Gigamachine: incremental machine learning on desktop computers

Gigamachine: incremental machine learning on desktop computers

Eray Özkural
Bilkent University
Ankara, Turkey
Abstract

We present a concrete design for Solomonoff’s incremental machine learning system suitable for desktop computers. We use R5RS Scheme and its standard library with a few omissions as the reference machine. We introduce a Levin Search variant based on a stochastic Context Free Grammar together with new update algorithms that use the same grammar as a guiding probability distribution for incremental machine learning. The updates include adjusting production probabilities, re-using previous solutions, learning programming idioms and discovery of frequent subprograms. The issues of extending the a priori probability distribution and bootstrapping are discussed. We have implemented a good portion of the proposed algorithms. Experiments with toy problems show that the update algorithms work as expected.

Gigamachine: incremental machine learning on desktop computers


Eray Özkural Bilkent University Ankara, Turkey

Introduction

Artificial General Intelligence (AGI) field has received considerable attention from researchers in the last decade, as the computing capacity marches towards human-scale. Many interesting theoretical proposals have been put forward (???) and practical general-purpose programs have been demonstrated (for instance (??)). We currently understand the requirements of such a system much better than we used to, therefore we believe that it is now time to start constructing an AGI system, or at least a prototype based on the solid theoretical foundation that exists today. We anticipate that during the tedious work of writing such general purpose AI programs, we will have to solve various theoretical problems and deal with practical details. It would be for the best if we expose those problems early on.

Gigamachine is our initial implementation of an AGI system in the O’Caml language with the goal of building what Solomonoff calls a “Phase 1 machine” that he plans to use as the basis of a quite powerful incremental machine learning system (?). While a lot of work remains to implement the full system, the present algorithms and implementation demonstrate a lot of issues in building a realistic system. Thus, we report on our ongoing research to share our experience in designing such a system. Due to space restrictions we cannot give much background, and we proceed directly to our contributions.

Scheme as the reference machine

(?) argues that the choice of a reference machine introduces a necessary bias to the learning system and looking for the “ultimate machine” may be a red herring. In (?), the program-size efficient FORTH language was employed to great effect. In (?) and other publications of Solomonoff, we see that an as of yet (seemingly) unimplemented reference machine called AZ is introduced. The AZ language is a functional programming language that adopts a prefix (Polish) notation for expressions. Solomonoff also suggests adding primitives such as , , , , , , etc. For a specific application, one must choose a universal computer with as many suitable primitives as possible, for it would take a lot of time for the system to discover those primitives on its own, and the training sequence would have to be longer to accommodate for the discovery of those primitives.

For a general purpose machine learning system, we need a general purpose programming system that can deal with a large variety of data structures and makes it possible to write sophisticated programs of any kind. While FORTH has yielded rather impressive results, we have chosen R5RS Scheme on the grounds that it is a simple yet general purpose high-level programming language. Certain other features of it also make it desirable. Scheme was invented by Guy Lewis Steele Jr. and Gerald Jay Sussman (?). It is an improvement over LISP in that it is statically scoped and its implementations are required to have proper tail recursion; R5RS Scheme is defined precisely in a standards document (?). R5RS contains a reasonably sized standard library. We do not think that Scheme has any major handicaps compared to AZ. The small syntactic differences are not very important, but language features are. Scheme does include a functional language, in addition to imperative features. It is highly orthogonal as it is built around symbolic expressions. The syntax-semantics mapping is quite regular, hence detecting patterns in syntax helps detecting patterns in semantics. There are a lot of efficient interpreters for Scheme, which may be modified easily for our uses (we used the ocs interpreter with a Scheme execution cycle limit that we added). Static scopes mean that the variable access is fast and uncomplicated. Scheme R5RS is quite high level, it has all the basic data structures like lists, vectors, and strings. It can work with a variety of numbers like integers, rationals, reals (also unlimited precision reals), complex numbers and the standard library contains the mathematical functions associated with these number types.

