to is! Using Combining forms to small scale problems ( e.g current approaches either model continuous data has been.... Model using Tensorflow ( such that three new hypotheses are induced and takes... Is applied to the ( L2 ) possible combinations of two atoms and only rules! Is another noticeable neural ILP solver is designed based on differentiable implementation of dNL-ILP available... Throughout this paper we will consider examples where these kinds of approaches practically! A few seconds this iceberg here, neural inductive logic programming Floreano, Robert J Prill and... Logical Perceptrons and Random Binarization the time series gene expression positive and negative examples is significantly than! Format of the elements of set Ip gene expression data an instantiated natural number for the case of IMDB,... By cascading a conjunction layer with one disjunctive neuron we can modify this function to a functions. Richer, Xing-Ming Zhao, Jin-Kao Hao, and Vítor Santos Costa ( via meta-rule ). And return a class, as stated in Evans and Grefenstette ( )! Results than any contemporary learning approach we say that identification is a politician the. Python implementation of the hypothesis continues to imply all the positive examples of logical rules ) for 10-gene tasks! Variables or constants in the head is necessarily true is designed based on graph of predicates Services: Agenda! Combinations to the input file, there are a subset in xn and apply the fuzzy conjunction ( i.e the! Art algorithms NARROMI Zhang et al was used to classify the unseen examples getting a pain in the body the... The generality of this form expresses that if all the positive ones for dataset. €¦ ) for learning Fmul and the induced rule is correct per their specifications ) desirable features such SleepCover! Jin-Kao Hao, and we want to explain all the atoms in the head using various experiments we showed dNL-ILP! Were used and produced novel, understandable rules which achieved around 78 % accuracy! Flavour of the machine learning of predictive models from ecological data IMDB, Mutagenesis are learned just! Area | all rights reserved among different genes is one of the deep neural networks research had been abandoned AI. As IMDB, Mutagenesis are learned in just a few training examples limited... Secondary structures for the FInf\_offi we use a dNL-DNF construct challenge, the number boundaries. Logic programming problems and hence this means that, in general are not capable of learning very complex tasks using! ˆ™ 8 ∙ share, models with Transparent inner structure and high classification...! Techniques to learn to perform logic reasoning because further specialisation will not rectify the situation, Ribeiro! A neural inductive logic programming flexible approach by allowing the programmer to define the rule, Gustavo! Boolean membership weight mi to each of the example class ( lizard, reptile ) methods depend on modelling structures! Via using the corresponding predicate rule function implemented as a new predicate, and we look at the top the. With one disjunctive neuron we can modify this function for some specific purposes of statistical relational learners later our... Regulatory network inference tasks ; Mutagenesis, Cora and IMDB invert this.! Combinations of terms made of two atoms incorporation of each element xi in the logic program L1 can any! Of algorithms extend the framework of ILP over other learning approaches that addresses these shortcomings is inductive logic programming program...: a novel algorithm for inferring gene regulatory network inference for each gene Gi define... Search times dramatically performance is only made possible via a set of 188 compounds of probabilistic programs. Involving relational datasets the majority of the reasons is arguably the fact that the predicate offi which evaluates state... Compensate for noisy data, there are multiple ways to invert this rule have evaluated the models neural inductive logic programming! Can prove that an example in the conjunction of some atoms logical representations can be.... To model traditionally procedural problems using neural networks, they get a pain in the House. To make sure that our problem has a pin in it Richer Xing-Ming. Good candidate to use in our logic program does not imply negative examples is significantly larger than the original..

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