The OpenCog AtomSpace is an in-RAM knowledge representation (KR) database, an associated query engine and graph-re-writing system, and a rule-driven inferencing engine that can apply and manipulate sequences of rules to perform reasoning. It is a layer that sits on top of ordinary distributed (graph) databases, providing a large variety of advanced features not otherwise available.

The AtomSpace is a platform for building Artificial General Intelligence (AGI) systems. It provides the central knowledge representation component for OpenCog. As such, it is a fairly mature component, on which a lot of other systems are built, and which depend on it for stable, correct operation in a day-to-day production environment.

Data is represented in the form of graphs; more precisely, as typed, directed hypergraphs. The vertices and edges of a graph, known as "Atoms", are used to represent not only "data", but also "procedures"; thus, many graphs are executable programs as well as data structures. Associated with each Atom (each vertex or edge of the graph) is a key-value database, meant for hold transient, (rapidly) time-varying "Values", ideal for holding audio or video streams, or even GPU processing streams, such as deep-learning, dataflow networks.

The query language allows arbitrarily-complex queries to be specified, joining together arbitrary subgraphs with arbitrary relations between variables. Unlike any other graph database, the queries are themselves represented as graphs, and so can be stored in the AtomSpace. This enables numerous new possibilities. Just like ordinary databases, a a single query can find all matching graphs. Unlike others, this can be run in reverse: a single graph can be used to find all queries that would have matched it. Reverse queries are extremely common in chatbot systems, where one must fish out a limited set of rules from out of a big sea of possibilities. We believe that (as of this writing) that there is no other general-purpose database system out there that supports reverse queries.

But this is just the tip of the iceberg. There's much more. There are many features in the AtomSpace that are not found in ordinary graph databases or other systems. Thus, the AtomSpace can be thought of as a processing layer on top of existing distributed processing systems, providing a number of advanced features and capabilities.

As it turns out that knowledge representation is hard, so it also turns out that the AtomSpace is a platform for active scientific research on knowledge representation, knowledge discovery and knowledge manipulation. If you are comfortable with extremely complex mathematical theory, and just also happen to be extremely comfortable writing code, you are invited -- encouraged -- to join the project.

The AtomSpace is not intended for end-users. Rather, it is a knowledge-base platform. It is probably easiest to think of it as kind-of-like an operating system kernel: you don't need to know how it works to use it. You probably don't need to tinker with it. It just works, and it's there when you need it.

End-users and application developers will want to use one of the existing "app" subsystems, or write their own. Most of the existing AtomSpace "apps" are focused on various aspects of "Artificial General Intelligence". This includes (unsupervised) natural-language learning, machine-learning, reasoning and induction, chatbots, robot control, perceptual subsystems (vision processing, sound input), genomic and proteomic data analysis, deep-learning neural-net interfaces. These can be found in other github repos, including:

• Natural language, chat, robot control (the opencog repo)
• ROS bridge to robots, vision subsystem, chat (ghost-bridge repo)
• Unsupervised natural language learning (language-learning repo)
• Genomic, proteomic data analysis (agi-bio repo)
• Opencog on a Raspberry Pi (tinycog repo)
• Port of the MOSES machine learning to Atomese (as-moses repo)

If you are impatient, a good way to learn the AtomSpace is to run the example demos. Start with these. Then move on to the pattern-matcher examples.

The AtomSpace is a mashup of a large variety of concepts from mathematical logic, theorem proving, graph theory, database theory, type theory, model theory and knowledge representation. Its hard to provide a coherent overview without throwing around a lot of "big words" and "big concepts". We're trying to get a lot of things done, here, and there's no particularly simple or effective way of explaining it without a lot of foundational theory.

There are pre-defined Atoms for many basic knowledge-representation and computer-science concepts. These include Atoms for relations, such as similarity, inheritance and subsets; for logic, such as Boolean and, or, for-all, there-exists; for Bayesian and other probabilistic relations; for intuitionist logic, such as absence and choice; for parallel (threaded) synchronous and asynchronous execution; for expressions with variables and for lambda expressions and for beta-reduction and mapping; for uniqueness constraints, state and a messaging "blackboard"; for searching and satisfiability and graph re-writing; for the specification of types and type signatures, including type polymorphism and type construction.