Adaptation to program generation

While we do not think that Scheme R5RS is the ultimate reference machine, it has formed a good platform for testing out our ideas about incremental learning. We have implemented most of the R5RS syntax, with a few omissions. We have elected to exclude the syntax for quasi-quotations and syntax transformation syntax, as the advanced macro syntax would complicate our grammar based guidance logic, and as it is an advanced feature that is used only in more complex programs, which we do not expect to generate in the Gigamachine. Further simplifications were deemed necessary. In some parts of the syntax, the same semantics can be expressed in different ways, for instance, (quote (0 1)) and ‘(0 1) have the same semantics. In that case, we only used quote. In the case of number literals, we do not generate alternative radii and use only base 10. All of the R5RS standard library has been implemented except for input/output (6.6) and system interface (6.6.4) forming an adequate basis for generating simple programs. A special non-terminal called standard-procedure was added to the grammar which produces standard library procedure calls, with the correct number of arguments. The standard-procedure is added as an alternative production of the procedure-call head in the Scheme standard grammar. Further useful libraries common across Scheme implementations may be easily added to the present system.

Program Search

In many AGI systems, a variant or extension of Levin Search (?) is used for finding solutions. Solomonoff’s incremental machine learning also uses Levin Search as the basic search algorithm to find solutions (?). In our system, we take advantage of the stochastic grammar based guiding probability mass function (pmf) for the search procedure as well. We first describe a generalized version of Levin Search and then build on it.

Generalized Levin Search

In Algorithm 1 we give a generalized Levin Search algorithm similar to the one described in (?). Inputs are as follows. is a universal computer and executes a program p up to a duration of and returns its output. is a grammar that defines the language of valid programs (and is its language) in . is an a priori pmf of programs of . TestProg is an algorithm that takes a candidate program and forms a test program in the program coding of . TrueVal is the value of “true” in the language of .

The constant is the initial time limit and the constant is the time quantum that is the minimum time we run a program.

We start with a global time limit equal to . We then start an infinite loop. Within an iteration, we allocate time to all the programs in proportion to their a priori probabilities. We choose a set of candidate programs among the language of such that the allocated time of a program is greater than or equal to . For each program in , we construct a test program using the algorithm TestProg. We execute the test program up to the time limit . Thus, the total time of running and testing candidate programs do not exceed (with some work, the cost of generation can be added as well). If the test is successful, the search procedure returns . Otherwise, after all the candidate programs are tested in the iteration, the time limit is doubled, and the search continues.

1:  
2:  while  true  do
3:     
4:     for all  do
5:        if   then
6:           return  
7:        end if
8:     end for
9:     
10:  end while
Algorithm 1

Using a stochastic CFG in Levin Search

A stochastic CFG is a CFG augmented by a probability value on each production. For each head non-terminal, the probabilities of productions of that head must sum to one, obviously.

We can extend our Levin Search procedure to work with a stochastic CFG that assigns probabilities to each sentence in the language. For this, we need two things, first a generation logic for individual sentences, and second a search strategy to enumerate the sentences that meet the condition in Line 3 of Algorithm 1.

In the present system, we use leftmost derivation to generate a sentence, intermediate steps are thus left-sentential forms (?, Chapter 5). The calculation of the a priori probability of a sentence depends on the obvious fact that in a derivation where productions have been applied in order to start symbol , the probability of the sentence is naturally . Note that the productions in a derivation are conditionally independent. While this makes it much easier for us to calculate probabilities of sentential forms, it limits the expressive power of the probability distribution.

A relevant optimization here is starting not from the absolute start symbol (in the case of R5RS Scheme program) but from any arbitrary sentential form. This helps fixing known parts of the program that is searched, and we have done so in the implementation.

The search strategy is important for efficient and correct implementation of the for loop in Line 4 of Algorithm 1. We examine two relevant search strategies.

