Enum SynthesisType

Namespace

AiDotNet.ProgramSynthesis.Enums

Assembly

AiDotNet.dll

Defines the different types of program synthesis approaches available.

public enum SynthesisType

Fields

Deductive = 5

Deductive program synthesis from formal specifications.

Deductive synthesis constructs programs from formal specifications that precisely describe the desired behavior. This approach provides strong correctness guarantees but requires users to provide detailed formal specifications.

For Beginners: Deductive synthesis works from precise descriptions.

You provide a detailed specification of exactly what the program should do using mathematical logic or formal notation, and the system constructs a program that provably meets that specification. Like building from detailed blueprints.

GeneticProgramming = 3

Genetic programming approach using evolutionary algorithms.

Genetic programming evolves programs through processes inspired by biological evolution, including selection, crossover (combining parts of programs), and mutation (random changes). Programs that perform better are more likely to survive and reproduce.

For Beginners: Genetic programming is like evolution in nature.

It creates a population of random programs, tests them, keeps the best ones, and creates new programs by mixing and mutating the good ones. Over many generations, the programs get better and better, like species evolving over time.

Hybrid = 2

Hybrid approach combining both neural and symbolic techniques.

Hybrid synthesis combines the strengths of both neural and symbolic approaches, using neural networks for creative exploration and symbolic methods for verification and constraint satisfaction.

For Beginners: Hybrid synthesis combines the best of both worlds.

It uses neural networks to come up with creative ideas quickly, then uses symbolic methods to check and refine them. Like brainstorming ideas (neural) then fact-checking them (symbolic).

Inductive = 4

Inductive program synthesis that learns from input-output examples.

Inductive synthesis generates programs by generalizing from a set of input-output examples. This is particularly useful when users can provide examples of desired behavior but may not know how to express the logic formally.

For Beginners: Inductive synthesis learns from examples of what you want.

Instead of telling the computer exactly what to do, you show it examples: "When input is [1,2,3], output should be 6" "When input is [4,5], output should be 9" The system figures out you want it to sum the numbers.

Neural = 0

Neural network-based program synthesis using deep learning models.

Neural synthesis uses trained neural networks to generate programs by learning patterns from a large corpus of existing code. This approach is data-driven and can produce creative solutions but may lack guarantees of correctness.

For Beginners: Neural synthesis is like learning to code by studying lots of examples.

The AI looks at thousands of code examples and learns patterns, then generates new code based on what it has learned. Similar to how you might learn to write by reading many books.

Symbolic = 1

Symbolic program synthesis using formal logic, grammars, and search algorithms.

Symbolic synthesis uses formal methods, programming language grammars, and logical constraints to systematically explore the space of possible programs. This approach provides stronger correctness guarantees but may be limited in creativity.

For Beginners: Symbolic synthesis is like following a recipe or instruction manual.

It uses strict rules about what code should look like and systematically tries different combinations until it finds one that works. Like solving a math problem step by step.

Remarks

This enumeration categorizes the various methodologies used for automated program synthesis. Each approach has different strengths and is suited for different types of programming tasks.

For Beginners: Think of these as different strategies for automatically creating programs.

Just like there are different approaches to solving a puzzle (looking at the picture, starting from corners, sorting by color), there are different ways to automatically generate code:

• Neural: Uses neural networks that learn from examples
• Symbolic: Uses logical rules and grammar
• Hybrid: Combines neural and symbolic approaches
• GeneticProgramming: Evolves programs through selection and mutation