Symbolic vs Subsymbolic AI Paradigms for AI Explainability by Orhan G. Yalçın

Human-like systematic generalization through a meta-learning neural network

symbolic machine learning

This directed mapping helps the system to use high-dimensional algebraic operations for richer object manipulations, such as variable binding — an open problem in neural networks. When these “structured” mappings are stored in the AI’s memory (referred to as explicit memory), they help the system learn—and learn not only fast but also all the time. The ability to rapidly learn new objects from a few training examples of never-before-seen data is known as few-shot learning. A key component of the system architecture for all expert systems is the knowledge base, which stores facts and rules for problem-solving.[52]

The simplest approach for an expert system knowledge base is simply a collection or network of production rules. Production rules connect symbols in a relationship similar to an If-Then statement.

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However, for discovering precise translations, such as code generation mappings, a symbolic ML approach seems more appropriate. Currently, the approach is oriented to the production of code generators from UML/OCL to 3GLs. It is particularly designed to work with target languages supported by Antlr Version 4 parsers. Antlr parsers are available for over 200 different software languages, so this is not a strong. RestrictionFootnote 12 To apply CGBE for target language T, the user needs to identify the T grammar rules that correspond to the general language categories of expressions, statements, etc. (Fig. 6). The metamodel mmCGBE.txt of syntactic categories, and the outline mapping of syntactic categories may also need to be modified.

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4.4 is also representative of typical code generation tasks from DSL specifications. Unlike the neural-net approach of [4], there is no deterioration of accuracy in our approach with larger inputs, because a precise and correct algorithm has been learnt. The execution time for translation grows linearly with input size (24 ms per example for S examples, 50 ms per example for L examples), whereas the NN model has less consistent time performance (360 ms per example for S examples, over 2 s per example for L examples). The paper [4] defines a neural-net ML approach for learning program translators from examples.

  • More advanced knowledge-based systems, such as Soar can also perform meta-level reasoning, that is reasoning about their own reasoning in terms of deciding how to solve problems and monitoring the success of problem-solving strategies.
  • Our analyses revealed symbolism, emotionality, and imaginativeness as the primary attributes influencing creativity judgments.
  • Performance was averaged over 200 passes through the dataset, each episode with different random query orderings as well as word and colour assignments.
  • Panel (A) shows the average log-likelihood advantage for MLC (joint) across five patterns (that is, ll(MLC (joint)) – ll(MLC)), with the algebraic target shown here only as a reference.

McCarthy’s Advice Taker can be viewed as an inspiration here, as it could incorporate new knowledge provided by a human in the form of assertions or rules. For example, experimental symbolic machine learning systems explored the ability to take high-level natural language advice and to interpret it into domain-specific actionable rules. The validation episodes were defined by new grammars that differ from the training grammars. Grammars were only considered new if they did not match any of the meta-training grammars, even under permutations of how the rules are ordered.

Human-like systematic generalization through a meta-learning neural network

Symbols can be organized into hierarchies (a car is windows, tires, seats, etc.). They can also be used to describe other symbols (a cat with fluffy ears, a red carpet, etc.). If I tell you that I saw a cat up in a tree, your mind will quickly conjure an image. Partial dependency plots showing the association between the most important art-attribute dimensions and creativity ratings.

symbolic machine learning

Examples of common-sense reasoning include implicit reasoning about how people think or general knowledge of day-to-day events, objects, and living creatures. Semantic networks, conceptual graphs, frames, and logic are all approaches to modeling knowledge such as domain knowledge, problem-solving knowledge, and the semantic meaning of language. DOLCE is an example of an upper ontology that can be used for any domain while WordNet is a lexical resource that can also be viewed as an ontology.

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  • One participant was excluded because they reported using an external aid in a post-test survey.
  • 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.
  • On average, the participants spent 5 min 5 s in the experiment (minimum 2 min 16 s; maximum 11 min 23 s).
  • In addition, we would expect that experts will use more art-attributes for their evaluation in general.
  • They are inserted in \(\sqsubset \) order, so that more specific rules occur prior to more general rules in the same category.
  • As in SCAN, the main tool used for meta-learning is a surface-level token permutation that induces changing word meaning across episodes.