Depth-limited depth-first search

Depth-first search is a common search strategy when the search space is large and the depth is manageable. We make use of a depth-limit in the form of a “probability horizon” which is a threshold we impose corresponding to the smallest probability sentence that we are willing to generate. The probability horizon can be calculated from and as , which ensures that we will not waste time generating any programs that we will not run. The depth-first search is implemented by expanding the leftmost non-terminal in a sentential form, pruning the sentential forms which have a priori probabilities smaller than the probability horizon, sorting the expanded sentential forms in the order of decreasing a priori probability and then recursively searching the list of sentences obtained in that fashion. The recursion can be implemented via a stack or plain recursion. Stack implementation turned out to be a bit faster.

Best-first search and hybrid search

A problem with the depth-first search order is that the search procedure can terminate with a program that has a smaller a priori probability than the best solution in current . To amend this shortcoming, we have tried a best-first search strategy, which maintains a global priority queue during the expansion of the leftmost non-terminal. Since every non-terminal has to be eventually expanded this should indeed maintain a global order. However, maintaining best-first search has a high memory cost, therefore in the implementation we disabled this strategy as it quickly exhausts available memory.

A solution that we have not yet tried is a hybrid search strategy. Hybrid search was first proposed in (?) as 50%-50% time sharing of breadth-first and depth-first search for a simpler program probability model. When using stochastic CFG’s, there may be many non-terminals to expand in a sentential form, therefore strict breadth-first search may incur a very high memory cost. Note that by lazily expanding nodes, we can fix this memory cost problem. The hybrid search strategy however, can be used to run either breadth-first or best-first alongside depth-first search. An improved hybrid search strategy is memory aware; it runs best-first or breadth-first until the search queue structure reaches a certain size, and then switches dynamically to depth-first search.

Solomonoff also suggests a randomized LSearch which we did not try as the disk swapping scheme seems too expensive (?).

Generation of literals

In (?) the Rissanen distribution is proposed for generation of integer literals. An alternative is the Zeta distribution with the pmf given by

where is the Riemann zeta function. We have used the Zeta distribution with and used a pre-computed table to generate up to a fixed integer (1024 in our current implementation). The Zeta distribution has empirical support, that is why it was preferred. The upper bound is present to avoid too many programs with equal constants. The expression syntax handles larger integers, for instance (* 1024 200). A smaller or greater upper bound may be appropriate, this is a matter of experimentation.

The string literals are generated as a sequence of characters in the grammar, as the default sequence rules seemed reasonable. We did not see the need to come up with a specialized generation, but of course a Zipf-distribution may be appropriate for that purpose.

To implement these special distributions and other kinds of rules, we have defined a second kind of non-terminal which we call a non-terminal procedure. A non-terminal procedure in our implementation is a function that generates a list of sentential forms and associated probabilities that can be used by the sequential enumeration algorithm. In the below, it will also be used to implement a special kind of context-sensitivity.

Defining and referencing variables

A major problem in our implementation was the abundance of “unbound reference” errors. We have devised a simple solution that can be implemented easily.

We maintain a static environment during leftmost derivation of a sentence that is passed along to the right, possibly with modification. The environment is also passed along to non-terminal procedures. Initially, the environment includes the input parameters to the function that is being searched. When a variable is defined, a robotic variable name is generated in the form of varinteger where the non-terminal integer is sampled from the Zeta distribution. We use the same pre-computed Zeta distribution and the same upper bound as in integer literal generation. This is not a drastic limitation as it limits only temporary variables within the same scope. When a variable reference is generated, the environment is present and we choose among available variable names with uniform distribution.

Nevertheless, this fix does not respect the nesting of scopes. If we are expanding a definition (as in (define a 3), then the variables defined in that definition must be available in the enclosing scope. This requires the generator to be aware of scope beginning and ending. Within a scope, the definitions, excluding mutually recursive definitions, are available from the point of definition until the scope end. Robotic variable definitions are seen in define, lambda, let, let*, letrec and do blocks. Mutually recursive definitions in letrec blocks have to be handled in a different way by first generating all the robotic variables and then generating the rest of the bindings.

