Code Generation by Example Using Symbolic Machine Learning SN Computer Science

Fiber reinforced polymer (FRP) reinforced concrete slabs, an extension of traditional reinforced concrete (RC) slabs, is widely utilized as the component resisting corrosion caused by environment [1]. Owing to the advantages such as corrosion resistance and light weight, FRP slabs are spread worldwide as an effective alternative of the RC slabs [2]. As for RC slabs, under the action of external load, the diagonal cracks emerging initially in the tensile zone of concrete develop toward the compression zone until the steel reinforcements rupture or yield. In such cases, the progressive collapse of entire structures may happen (Fig. 1) [3].

Moreover, they lack the ability to reason on an abstract level, which makes it difficult to implement high-level cognitive functions such as transfer learning, analogical reasoning, and hypothesis-based reasoning. Finally, their operation is largely opaque to humans, rendering them unsuitable for domains in which verifiability is important. In this paper, we propose an end-to-end reinforcement learning architecture comprising a neural back end and a symbolic front end with the potential to overcome each of these shortcomings. As proof-of-concept, we present a preliminary implementation of the architecture and apply it to several variants of a simple video game.

Extended Data Fig. 2 The gold interpretation grammar that defines the human instruction learning task.

The mapping g should be injective, and only restricted forms of rule condition are possible, such as tests on metavariable type. The generated g should be correct wrt D, i.e., it should correctly translate the source part of each example \(d \in D\) to the corresponding target part of d. This method was popularized in 2009 with the introduction of a desktop software called Eureqa [1], which used a genetic algorithm to search for relevant formulas.

The richly structured architecture of the Schema Network can learn the dynamics of an environment directly from data. We compare Schema Networks with Asynchronous Advantage Actor-Critic and Progressive Networks on a suite of Breakout variations, reporting results on training efficiency and zero-shot generalization, consistently demonstrating faster, more robust learning and better transfer. We argue that generalizing from limited data and learning causal relationships are essential abilities on the path toward generally intelligent systems. Parsing, tokenizing, spelling correction, part-of-speech tagging, noun and verb phrase chunking are all aspects of natural language processing long handled by symbolic AI, but since improved by deep learning approaches. In symbolic AI, discourse representation theory and first-order logic have been used to represent sentence meanings. Latent semantic analysis (LSA) and explicit semantic analysis also provided vector representations of documents.

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The encoder network (Fig. 4 (bottom)) processes a concatenated source string that combines the query input sequence along with a set of study examples (input/output sequence pairs). The encoder vocabulary includes the eight words, six abstract outputs (coloured circles), and two special symbols for separating the study examples (∣ and →). The decoder network (Fig. 4 (top)) receives messages from the encoder and generates the output sequence. The decoder vocabulary includes the abstract outputs as well as special symbols for starting and ending sequences ( and , respectively). We next evaluated MLC on its ability to produce human-level systematic generalization and human-like patterns of error on these challenging generalization tasks.

Our machine learning approach holds promise in delving deeper into the concept of an aesthetic threshold. Individuals’ perceptions of art can be influenced by a range of factors including personal experiences, cultural background, emotional state, and inherent biases, all of which can change over time and in different contexts. This dynamism and its non-linearity make it challenging to accurately predict using traditional linear models.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. ArXiv is committed to these values and only works with partners that adhere to them. The general correlation between predictors and target were evaluated by creating a heatmap depicting all possible correlations (see Fig. S3 in Supplementary Information). This heatmap shows that there was no significant correlation between the predictors and the target variable (creativity). Our Principal Component Analysis (see Fig. S4 in Supplementary Information) further suggests that while these predictors are somewhat interrelated, they each contribute unique information to the understanding of creativity. Imagine how Turbotax manages to reflect the US tax code – you tell it how much you earned and how many dependents you have and other contingencies, and it computes the tax you owe by law – that’s an expert system.

In our case, the search space is restricted to those tree-to-tree functions which we have found to be widely used in code generation. Deep reinforcement learning (DRL) brings the power of deep neural networks to bear on the generic task of trial-and-error learning, and its effectiveness has been convincingly demonstrated on tasks such as Atari video games and the game of Go. However, contemporary DRL systems inherit a number of shortcomings from the current generation of deep learning techniques. For example, they require very large datasets to work effectively, entailing that they are slow to learn even when such datasets are available.