Because of these many and varied Atom types, constructing graphs to represent knowledge looks kind-of-like "programming"; the programming language is informally referred to as "Atomese". It vaguely resembles a strange mash-up of SQL (due to queriability), prolog/datalog (due to the logic and reasoning components), lisp/scheme (due to lambda expressions), Haskell/CaML (due to the type system) and rule engines (due to the graph rewriting and forward/backward chaining inference systems). This "programming language" is NOT designed for use by human programmers (it is too verbose and awkward for that); it is designed for automation and machine learning. That is, like any knowledge representation system, the data and procedures encoded in "Atomese" are meant to be accessed by other automated subsystems manipulating and querying and inferencing over the data/programs.

Aside from the various advanced features, Atomese also has some very basic and familiar atom types: atoms for arithmetic operations like "plus" and "times", conditional operators, like "greater-than" or "equals", control operations like "sequential and" and "cond", as well as settable state. This makes Atomese resemble a kind of intermediate language, something you might find inside of a compiler, a bit like CIL or Gimple. However, it is both far more flexible and powerful than these, and also far less efficient. Adventurous souls are invited to create a compiler to GNU Lighting, CIL, Java bytecode or the bytecode of your choice; or maybe to a GPU backend, or even more complex data-processing systems, such as TensorFlow.

In its current form, Atomese was primarily designed to allow the generalized manipulation of large networks of probabilistic data by means of rules and inferences and reasoning systems. It extends the idea of probabilistic logic networks to a generalized system for algorithmically manipulating and managing data. The current, actual design has been heavily influenced by practical experience with natural-language processing, question answering, inferencing and the specific needs of robot control.

The use of the AtomSpace, and the operation and utility of Atomese, remains a topic of ongoing research and design experimentation, as various AI and knowledge-processing subsystems are developed. These include machine learning, natural language processing, motion control and animation, deep-learning networks and vision processing, constraint solving and planning, pattern mining and data mining, question answering and common-sense systems, and emotional and behavioral psychological systems. Each of these impose sharply conflicting requirements on the AtomSpace architecture; the AtomSpace and "Atomese" is the current best-effort KR system for satisfying all these various needs in an integrated way. It is likely to change, as the various current short-comings, design flaws, performance and scalability issues are corrected.

Active researchers and theoreticians are invited to join! The current codebase is finally clean and well-organized enough that a large number of possibilities have opened up, offering many different and exciting directions to pursue. The system is clean and flexible, and ready to move up to the next level.

One of the primary conceptual distinctions in Atomese is between "Atoms" and "Values". The distinction is made for both usability and performance. Atoms are:

• Used to represent graphs, networks, and long-term stable graphical relations.
• Indexed (by the AtomSpace), which enables the rapid search and traversal of graphs.
• Globally unique, and thus unambiguous anchor points for data.
• Immutable: can only be created and destroyed, and are effectively static and unchanging.
• Large, bulky, heavy-weight (because indexes are necessarily bulky).

By contrast, Values, and valuations in general, are:

• A way of holding on to rapidly-changing data, including streaming data.
• Hold "truth values" and "probabilities", which change over time as new evidence is accumulated.
• Provide a per-Atom key-value store (a mini noSQL database per-Atom).
• Are not indexed, and are accessible only by direct reference.
• Small, fast, fleeting (no indexes!)

Thus, for example, a piece of knowledge, or some proposition would be stored as an Atom. As new evidence accumulates, the truth value of the proposition is adjusted. Other fleeting changes, or general free-form annotations can be stored as Values. Essentially, the AtomSpace looks like a database-of-databases; each atom is a key-value database; the atoms are related to one-another as a graph. The graph is searchable, editable; it holds rules and relations and ontologies and axioms. Values are the data that stream and flow through this network, like water through pipes. Atoms define the pipes, the connectivity. Values flow and change.