The current implementation does not have any such scope begin/end awareness since it is unlikely to generate very long programs. It propagates environment modifications from left to right disregarding any nesting of scopes. To make it aware without rewriting everything, we might try to annotate the rules that define new scopes with special non-terminal symbols scope-begin and scope-end. Instead of passing a single environment to the partial derivation, we can pass a stack of environments. when a scope-begin is seen, the current stack is pushed, and when a scope-end is seen the stack is popped.

Stochastic CFG updates

The most critical part of our design is updating the stochastic CFG so that the discovered solutions in a training sequence will be more probable when searching for subsequent problems. We propose four update algorithms that work in tandem.

(?) mentions PPM (?). PPM can indeed be used to extrapolate a set of programs, however we do not think it is practical for incremental machine learning. In fact, we had adopted one of the recent variants of PAQ family of compression programs (?) for this purpose (by first compressing the set of programs and then appending bits to the end of the stream dynamically during decompression), and we saw that the extrapolated programs were mostly syntactically incorrect. We can devise a PPM variant that is useful for extrapolating programs but this would not make our job easier. The update algorithms that we propose are more powerful.

Modifying production probabilities

The simplest kind of update is modifying the probabilities as new solutions are added to the solution corpus. For this, however, the search algorithm must supply the derivation that led to the solution (which we do), or the solution must be parsed using the same grammar. Then, the probability for each production in the solution corpus can be easily calculated by the ratio of frequency of productions in the solution corpus to the frequency of productions in the corpus with a head of . The non-terminal procedures are naturally excluded from the update as they can be variant. However, we cannot simply write the probabilities calculated this way over the initial probabilities, as initially there will be few solutions, and most probabilities will be zero. We use exponential smoothing to solve this problem.

where is the initial probability, is the probability in the corpus and is the smoothing factor. We used a smoothing factor of . See (?) for the application of smoothing in a similar problem. Other methods like Laplace’s rule may be used to avoid zero probabilities (?).

While modifying production probabilities is a useful idea, it cannot add much information to the guiding pmf as the total amount of information is limited by the number of bits per probability multiplied by the number of probabilities. While we can use arbitrary precision floating point numbers, it does not seem likely that distinguishing more finely among a few number of alternative productions for a non-terminal will result in great improvements. Then, it seems that we need to augment the grammar with new productions. An idea we have thought but not yet tried is to convert occurrences of the same non-terminal into multiple non-terminals, so they will have different probabilities as a result of learning. A collection of non-terminals can be replicated in this way, as well. However, of course, this replication is also limited and does not seem to overcome the structural limitation of modifying probabilities.

Re-using previous solutions

In the course of a training sequence, the solutions can be incorporated in full by adding the solutions to the grammar. In the case of Scheme, there could be many possible implementations. The simplest way we have found is to add all the solutions to the library of the Scheme interpreter, add a hook non-terminal previous-solution to the grammar, and then extend the previous-procedure with the syntax to call the new solution. We assume that this syntax is provided in the problem definition. We add new solutions as follows, the new solution among other previous solutions is given a probability of in the hope that this solution will be re-used soon, and then the probabilities of the old productions of previous-solution are normalized so that they sum to . We currently use a of .

If it is impossible or difficult to add the solutions to the Scheme interpreter as in our case, then all the solutions can be added as define blocks in the beginning of the program produced. The R5RS Scheme, being an orthogonal language, will allow us to make definitions almost anywhere. However, there will be a time penalty when too many solutions are incorporated, as they will have to be repeatedly parsed by the interpreter during LSearch. To solve this problem and make the search a bit more scalable, we add a hook called solution-corpus to the grammar for definition, which can be achieved in a similar way to previous-solution. However, then, the probability of defining and using a previous solution will greatly decrease. Assume that a previous solution is defined with a probability of and called with a probability of . Since the grammar does not condition calling a previous solution on the basis of definition, the probability of a correct use is ; most of the time this logic will just generate semantically incorrect invocations of the past solutions. To fix this undesirable situation, we can use a non-terminal procedure in the definition production for the particular solution, that stores the solution in the environment that already stores variable names, defines the solution name as a variable, and in the production that calls the previous solution, selects among those solutions in the environment with uniform probability. When the solution is present we return a nil production with probability to avoid generation of redundant programs.

