OpenCog is an open-source artificial intelligence project that aims to build a unified, large-scale artificial general intelligence (AGI) system. It is a highly modular and flexible system that is built around a core database of interconnected nodes and links called the AtomSpace, which represents knowledge and information in the system.
The AtomSpace is the central hub of the OpenCog system, and it is used to store and manipulate a wide variety of different types of information, including concepts, facts, beliefs, goals, emotions, and relationships. The AtomSpace is implemented as a distributed, graph-based database, and it is designed to be scalable, flexible, and efficient.
OpenCog includes a wide range of tools and components for various AI and machine learning tasks, such as natural language processing, reasoning, planning, learning, and decision-making. These tools and components are implemented as a set of interconnected modules and libraries, and they can be combined and used in various ways to build complex AI systems and applications.
Artificial general intelligence (AGI) is a type of artificial intelligence that is capable of understanding or learning any intellectual task that a human being can. It is a hypothetical form of intelligence that is characterized by a wide range of abilities and skills, including the ability to learn, reason, plan, solve problems, think abstractly, comprehend complex ideas, learn quickly, and learn from experience.
AGI is often contrasted with narrow or specialized artificial intelligence, which is designed to perform a specific task or set of tasks, but is not capable of adapting to new situations or learning new things. Some examples of narrow AI include virtual assistants, facial recognition software, and self-driving cars.
The development of AGI is considered to be a long-term goal of artificial intelligence research, and it is a subject of much debate and speculation within the AI community. Some researchers believe that AGI is achievable and that it could have a significant impact on society, while others are more skeptical and believe that it may be difficult or impossible to achieve.
The OpenCog Core system is a software framework that provides tools and infrastructure for building intelligent systems. It is designed to be modular and extensible, allowing developers to build upon and customize the system for their specific needs.
Atomspace is a central component of the OpenCog Core system. It is a graph database that stores information about the system’s knowledge and processes. The Atomspace allows for the representation and manipulation of complex data structures, including concepts, relationships, and patterns.
The CogServer is a networked server that runs OpenCog processes and algorithms. It is responsible for coordinating the various components of the system and providing an interface for interacting with the system.
There are other components that make up the OpenCog Core system, such as the Pattern Miner, which is a tool for discovering patterns in data, and the RelEx natural language processing system, which allows for the understanding and processing of human language. Overall, the OpenCog Core system provides the necessary infrastructure and tools for building intelligent systems that can process and manipulate complex data.
- wiki.opencog.org/w/OpenCog Core .. the kernel of MindOS
- wiki.opencog.org/w/AtomSpace .. central knowledge representation system, hypergraphs interface
- wiki.opencog.org/w/CogServer .. the scheduler executes the system processes (MindAgents)
OpenCog is a framework for building artificial general intelligence (AGI). It is an open source project with the goal of creating a machine that can think, reason, learn, and communicate like a human.
The OpenCog Core system is the central part of OpenCog, which consists of three main components: AtomSpace, CogServer, and MindAgents.
AtomSpace is a graph database designed to represent and manipulate large-scale knowledge and cognitive processes. Atoms are the basic units of information stored in AtomSpace, and they can be connected together to form complex structures.
CogServer is a daemon that runs in the background and handles communication between different components of OpenCog. It is responsible for executing cognitive processes and coordinating the flow of information between different parts of the system.
MindAgents are software modules that perform specific tasks within the OpenCog system, such as learning, planning, and decision-making. They operate on the information stored in AtomSpace and communicate with other parts of the system through CogServer.
DimensionalEmbedding is a feature of AtomSpace that allows Atoms to be embedded in n-dimensional space. This can be useful for representing and manipulating information in a more intuitive and visual way, as it allows Atoms to be represented as points in space rather than just as abstract symbols.
- wiki.opencog.org/w/OpenCogPrime:DimensionalEmbedding .. the embedding of Atoms into n-dimensional space (in AtomSpace)
OpenCog is an open-source artificial intelligence project that aims to create intelligent agents that can learn and adapt to their environment. One component of the OpenCog system is the Atomspace, a knowledge representation and reasoning system that stores information as a network of interconnected “atoms.”