Symbolic Reasoning (Symbolic AI) and Machine Learning

As computational capacities grow, the way we digitize and process our analog reality can also expand, until we are juggling billion-parameter tensors instead of seven-character strings. Using symbolic AI, everything is visible, understandable and explainable, leading to what is called a “transparent box,” as opposed to the “black box” created by machine learning. Marvin Minsky first proposed frames as a way of interpreting common visual situations, such as an office, and Roger Schank extended this idea to scripts for common routines, such as dining out. Cyc has attempted to capture useful common-sense knowledge and has “micro-theories” to handle particular kinds of domain-specific reasoning. The logic clauses that describe programs are directly interpreted to run the programs specified. No explicit series of actions is required, as is the case with imperative programming languages.

• Implementations of symbolic reasoning are called rules engines or expert systems or knowledge graphs.
• The standard decoder (top) receives this message from the encoder, and then produces the output sequence for the query.
• All operations are executed in an input-driven fashion, thus sparsity and dynamic computation per sample are naturally supported, complementing recent popular ideas of dynamic networks and may enable new types of hardware accelerations.
• The phrase only occurs 4 times in the article and that includes the title and abstract.

Fodor and Pylyshyn1 famously argued that artificial neural networks lack this capacity and are therefore not viable models of the mind. Neural networks have advanced considerably in the years since, yet the systematicity challenge persists. Here we successfully address Fodor and Pylyshyn’s challenge by providing evidence that neural networks can achieve human-like systematicity when optimized for their compositional skills. To do so, we introduce the meta-learning for compositionality (MLC) approach for guiding training through a dynamic stream of compositional tasks.

Most of the time you will see people calling out Symbolic AI vs. symbolic machine learning. If you are interested in trying to build an artificial intelligence system that works similar to the human brain in terms of learning, then you are going to run into the idea of Symbolic AI. Think of things like deep learning, Bayesian networks, or evolutionary algorithms. What I was curious about this week was how many times people try to evaluate symbolic machine learning as a concept. Explicitly searching on Google Scholar for “symbolic machine learning” will yield just over 2,000 results [1]. Some of the academic coverage on this topic goes back to the 1990’s which was obviously where my reading started.

Beyond natural language, people require a years-long process of education to master other forms of systematic generalization and symbolic reasoning6,7, including mathematics, logic and computer programming. Although applying the tools developed here to each domain is a long-term effort, we see genuine promise in meta-learning for understanding the origin of human compositional skills, as well as making the behaviour of modern AI systems more human-like. Despite its successes, MLC does not solve every challenge raised in Fodor and Pylyshyn1. Moreover, MLC is failing to generalize to nuances in inductive biases that it was not optimized for, as we explore further through an additional behavioural and modelling experiment in Supplementary Information 2. However, meta-learning alone will not allow a standard network to generalize to episodes that are in turn out-of-distribution with respect to the ones presented during meta-learning. The current architecture also lacks a mechanism for emitting new symbols2, although new symbols introduced through the study examples could be emitted through an additional pointer mechanism55.

Symbolic AI

Symbolic artificial intelligence showed early progress at the dawn of AI and computing. You can easily visualize the logic of rule-based programs, communicate them, and troubleshoot them. In the paper, we show that a deep convolutional neural network used for image classification can learn from its own mistakes to operate with the high-dimensional computing paradigm, using vector-symbolic architectures. It does so by gradually learning to assign dissimilar, such as quasi-orthogonal, vectors to different image classes, mapping them far away from each other in the high-dimensional space. One promising approach towards this more general AI is in combining neural networks with symbolic AI.

• A successful model must learn and use words in systematic ways from just a few examples, and prefer hypotheses that capture structured input/output relationships.
• Say you have a picture of your cat and want to create a program that can detect images that contain your cat.
• In the paper, we show that a deep convolutional neural network used for image classification can learn from its own mistakes to operate with the high-dimensional computing paradigm, using vector-symbolic architectures.
• We have also evaluated CGBE on realistic examples of code generation tasks, to establish that it is effective for such tasks.

For the one-to-one translations, first, the study examples in the episode are examined for any instances of isolated primitive mappings (for example, ‘tufa → PURPLE’). For the noisy rule examples, each two-argument function in the interpretation grammar has a 50% chance of flipping the role of its two arguments. 4, the rule ⟦u1 lug x1⟧ → ⟦x1⟧ ⟦u1⟧ ⟦x1⟧ ⟦u1⟧ ⟦u1⟧, when flipped, would be applied as ⟦u1 lug x1⟧ → ⟦u1⟧ ⟦x1⟧ ⟦u1⟧ ⟦x1⟧ ⟦x1⟧. Symbolic ML approaches have been applied to model transformation by-example (MTBE) by [2] (using ILP) and by [21] (using search-based techniques). These typically use considerably smaller (i.e., KB-scale) training datasets compared to non-symbolic ML, and produce explicit rules.

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