Since this is a complex solution, for other implementers it may be preferable to just add the new solutions to the interpreter in a format that can be executed efficiently. Many Scheme interpreters have such “compiled” data structures that the interpreter first converts to after parsing the program, and the evaluator is designed to work on those structures.

Learning programming idioms

Programmers do not only learn of concrete solutions to problems, but they also learn abstract programs, or program schemas. One way to formalize this is that they learn sentential forms. If we can extract appropriate sentential forms, we can add these to the grammar via the same algorithm that modifies production probabilities.

We have not yet implemented this update algorithm because we use a leftmost derivation which does not immediately contain appropriate sentential forms. Appropriate sentential forms can be obtained by deriving from a relevant start symbol like body (which is the body of a Scheme function definition) or expression (which is the basic symbolic expression syntax that is used many times in the Scheme grammar) until a particular level or a border in the derivation tree of a solution body or a top-level expression. Thus, some sub-expressions will remain unexpanded. To implement this in a leftmost derivation scheme as ours, we may either parse the found solutions, or change the derivation logic so that a derivation tree is constructed during search. It is also possible to construct the derivation tree from the leftmost derivation.

It is hoped that the system will be able to learn programming idioms like that of recursion patterns, or ways to use loops via this kind of update. For instance, assume that it discovered a kind of integer recursion for a problem: (define (myrec n) (if (= n 1) 0 (+ 1 (myrec (/ n 2))))). Then, the sentential forms at intermediate levels of the derivation tree for the body of the solution would abstract some of the sub-expressions, resulting for instance in (if (= variable uinteger-10) uinteger-10 (+ uinteger-10 (variable variable uinteger-10))) if we pruned one level up from each leaf of the derivation tree (since the derivation trees are usually quite unbalanced) as determined from a hand made derivation tree, which may come in handy when dealing with similar recursions. Several sentential forms can be learnt from a single solution in this fashion corresponding to different syntactic abstractions, and the algorithm for re-using previous solutions can be invoked to add them to the grammar. Otherwise, we would have to teach that kind of recursion through higher-order functions (which is also an admissible strategy).

Frequent sub-program mining

Mining the solution corpus would enhance the guiding probability distribution. Frequent sub-programs in the solution corpus, i.e., sub-programs that occur with a frequency above a given support threshold, can be added again as alternative productions to the commonly occurring non-terminal expression in the Scheme grammar. For instance, if the solution corpus contains several (lambda (x y) (* x y) ) subprograms the frequent sub-program mining would discover that and we can add it as an alternative expression to the Scheme grammar. We have not yet detailed this update algorithm but it seems reasonable and it can benefit from the well-developed field of data mining.

Discussion

What does a programmer know?

In order to encode useful information in the a priori probability distribution, we must reflect on what a human programmer knows when she writes a program. The richness of the R5RS Scheme language requires us to solve some problems like avoiding unbound references to make even the simplest program searches feasible, therefore it is important that we encode as much a priori knowledge about programming as possible into the system. Among other things, a programmer knows the following. a) The syntax of the programming language, sometimes imperfectly. Our system knows the syntax perfectly, and does not make any syntactic mistakes. b) The semantics of the programming language, again imperfectly. Our system knows little about writing semantically correct programs and often generates incorrect programs. We may need to add more semantic checks to enhance that. Our system does not know the referential semantics of the programs, only how to run the program. It may be useful for the search procedure to be informed of more semantics. Human programmers use semantic information to accelerate writing programs, for instance by using proper types. c) The running time and space complexity of the program, imperfectly. The more computer science a programmer knows, the fewer mistakes she will make. It can be argued that the programmers have partial knowledge of the halting problem, as they know many programs which will loop infinitely, and avoid writing them. They also learn which programs seem to loop indefinitely. d) Pre and post conditions. Sometimes when the programs are well-specified, the programmer understands the conditions that will be assumed prior to the running of the program and after the program finishes.

