A proof of concept for machine learning-based virtual knapping using neural networks | Scientific Reports – Nature.com

Knapped stone tools provide an abundant and long-lasting record of past behaviours and cognition of prehistoric humans on an evolutionary time scale. As a result, the stone artefact record is one of main pillars upon which our understanding of human evolution—and the evolution of human behaviour and cognition—is built. This understanding comes from building inferential links between formal and technological variation observed in the archaeological record and the behavioural, cognitive, and evolutionary processes that lead to its formation1,2,3,4,5,6,7,8. However, these links are not always apparent from the stone tools themselves, even in the earliest lithic technologies8,9,10,11,12,13,14,15,16,17,18,19,20, where the archaeological record is primarily comprised of simpler core and flake tools10,21,22,23,24. Therefore, archaeologists rely on experimental approaches to replicate stone artefacts under test conditions to determine whether factors such as function25,26, raw material availability27, skill28, technique29,30,31, cognition28,32,33,34,35,36, or culture and social learning33,34,36,37,38 played a role in the production (and subsequent discard) of knapped stone tools.

Replication experiments produce insights into the archaeological record but come with some limitations. For one, replication experiments are necessarily affected by the knapper’s own conscious and unconscious biases, their knapping experience, their expertise in the manufacture of certain tool forms, and their range of knowledge of various knapping techniques39. In addition, replication experiments cannot be easily reproduced, as many variables cannot be controlled under traditional experimental setups with modern knappers, whilst using a different knapper could introduce an additional variable not under control. Some experimenters partly address these issues by standardising the blanks (i.e. cores or flakes). Standardising raw materials can be done by sawing blocks of material into particular shapes, casting standardised shapes in materials like ceramic or glass, and more recently, by 3D milling of materials into particular shapes. In addition, some experimenters have also begun using machine-controlled knapping, focusing on searching for first principles in knapping by isolating the effect of specific variables on flake production40,41,42,43. However, standardising blanks and building machine-controlled flaking apparatuses comes with a substantial increase in the amount of time and resources required to prepare, measure, and store the experimental equipment and materials. The need for time and resources is further amplified in the first principles approach, as the number of different experiments needed to investigate the effect of multiple interacting variables is substantial.

One alternative that may circumvent the potentially vast resource and time limitations of traditional lithic experimentation entirely, or otherwise reduce costs, is to develop simulations of stone tool reduction in a digital environment. More specifically, a piece of software able to accurately virtually simulate flaking in three dimensions—comparably similar to actual knapping—would allow for fast, inexpensive, and replicable experiments. Doing so would provide a means to carry out stone tool production experiments in a controlled and reproducible environment for less time and money. The virtual knapping program would also be unaffected by any biases that individual human knappers may have in traditional experiments, and which are hard to control (given also that these biases may in some cases still be unknown).

If knapping could be done virtually, and it were—at least in some cases—a valid substitute for actual knapping, it would serve as a less resource-expensive and more feasible alternative for lithic experiments. Variation in flake shape arises out of a large constellation of parameters that are difficult to systematically test. Having a computer-based model where individual variables could be isolated and examined programmatically would not only increase the speed of what is currently a lengthy process, but could also help us further understand cause and effect relationships of different variables and the interactions between them.

In addition, there would be fewer material requirements, also in terms of long-term storage and transport, since cores could be shaped entirely within a computer, and infinitely duplicated and knapped (and re-knapped), allowing for increased dataset sizes and greater reproducibility. The software could be used to create virtual assemblages testable against actual lithic experiments, examining the influence of certain variables during lithic reduction, or more exhaustively uncovering the possible range of variability of specific reduction techniques. Moreover, the reproducibility and robusticity inherent within a well-made virtual knapping program could even counterbalance some of the error during simulation. A well-crafted virtual knapping program would also be free of human knapper biases entirely, allowing experiments undertaken with it to be more controlled, more reproducible, and perhaps more representative compared to traditional lithic experiments.

A single virtual simulation would ideally take considerably less time to reduce a set of cores than a human knapper would, and even many measurements on the resulting lithics could be automated and performed at a fraction of the time within the software, given that the products would already be digitised. It would also be much more reproducible than current knapping experiments, especially as the (virtual) knapper’s biases could be kept identical for all experiments. Currently, this is not possible to a similar degree due to factors such as differences across knappers (e.g. different skill levels, different modern traditions of knapping) and even within them (e.g. changing motivation, energy, concentration, learning during the experiment).