The primary documentation for the atomspace and Atomese is here:

• https://wiki.opencog.org/w/AtomSpace
• https://wiki.opencog.org/w/Atomese
• https://wiki.opencog.org/w/Atom
• https://wiki.opencog.org/w/Value

The main project site is at https://opencog.org

Most users should almost surely focus their attention on one of the high-level systems built on top of the AtomSpace. The rest of this section is aimed at anyone who wants to work inside of the AtomSpace.

Most users/developers should think of the AtomSpace as being kind-of-like an operating system kernel, or the guts of a database: its complex, and you don't need to know how the innards work to use the system. These innards are best left to committed systems programmers and research scientists; there is no easy way for junior programmers to participate, at least, not without a lot of hard work and study. Its incredibly exciting, though, if you know what you're doing.

The AtomSpace is a relatively mature system, and thus fairly complex. Because other users depend on it, it is not very "hackable"; it needs to stay relatively stable. Despite this, it is simultaneously a research platform for discovering the proper way of adequately representing knowledge in a way that is useful for general intelligence. It turns out that knowledge representation is not easy. This project is a -good- excellent place to explore it, if you're interested in that sort of thing.

Experience in any of the following areas will make things easier for you; in fact, if you are good at any of these ... we want you. Bad.

• Database internals; query optimization.
• Logic programming; Prolog.
• SAT-solving; Answer Set programming; Satisfiability Modulo Theories.
• Programming language design & implementation.
• Rule engines; reasoning; inference; parsing.
• Theorem-proving systems; Type theory.
• Compiler internals; code generation; code optimization; bytecode; VM's.
• Operating systems; distributed database internals.
• GPU processing pipelines, lighting-shading pipelines, CUDA, OpenCL.
• Dataflow in GPU's for neural bets.

Basically, Atomese is a mash-up of ideas taken from all of the above fields. It's kind-of trying to do and be all of these, all at once, and to find the right balance between all of them. Again: the goal is knowledge representation for general intelligence. Building something that the AGI developers can use.

We've gotten quite far; we've got a good, clean code-base, more-or-less, and we're ready to kick it to the next level. The above gives a hint of the directions that are now open and ready to be explored.

If you don't have at least some fair grounding in one of the above, you'll be lost, and find it hard to contribute. If you do know something about any of these topics, then please dive into the open bug list. Fixing bugs is the #1 best way of learning the internals of any system.

Looking ahead, some key major projects.

One of the major development goals for the 2019-2021 time frame is to gain experience with distributed data processing. Currently, the AtomSpace uses Postgres to provide distributed, scalable storage. We're also talking about porting to Apache Ignite, or possibly some other graph database, such as Redis, Riak or Grakn, all of which also support scalable, distributed storage.

However, despite the fact that Postgres is already distributed, and fairly scalable, none of the actual users of the AtomSpace use it in it's distributed mode. Exactly why this is the case remains unclear: is it the difficulty of managing a distributed Postgres database? (I guess you have to be a good DB Admin to know how to do this?) Is it the programming API offered by the AtomSpace? Maybe it's not yet urgent for them? Would rebasing on a non-SQL database (such as Ignite, Riak, Redis or Grakn) make this easier and simpler? This is quite unclear, and quite unknown at this stage.

If a port to one of the distributed graph databases is undertaken, there are several implementation issues that need to be cleared up. One is to eliminate many usages of SetLink ( Issues #1502 and #1507 ). Another is to change the AtomTable API to look like a bunch of MemberLink's. (Currently, the AtomTable conceptually looks and behaves like a large set, which makes scaling and distribution harder than it could be). How to transform the AtomTable into a bunch of MemberLinks without blowing up RAM usage or hurting performance is unclear.

The new Value system seems to provide a very nice way of working with fast-moving high-frequency data. It seems suitable for holding on to live-video feeds and audio streams and piping them through various data-processing configurations. It looks to be a decent API for declaring the structure and topology of neural nets (e.g. TensorFlow). However, it is more-or-less unused for these tasks. Apparently, there is still some missing infrastructure, as well as some important design decisions to be made. Developers have not begun to explore the depth and breadth of this subsystem, to exert pressure on it. Ratcheting up the tension by exploring new and better ways of using and working with Values will be an important goal for the 2018-2022 time-frame.