Some of this information can be incorporated in the probabilistic model, for others we may need to augment our system with relevant algorithms from fields like automated theorem proving.

Bootstrapping problems

The incremental machine learning capabilities of the Phase 1 machine in (?) will be used to calculate the conditional distributions that are necessary for the Phase 2 machine to take off. Our current implementation can find short programs, but despite any future improvements to processing speed, it may have difficulty in finding the required sort of programs without a huge training sequence that approaches those programs very closely, and it has little chance of rewriting itself unless the current implementation is further developed.

Experiments

Currently, we have made our implementation work only on toy problems. We initially solved problems for inverting mathematical functions, for identity function, division, and square-root functions to test LSearch. We then developed a simple training sequence composed of a series of problems. For each problem, we have a sequence of input and output pairs, and we use incremental operator induction (??). The operator induction, in a similar way to OOPS (?) first finds a solution to the first pair, then, the first and second pairs, and then the first three, and so forth. After each partial or complete solution to a problem, a stochastic CFG update is applied. In our first toy training sequence, we have the identity function id, the square function sqr, the addition of two variables add, a function to test if the argument is zero is0, all of which have example pairs, fourth power of a number pow4 with just example pairs, boolean nand, nor and xor functions with example pairs each, and the factorial function with pairs for inputs . The factorial function took more than a day at the fifth example () so we interrupted it as it is not feasible on a desktop machine, it did have a partial solution from the first four pairs. However, we have observed that the two update algorithms implemented work gracefully. The search time for the later problems tend to reduce compared to the first. Since we extend the grammar, sometimes a slight slowdown is experienced, which would be amortized in later problems depending on the training sequence. For instance, the sqr problem took trials while the add problem took . The solution of logical functions took longer than previous problems, but eventually we saw time reductions in them. The nand solution (not (if y x y)) took trials, the next problem nor took trials, and about speedup in Scheme cycles. Previous solutions are re-used aggressively. sqr problem is solved in the first example of and incorporating the next example takes only trials corresponding to about speedup. pow4 solution (define (pow4 x ) (define (sqr x ) (* x x)) (sqr (sqr x ) )) re-uses the sqr solution and takes trials, faster than solving sqr itself. The update algorithms help the machine learn something about semantics. While searching for sqr, at seventh iteration, the system reported error rate in evaluation of candidate programs. In the last problem, the error rate dropped to in the same iteration, although we did not consider any semantics in the update algorithms. Error reductions are seen also when code re-use is disabled.

Conclusion

We have described a stochastic CFG based incremental machine learning system targeting desktop computers in detail. We have adapted R5RS Scheme as the reference universal computer to our system. The stochastic CFG is used in sequential LSearch to calculate a priori probabilities and to generate programs efficiently avoiding syntactically incorrect programs. We derive sentences using leftmost derivation. We use a probability horizon to limit the depth of depth-first search, and we also propose using best-first search and memory-aware hybrid search. We have specialized productions for number literals, variable bindings and variable references; in particular, we avoid unbound variables in generated programs. We have proposed four update algorithms for incremental machine learning. Two of them have been implemented. To the extent that the update algorithms work, their use has been demonstrated in a toy training sequence.

The slowness of searching the factorial function made us realize that we need major improvements in both the search and the update algorithms if we would like to continue using Scheme. We have been working on a more realistic training sequence that features recursive problems, optimizing search and implementing remaining update algoritms. After that, we will extend our implementation to work on parallel hardware, implement the Phase 2 of Solomonoff’s system, and attempt incorporating features from other AGI proposals such as HSearch and Gödel Machine. We would like to implement alternative approaches and compare them, as well.

References

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