The DimensionalEmbedding module in OpenCog is used to embed the atoms in the Atomspace into n-dimensional space, allowing for more efficient querying and manipulation of the atoms. This embedding is based on the relationships between the atoms and their associated data, and allows for the atoms to be represented as points in a high-dimensional space.
The Economic Attention Allocation module in OpenCog is used to manage the competition for attention among the atoms in the Atomspace. In the OpenCog system, each atom has an associated “attention value,” which reflects the importance or relevance of the atom. The Economic Attention Allocation module helps to prioritize the processing and manipulation of atoms based on their attention value, ensuring that the system is able to focus on the most important or relevant information.
- wiki.opencog.org/w/Economic Attention Allocation .. the Competition for Attention, each Atom has an Attention Value attached to it
OpenCog is an open-source artificial intelligence project that aims to build a unified, large-scale artificial general intelligence (AGI) system. The OpenCog embodiment is a set of components within the OpenCog system that are designed to control an avatar, which is a virtual or physical embodiment of an agent (such as a robot or character in a virtual world). These components can be used to give the avatar sensory input, process that input using various AI techniques, and generate appropriate actions and responses. The goal of the embodiment components is to allow the avatar to interact with the environment in a natural and intelligent way, and to enable the avatar to learn and adapt over time.
MOSES is a machine learning algorithm that is part of the OpenCog project. It stands for Meta-Optimizing Semantic Evolutionary Search, and it is a type of evolutionary computation that uses a semantic representation of the search space and a probabilistic model of the problem being solved.
MOSES works by iteratively searching for a solution to a problem by building and evaluating a set of candidate solutions, called “demes”, using a combination of evolutionary search and probabilistic modeling. The algorithm uses a semantic representation of the search space, which allows it to capture the high-level structure and relationships in the problem being solved. It also uses probabilistic modeling to evaluate the quality of the candidate solutions and guide the search process.
MOSES has been used in a variety of applications, including natural language processing, computer vision, and robotics, and it has been shown to be effective at finding good solutions to complex problems.
- wiki.opencog.org/w/Meta-Optimizing Semantic Evolutionary Search | Category:MOSES .. representation-building and probabilistic modeling
RelEx (Semantic Relationship Extractor) is a natural language processing tool that is part of the OpenCog project. It is a semantic dependency relationship extractor that is built on the Carnegie-Mellon Link Grammar parser, which is a tool for analyzing the grammatical structure of English sentences.
RelEx is designed to extract semantic relationships from text, such as subject-verb-object (SVO) relationships, and to represent those relationships in a format that can be used by other OpenCog tools for natural language processing and artificial intelligence applications. The output of RelEx is a set of typed relationships between words in the input text, which can be used to represent the meaning of the text and to facilitate further processing and analysis.
RelEx is implemented as a set of C++ libraries that can be integrated with other OpenCog tools and systems, and it is used in a variety of natural language processing tasks, including question answering, text summarization, and machine translation.
- wiki.opencog.org/w/RelEx Semantic Relationship Extractor | Category:RelEx .. semantic dependency relationship extractor built on the Carnegie-Mellon Link Grammar parser
SegSim (Segment Similarity) is a natural language generation module that is part of the OpenCog project. It is a tool for generating text that is similar in style and content to a given input text.
SegSim works by dividing the input text into segments, or small chunks of text, and then using machine learning techniques to analyze the patterns and relationships within those segments. It uses this analysis to build a model of the input text, which it can then use to generate new text that is similar in style and content to the input.
The output of SegSim is a series of text segments that can be combined to form a coherent piece of text. It has been used in a variety of natural language processing tasks, including text summarization, machine translation, and text generation. SegSim is implemented as a set of C++ libraries that can be integrated with other OpenCog tools and systems.
- wiki.opencog.org/w/SegSim .. natural-language generation module
OpenCogPrime:DialogueManagement is a dialog manager that is part of the OpenCog project. It is a tool for managing and controlling conversation between agents, such as between a human and a virtual assistant.
The OpenCogPrime:DialogueManagement system uses Rhetorical Structure Theory (RST) to analyze the structure and content of the conversation, and to generate appropriate responses based on that analysis. RST is a theory of text and discourse structure that describes how different parts of a text or conversation are related to one another, and how those relationships contribute to the overall meaning and purpose of the text or conversation.
