Neural-Symbolic Machine Learning for Retrosynthesis and Reaction Prediction

Where f.cstl is the code generator, and asts.txt is a list of source language ASTs, one per line. The script applies f.cstl to each AST in asts.txt and computes statistics of the average size and translation time of the examples. Because software languages are generally organised hierarchically into sublanguages, e.g., concerning types, expressions, statements, operations/functions, classes, etc., D will typically be divided into parts corresponding to the main source language divisions.

The phrase only occurs 4 times in the article and that includes the title and abstract. On the other hand, researchers in visual arts, creativity, and psychology typically measure creativity levels through subjective assessments of the final products using mainly linear statistical methods (3, see for review24,25). Such assessments have been employed in art viewing studies, including, e.g., standardized tests where participants complete drawings and provide subsequent scores or ratings26. These assessments gained prominence following the seminal work of Daniel Berlyne27,28,29, a central figure in art research who primarily studied art-specific properties through preference judgments.

Behavior appraisal of steel semi-rigid joints using Linear Genetic Programming

The expert system processes the rules to make deductions and to determine what additional information it needs, i.e. what questions to ask, using human-readable symbols. For example, OPS5, CLIPS and their successors Jess and Drools operate in this fashion. During the COGS test (an example episode is shown in Extended Data Fig. 8), MLC is evaluated on each query in the test corpus. For each query, eight study examples are sampled from the training corpus, using the same procedure as above for picking study examples that facilitate word overlap (note that picking study examples from the generalization corpus would inadvertently leak test information). Neither the study nor query examples are remapped to probe how models infer the original meanings. First, we evaluated lower-capacity transformers but found that they did not perform better.

The set of mathematical formulas to choose from is larger than you might imagine, but this is where a good symbolic regression algorithm will help. By cleverly navigating this space, meaningful formulas can be obtained in a reasonable amount of time. Code generation of Java, Kotlin, and C involves many of the problems encountered in code generation of other object-oriented and procedural languages. As a representative of languages with implicit typing we also considered code generation of JavaScript. Table 6 shows two maintenance examples and the relative costs in terms of developer time of performing these on manually coded M2T and T2T generators, and using CGBE.

Translations into Polish

Interpretations of general collection expressions, loops over collections, and general operation definitions and calls cannot be given. Nonetheless, some meaningful rules, such as the above case of not, can be successfully learnt from examples. Context-sensitive generation is handled by the construction of new local functions in strategy 3; these functions are specific to the particular mapping context. In other words, this translation of source trees to target trees is assumed to be valid for all OclIdentifier elements with the same structure, regardless of the actual identifier in the first subterm of the source element parse tree.

• As a subset of first-order logic Prolog was based on Horn clauses with a closed-world assumption — any facts not known were considered false — and a unique name assumption for primitive terms — e.g., the identifier barack_obama was considered to refer to exactly one object.
• Code generation enables the synthesis of applications in executable programming languages from high-level specifications in UML or in a domain-specific language.
• Instead, MLC provides a means of specifying the desired behaviour through high-level guidance and/or direct human examples; a neural network is then asked to develop the right learning skills through meta-learning21.
• Word meanings are changing across the meta-training episodes (here, ‘driver’ means ‘PILLOW’, ‘shoebox’ means ‘SPEAKER’ etc.) and must be inferred from the study examples.

For RQ2, we evaluate the effectiveness of CGBE using it to construct code generators from UML/OCL to Java, Kotlin, C, JavaScript, and assembly language, based on text examples. We also construct a code generator from a DSL for mobile apps to SwiftUI code. For RQ3, we compare the effort required for construction and maintenance of code generators using CGBE with manual code generator construction/maintenance. We also compare the application of CGBE on the FOR2LAM case of [4] with their neural-net solution, and compare CGBE with the use of MTBE on instance models of language metamodels. For RQ4, we consider how CGBE can be generalised to apply to the learning of software language abstractions and translations.

Extended Data Fig. 4 Example meta-learning episode and how it is processed by different MLC variants.

For comparison with the gold grammar or with human behaviour via log-likelihood, performance was averaged over 100 random word/colour assignments. Samples from the model (for example, as shown in Fig. 2 and reported in Extended Data Fig. 1) were based on an arbitrary random assignment that varied for each query instruction, with the number of samples scaled to 10× the number of human participants. Optimization for the copy-only model closely followed the procedure for the algebraic-only variant. Critically, this model was trained only on the copy task of identifying which study example is the same as the query example, and then reproducing that study example’s output sequence (see specification in Extended Data Fig. 4; set 1 was used for both study and query examples). It was not trained to handle novel queries that generalize beyond the study set.

