# Non-deterministic interpreters and program synthesis

**Publish date:**Sep 14, 2022

**Last updated:**Dec 17, 2022

The goal of this post is to illustrate a simple enumerative synthesis technique based on graph traversal. To this end, I describe a very simple non-deterministic interpreter capable of synthesizing programs at run-time. Specifically, the interpreter seeks to leverage a corpus which includes competing implementations of function signatures, each implementation written under different contexts and assumptions.

All of the ideas presented here have been discussed elsewhere under different names (e.g. angelic programming).

## Preliminaries

Some notation

Primitive instructions \(p_i\).

Instructions that cannot be decomposed into steps.

Signatures \(\sigma\).

Function names typically used to represent sequences of steps.

Library \(L: \sigma \to [\sigma|p]^*\).

Map from function names to sequences of steps, called implementations.

Program state: \(s\).

State of the environment in which a program is being executed.

Primitive interpreter: \(P: (p, s) \to s\).

Map from primitive instructions and program states to program states.

As an example, picture the contents of \(L\) to be of the following form, where lowercase names represent signatures and uppercase names represent primitive instructions:

```
def craft_stick():
craft_plank()
craft_plank()
PLACE_PLANK()
PLACE_PLANK()
CRAFT()
```

## Direct programming

Let us review the mechanics of executing a function call in imperative programming languages. For simplicity, we will omit control-flow, variables and assume programs are straight-line sequences of steps.

Assume we have to execute \(\sigma()\) under program state \(s_0\) using function library \(L\). Our interpreter will probably do something like this:

If \(\sigma\) is a primitive instruction \(p\), return \(P(p, s)\). Otherwise,

Read implementation of \(\sigma\) in \(L\): \(L(\sigma) = [\sigma_1(); \ldots; \sigma_n();]\).

Recursively execute each \(\sigma_i()\) under \(s_{i-1}\) to get \(s_i\).

Return \(s_n\).

This heavily simplified description of program execution represents “direct programming” (ad-hoc name in use by collaborators and I): the traditional programming paradigm where function calls have a one-to-one correspondence with concrete implementations.

Note that the new program state \(s_n\) is fully determined by the sequence of primitives that were executed.

## Natural programming

Assume library \(L\) contains several implementations of \(\sigma\), instead of just one. The value of \(L\) for a signature \(\sigma\) is thus a collection of step sequences: \(L(\sigma) = \{[\sigma_1^i(); \ldots; \sigma_{n_i}^i();]\}_i\)

This is the core of a programming paradigm we have been calling Natural Programming, the details of which I will not go into in this post. For now let us only concern ourselves with designing an interpreter that can leverage a corpus of competing implementations for any function signature.

### Specification

The first thing that becomes apparent is the need for choosing among the different implementations. Let us delegate this to the user and assume each signature comes equipped with a specification: a boolean function \(\varphi_\sigma: (s, [p_i]^*) \to \{\top, \bot\}\) which indicates whether a particular sequence of primitives is a good implementation of \(\sigma\) in state \(s\).

For example, for \(\sigma = \textit{go to kitchen}\), the corresponding \(\phi_\sigma\) would check if executing the given sequence of primitives takes the robot to the kitchen. Note that multiple implementations may satisfy a specification.

## Natural Program interpreter

A key realization is that adding constraints to function signatures allows us to consider program search as part of the interpreter. In this approach, function signatures do not have a fixed implementation, rather, they specify properties that the function execution must satisfy and can leave the specific low-level details to a solver.

The interpreter can then choose an implementation appropriate for the given context, thus, transforming into a non-deterministic interpreter. The question then is: how do we search for implementations in the set of natural programs given a library of known signature implementations?

It is important to note that every function call is recursively now a synthesis problem, as for every function call we have access to a corpus of implementations.

### First attempt

To interpret a signature with a specification \(\sigma\), one could be inclined to simply execute each implementation in \(L(\sigma)\) and return the first one that satisfies \(\varphi_\sigma\). Checking if an implementation satisfies the constraint requires keeping track of which primitives are executed. Thus our first attempt may look like this:

```
# First attempt.
def interpreter(sigma, L, s0, P) -> tuple[ProgramState, list[Primitive]]:
if is_primitive(s):
return P(s, sigma), [sigma]
for implementation in L(sigma):
si = s0
primitives = []
for sigma_i in implementation:
si, ps = interpreter(sigma_i, L, si, P)
primitives.extend(ps)
if sigma.phi(s, primitives):
return si, primitives
raise NoImplementationFoundError(sigma, L, s0, P)
```

This interpreter is incomplete in the sense that it does not maximally use data in \(L\). The problem is that only the first satisfying implementation will be returned, but that implementation may not lead to a program state where the next signatures can be interpreted.

### Graph traversal

To understand the problem with our first attempt this we can describe the synthesis problem as a graph search problem. Programs (including “incomplete” ones, e.g., signatures) correspond to nodes in the graph. Programs that are complete (i.e., have no signatures without implementation) or crash will be leafs. Programs that are incomplete will be connected to all programs that correspond to a top-level implementation of the left-most non-expanded signature.

Casting the problem as a graph search problem allows us to devise a solution to our problem: we can simply traverse the graph (e.g., depth-first) starting from a given signature until we find a program that satisfies the constraints and does not crash. If we end up in a leaf that does not satisfy the specification, we can backtrack one level and continue the search with the other children of the parent node. Our previous algorithm did not backtrack, and thus it did not search the entire graph, only a subset of it. This algorithm maximally uses data in \(L\) because it recursively explores every possible way to interpret the program with the implementations in \(L\) before failing.

In fact, this is the approach we took in our interpreter. In our code graph traversal is implemented with a priority queue (also known as a fringe), because the search space is extremely big and we compute recency and frequency implementation scores to guide the search.

(*Note: I know this rushed description of the interpreter goes against the goal of illustrating the algorithm. Please excuse my brevity and do contact me if you think it is worth to add pictures and a thorough explanation, including step-by-step examples and how to go from graph-traversal to fringe-search.*)

## Why?

On-line search opens the door to creating agents that can better adapt to novel scenarios. This has been observed beyond program synthesis, in the context of model-based reinforcement learning and model-predictive control.

There are caveats to this approach. Performance of interpreters that synthesize programs at run-time depends on execution state and other factors, which can easily lead to inherently unstable systems. For instance, a program that synthesizes in a fast computer may fail to execute in a slow computer. Or a program that synthesizes in an execution environment may fail in another environment.

It is my opinion, however, that in some domains non-determinism can be a better approach to traditional deterministic interpreters, particularly in domains where these caveats can be engineered to be non-significant to user experience.