Here we provide an attempt for a proof of concept of a framework for a virtual knapper using a machine learning approach based on neural networks applied to programmatically created 3D inputs (cores and flakes). Our approach generated a predicted 3D flake and modified core as an output from an intact (i.e. unknapped) core. Our approach proved capable of reliably and validly predicting the length, width, volume, and overall shape of a flake removal from the surface of a core given the point of percussion. We therefore conclude that we successfully created a proof of concept—pathway—for a virtual knapper.

Predicted flakes from a more complete virtual knapper—e.g. using the approach outlined here—could form the basis for (virtual) lithic assemblages to compare with archaeological data, which could also allow archaeologists to examine how the different knapping variables affect the resulting assemblages, and to examine important inferences on the various biological, environmental, and sociocultural factors that could have played a role in the formation of the archaeological assemblages we find in the present; thus, also informing a large part of our understanding of human evolution.

Machine learning

Arguably, the most intuitive approach for virtual knapping would be physics-based simulations of conchoidal fracture—a type of fracture underlying stone knapping—that would likely require the use of mathematical methods such as finite element analysis (FEA). Although the application of FEA for virtual knapping is an important avenue to explore, simulating conchoidal fractures is a resource intensive process, and even the most recent research uses high-performance cluster computers to run simulations44,45, especially if we wished to simulate more realistic—hence complicated—knapping scenarios. Simulations wishing to examine the effects that different reduction sequences have on the resulting assemblages, or whether and how some tool forms can come about through the reduction of other forms24,29 require large amounts of flake removals and changing of knapping variables, making a FEA approach not entirely viable.

However, FEAs are only one of many approaches available to tackle the development of a virtual knapping program. To address all of the requirements we had set forth for a virtual knapper, we chose to base our method on neural networks. In a similar way as to how neural networks have allowed for drastically increasing the resolution of images in a fraction of the time it takes for computers to render them traditionally46,47, we sought for our neural network framework to predict a flake removal virtually in a fraction of the time it takes for physics-based simulations.

The primary goal for the virtual knapping program was to be a tool that could reliably perform a virtual replication experiment in a very short time without requiring large amounts of computational resources. To this end, a virtual knapper program should also be able to run on an office computer system, not unlike common agent-based modelling software tools, but it should also accurately simulate real stone flaking—focusing, as a starting goal, on hard-hammer percussion knapping (i.e. flakes removed using a hand-held hammerstone to strike the core) of a single raw material type.

Machine learning is a technique that allows computers to build a model of a set of data automatically by analysing the data and learning from it, without requiring the user to manually set-up or adjust the model’s parameters48,49. The advantage of machine learning-based modelling is that it allows for the bulk of the computational processing—i.e. the training of the machine learning model—to be completed prior to the model’s practical use; normally requiring only a very small fraction of the computing time needed to train the model in the first place.

Machine learning is a broad field, and encompasses a wide range of methods and algorithms. One such family of algorithms are artificial neural networks, which are broadly based on a simplified model of inter-connected biological neurons50,51. Artificial neural networks learn iteratively by a process known as training: the network makes predictions from the input data, then evaluates the error in prediction with a mathematical function, and adjusts its neurons and the strength of their connections in order to improve future predictions51.

Artificial neural networks have gained prominence in recent years, as they are advantageous for highly-dimensional data with large numbers of variables and complex interactions. This advantage is even more important for problems where these interactions are difficult to formulate with traditional statistical modelling, or even when we do not know which variables and interactions are important. For instance, human vision is very good at recognising objects, but programming—or mathematically describing—an algorithm to recognise objects in images would be extremely difficult when done traditionally, but can even surpass human performance in specific scenarios51,52. Applications of neural networks include autonomous driving53, recommendation algorithms54, and computer-aided medical diagnosis55,56.