The OpenCogPrime:DialogueManagement system is implemented as a set of C++ libraries that can be integrated with other OpenCog tools and systems, and it is used in a variety of natural language processing and artificial intelligence tasks, including dialogue systems, question answering, and machine translation.
- wiki.opencog.org/w/OpenCogPrime:DialogueManagement .. dialog manager using Rhetorical Structure Theory
OpenPsi is a cognitive planning and motivation system that is part of the OpenCog project. It is designed to enable intelligent agents, such as virtual assistants or robots, to plan and pursue goals, and to make decisions based on their emotions and motivations.
OpenPsi is based on the concept of a “Cognitive Schematic”, which is a representation of an agent’s goals, emotions, and motivations as a network of interconnected nodes. The Cognitive Schematic is used to represent the current state of the agent and to guide the planning and decision-making process.
OpenPsi uses a variety of AI techniques, including probabilistic reasoning and machine learning, to evaluate and update the Cognitive Schematic in real-time, and to generate appropriate actions and responses based on the agent’s current state and goals. It is implemented as a set of C++ libraries that can be integrated with other OpenCog tools and systems, and it is used in a variety of artificial intelligence and robotics applications.
- wiki.opencog.org/w/OpenPsi (Embodiment) | Category:OpenPsi .. “Cognitive Schematic” planner, emotion and motivation model
Fishgram is an algorithm that is part of the OpenCog project. It is a tool for finding patterns in knowledge represented as a conjunction of links, using a technique known as “frequent subgraph mining”.
In the OpenCog system, knowledge is represented as a graph of interconnected nodes and links, which can be used to represent the relationships and connections between different pieces of information. Fishgram is designed to analyze this graph and identify patterns within it by searching for frequently occurring subgraphs, or small clusters of interconnected nodes and links.
Fishgram works by constructing a graph of all of the possible subgraphs within a larger graph, and then using statistical techniques to identify the subgraphs that occur most frequently. These frequently occurring subgraphs are considered to be patterns within the larger graph, and can be used to facilitate further analysis and understanding of the knowledge represented in the graph. Fishgram is implemented as a set of C++ libraries that can be integrated with other OpenCog tools and systems.
- wiki.opencog.org/w/Fishgram .. algorithm for finding patterns in knowledge as a conjunction of links (AndLink)
Probabilistic Logic Networks (PLN) is a computational approach to uncertain inference that is part of the OpenCog project. It is a framework for representing and manipulating uncertain knowledge using probabilistic logical statements, and for making inferences and predictions based on that knowledge.
In PLN, knowledge is represented as a set of logical statements that express relationships between different concepts or entities. These statements can be probabilistic, meaning that they are associated with a probability or degree of confidence, which reflects the uncertainty or reliability of the knowledge. PLN uses a combination of logical reasoning and probabilistic techniques to manipulate and combine these statements, and to make inferences and predictions based on the resulting knowledge.
PLN is implemented as a set of C++ libraries that can be integrated with other OpenCog tools and systems, and it is used in a variety of artificial intelligence and machine learning applications, including natural language processing, reasoning, and decision-making.
- wiki.opencog.org/w/Probabilistic Logic Networks | Category:PLN .. computational approach to uncertain inference
12 Scheme shell
The OpenCog shell is a command-line interface (CLI) for the OpenCog system that allows users to interact with the system and manipulate the contents of an AtomSpace, which is a database of interconnected nodes and links that represents knowledge and information in the OpenCog system.
The OpenCog shell is implemented in the Scheme programming language, which is a dialect of Lisp. Scheme is a functional programming language that is known for its simplicity and flexibility, and it is well-suited for use in artificial intelligence and machine learning applications.
The OpenCog shell allows users to enter Scheme code directly, which can be used to manipulate the contents of the AtomSpace and to perform various tasks, such as querying the AtomSpace, adding or modifying atoms, and evaluating expressions. The shell also includes a set of built-in functions and commands that can be used to perform common tasks and interact with the AtomSpace in a more convenient way.