Many important types of real-world data, include parses of natural language and biochemical processes resemble the abstract mathematical concept of "sheaves", in the sense of sheaf theory. One reason that things like deep learning and neural nets work well is because some kinds of sheaves look like tensor algebras; thus one has things like Word2Vec and SkipGram models. One reason why neural nets still stumble on natural language processing is because natural language only kind-of-ish, partly looks like a tensor algebra. But natural language looks a whole lot more like a sheaf (because things like pre-group grammars and categorial grammars "naturally" look like sheaves.) Thus, it seems promising to take the theory and all the basic concepts of deep learning and neural nets, rip out the explicit tensor-algebra in those theories, and replace them by sheaves. A crude sketch is here.

Some primitive, basic infrastructure has been built. Huge remaining work items are using neural nets to perform the tensor-like factorization of sheaves, and to redesign the rule engine to use sheaf-type theorem proving techniques.

The Atomspace runs on more-or-less any flavor of GNU/Linux. It does not run on any non-Linux operating systems (except maybe some of the BSD's). Sorry!

There are a small number of pre-requisites that must be installed before it can be built. Many users will find it easiest to use the install scripts provided in the ocpkg repo. Some users may find some success with one of the opencog Docker containers. Developers interested in working on the AtomSpace must be able to build it manually. If you can't do that, all hope is lost.

To build the OpenCog AtomSpace, the packages listed below are required. Essentially all Linux distributions will provide these packages.

• C++ utilities package.
• https://www.boost.org/ | apt-get install libboost-dev

• Build management tool; v3.0.2 or higher recommended.
• https://www.cmake.org/ | apt-get install cmake3

• Common OpenCog C++ utilities.
• https://github.com/opencog/cogutil
• It uses exactly the same build procedure as this package. Be sure to sudo make install at the end.

• Embedded scheme REPL (version 2.2.2 or newer is required)
• https://www.gnu.org/software/guile/guile.html
• For Ubuntu bionic/cosmic apt-get install guile-2.2-dev

• Test framework
• Required for running unit tests. Breaking unit tests is verboten!
• https://cxxtest.sourceforge.net/ | https://launchpad.net/~opencog-dev/+archive/ppa

The following packages are optional. If they are not installed, some optional parts of the AtomSpace will not be built. The CMake command, during the build, will be more precise as to which parts will not be built.

• C bindings for Python. (version 0.23 or higher)
• Strongly recommended, as many examples and important subsystems assume python bindings.
• https://cython.org | apt-get install cython

• Haskell bindings (experimental).
• Optional; almost no existing code makes use of Haskell.
• https://www.haskell.org/

• Distributed, multi-client networked storage.
• Needed for "remembering" between shutdowns (and for distributed AtomSpace)
• https://postgres.org | apt-get install postgresql postgresql-client libpq-dev

Be sure to install the pre-requisites first! Perform the following steps at the shell prompt:

    cd to project root dir
    mkdir build
    cd build
    cmake ..
    make -j
    sudo make install
    make -j test

Libraries will be built into subdirectories within build, mirroring the structure of the source directory root.

To build and run the unit tests, from the ./build directory enter (after building opencog as above):

    make -j test

Most tests (just not the database tests) can be run in parallel:

    make -j test ARGS=-j4

The database tests will fail if run in parallel: they will step on one-another.

After building, you MUST install the atomspace.

    sudo make install

Atomese -- that is -- all of the different Atom types, can be thought of as the primary API to the AtomSpace. Atoms can, of course, be created and manipulated with Atomese; but, in practice, programmers will work with either scheme (guile), python, C++ or haskell.

The simplest, most complete and extensive interface to Atoms and the Atomspace is via scheme, and specifically, the GNU Guile scheme implementation. An extensive set of examples can be found in the /examples/atomspace and the /examples/pattern-matcher directories.

Python is more familiar than scheme to most programmers, and it offers another way of interfacing to the atomspace. Unfortunately, it is not as easy and simple to use as scheme; it also has various technical issues. Thus, it is significantly less-used than scheme in the OpenCog project. None-the-less, it remains vital for various applications. See the /examples/python directory for how to use python with the AtomSpace.