One disadvantage of machine learning, however, is that it often requires a large amount of training data. For our envisioned framework, we required 3D models of a large number of core and flake combinations (i.e. a flake and the core from which it was removed). Such a dataset is not (yet) publicly available, and we did not have the resources to create it ourselves. Moreover, for the initial evaluation of our approach, we sought to avoid adding unnecessary complexity by limiting the shape of the initial cores in our dataset, since—due to the bias-variance trade-off—additional variability in a dataset usually requires a larger dataset for the model not to overfit to the particular training dataset, performing poorly with new data51. In the meantime, we opted instead for programmatically-generated cores and flakes. These have the advantage of being quickly generated with a constrained amount of variability, and if a machine learning model can successfully predict the flakes from this data set, then predicting flakes from a larger more varied data set could likely only be a question of additional training data, as the cores and flakes we used here were based on empirical findings from previous machine-controlled knapping experiments40. Unlike previous machine-controlled knapping experiments, however, our flakes were not restricted to a single removal for each core, as we also removed flakes from already knapped cores during data generation (see Fig. 1).

Figure 1

Example of a core and removed flakes from the input dataset. (a) The training dataset consists of pairs of flaked cores (blue) and their matching flakes removals (red), oriented such that together they represent the complete core prior to flaking, much like a refit. (b) Some of the flake and core pairs were generated in different stages of reduction (see “Methods”). This is an illustration of a generated reduction sequence. Note that, in the dataset, each flake has a matching modified core model as well.

Image-to-image translation

Neural network algorithms that predict one 3D shape from another are rare or remain limited in their application57,58. However, predictions from 2D datasets are far more common. Here, we circumvent this problem by representing our 3D datasets as a two dimensional surfaces to apply image-to-image translation.

Image-to-image translation is a task in which a neural network model converts (or translates) one type of picture to another type altogether. Examples include converting a picture of a landscape taken during the day into a picture of the same landscape at night, converting a line drawing into a photorealistic image, predicting the colourised version of a black and white image, or converting a diagram of a façade into a photorealistic image of a building.

However, since our input consisted of 3D objects, not (2D) images, we needed to encode the information of the relevant surfaces of the 3D cores and flakes into an image. In order to accomplish this task, we made use of depth maps on our 3D cores and flakes.

Depth maps

Depth maps (or z-buffers) are images that encode the distance (or depth) between a view point in 3D space from where the depth map is captured, and the 3D surfaces visible from that same point (see Fig. 2). Depth maps are very similar in concept to digital elevation models, which capture the elevation of a portion of the Earth’s surface (a 3D property), and encode it into a 2D image whose colours (or raster values) represent different elevations. Depth maps can be conceptualised as a less-restricted form of elevation maps, with the depth map’s maximum allowed depth analogous to the lowest surface elevation of a digital elevation model, and the distance between the surface of the object and the view point as analogous to the elevation of the terrain’s surface.

Figure 2

Example of the standard orientation for depth map capture as displayed using a 3-D model of a knapped core, which has a near-perfectly flat platform surface. Note that the platform surface is aligned horizontally with respect to the depth map. Note also that, though difficult to see, the point of percussion is aligned to be in the exact centre of the image. (a) 3-D mesh of the core with camera (left) to capture depth map image. (b) Depth map rendered into a 3-D surface superimposed over the original core mesh. The depth map’s frame is located at the maximum depth we set when captured. Anything deeper than the maximum depth is rendered as pure black in the image. (c) Resulting depth map image.

Conditional generative adversarial network (CGAN)

The conditional generative adversarial network (CGAN) architecture consists of a discriminator model, which learns to distinguish between the real outputs of our dataset and fake outputs created by a generator model, the second part of the CGAN. The generator model learns to create outputs that are realistic enough to fool the discriminator into believing they are real based on the input images. The training process becomes an iterative adversarial contest in which, as the training progresses, the generator becomes better at fooling the discriminator, and the discriminator, in turn, becomes better at detecting the generator’s predicted output. The training ideally culminates in a generator model trained to create outputs that are as close to the real outputs as possible, and able to provide highly accurate predictions under non-training circumstances.

The CGAN performs image-to-image translation by mapping the unmodified core depth maps (input) to the resulting flake volume depth maps (output); what is, in essence, an abstraction of the task of predicting flakes from cores. The predicted flake depth maps obtained as outputs can be then used to obtain the modified core depth map, and with these, calculate the 3D flakes and modified cores using the 3D model of the unmodified core (which would be available in a standard use-case).

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