Symbolic AI is a sub-field of artificial intelligence that focuses on the high-level symbolic (human-readable) representation of problems, logic, and search. For instance, if you ask yourself, with the Symbolic AI paradigm in mind, “What is an apple? ”, the answer will be that an apple is “a fruit,” “has red, yellow, or green color,” or “has a roundish shape.” These descriptions are symbolic because we utilize symbols (color, shape, kind) to describe an apple. One of the keys to symbolic AI’s success is the way it functions within a rules-based environment.

LBE corrosion fatigue life prediction of T91 steel and 316 SS using machine learning method assisted by symbol regression

We have shown that this approach can produce correct and effective code generators, with a significant reduction in effort compared to manual construction of code generators. We also showed that it can offer reduced training times and improved accuracy compared with a neural net-based approach to learning program translations. Already, this technology is finding its way into such complex tasks as fraud analysis, supply chain optimization, and sociological research. But the benefits of deep learning and neural networks are not without tradeoffs. Deep learning has several deep challenges and disadvantages in comparison to symbolic AI.

However, machine learning also faces general limitations of data-driven modelling method. Nevertheless, interpretable machine learning overcomes some limitations of traditional data modelling. That is, interpretable machine learning offers flexible, complex, yet robust, credible, and assessable models39.

It also empowers applications including visual question answering and bidirectional image-text retrieval. The query input sequence (shown as ‘jump twice after run twice’) is copied and concatenated to each of the m study examples, leading to m separate source sequences (3 shown here). A shared standard transformer encoder (bottom) processes each source sequence to produce latent (contextual) embeddings. The contextual embeddings are marked with the index of their study example, combined with a set union to form a single set of source messages, and passed to the decoder.

The standard decoder (top) receives this message from the encoder, and then produces the output sequence for the query. Each box is an embedding (vector); input embeddings are light blue and latent embeddings are dark blue. MLC was evaluated on this task in several ways; in each case, MLC responded to this novel task through learned memory-based strategies, as its weights were frozen and not updated further. MLC predicted the best response for each query using greedy decoding, which was compared to the algebraic responses prescribed by the gold interpretation grammar (Extended Data Fig. 2). MLC also predicted a distribution of possible responses; this distribution was evaluated by scoring the log-likelihood of human responses and by comparing samples to human responses. Although the few-shot task was illustrated with a canonical assignment of words and colours (Fig. 2), the assignments of words and colours were randomized for each human participant.

(a) Association between symbolism and creativity, (b) association between emotionality and creativity, (c) association between imaginativeness and creativity. At the end of the testing session, all participants answered questions about their art expertise, education, and experience in making art. Additionally, they responded to the questions included in VAIAK55 to measure their art interest and knowledge scores. The set of art-attribute items was presented as bipolar scales (semantic differentials), with a slider positioned in the middle between the two poles on a 100-point Likert scale. The full list of items for the attributes, along with their dimensions and the exact wording in German, is provided in Supplementary Information Table S1, and the English version is described in the following subsection (also see Table 1).

Thus, the model was trained on the same study examples as MLC, using the same architecture and procedure, but it was not explicitly optimized for compositional generalization. The tension between deduction and induction is perhaps the most fundamental issue in areas such as philosophy, cognition and artificial intelligence (AI). The deduction camp concerns itself with questions about the expressiveness of formal languages for capturing knowledge about the world, together with proof systems for reasoning from such knowledge bases.

The last rule was the same for each episode and instantiated a form of iconic left-to-right concatenation (Extended Data Fig. 4). Study and query examples (set 1 and 2 in Extended Data Fig. 4) were produced by sampling arbitrary, unique input sequences (length ≤ 8) that can be parsed with the interpretation grammar to produce outputs (length ≤ 8). Output symbols were replaced uniformly at random with a small probability (0.01) to encourage some robustness in the trained decoder. For this variant of MLC training, episodes consisted of a latent grammar based on 4 rules for defining primitives and 3 rules defining functions, 8 possible input symbols, 6 possible output symbols, 14 study examples and 10 query examples.

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