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A.2 Glossary of Specialized Terms 337

• MOSES (Meta-Optimizing Semantic Evolutionary Search): An algorithm for procedure learning, which in the current implementation learns programs in the Combo language. MOSES is an evolutionary learning system, which differs from typical genetic programming systems in multiple aspects including: a subtler framework for managing multiple "demes" or "islands" of candidate programs; a library called Reduct for placing programs in Elegant Normal Form; and the use of probabilistic modeling in place of, or in addition to, mutation and crossover as means of determining which new candidate programs to try.
• Motoric: Pertaining to the control of physical actuators, e.g. those connected to a robot. May sometimes be used to refer to the control of movements of a virtual character as well.
• Moving Bubble of Attention: The Attentional Focus of a CogPrime system.
• Natural Language Comprehension: See Language Comprehension
• Natural Language Generation: See Language Generation
• Natural Language Processing (NLP): See Language Processing
• NLGen: Software for carrying out the surface realization phase of natural language generation, via translating collections of RelEx output relationships into English sentences. Was made functional for simple sentences and some complex sentences; not currently under active development, as work has shifted to the related Atom2Link approach to language generation.
• Node: A type of Atom. Links and Nodes are the two basic kinds of Atoms. Nodes, mathematically, can be thought of as "0-ary" links. Some types of Nodes refer to external or mathematical entities (e.g. WordNode, NumberNode); others are purely abstract, e.g. a ConceptNode is characterized purely by the Links relating it to other atoms. GroundedPredicateNodes and GroundedSchemaNodes connect to explicitly represented procedures (sometimes in the Combo language); ungrounded PredicateNodes and SchemaNodes are abstract and, like ConceptNodes, purely characterized by their relationships.
• Node Probability: Many PLN inference rules rely on probabilities associated with Nodes. Node probabilities are often easiest to interpret in a specific context, e.g. the probability P(cat) makes obvious sense in the context of a typical American house, or in the context of the center of the sun. Without any contextual specification, P(A) is taken to mean the probability that a randomly chosen occasion of the system's experience includes some instance of A.
• Novamente Cognition Engine (NCE): A proprietary proto-AGI software system, the predecessor to OpenCog. Many parts of the NCE were open-sourced to form portions of OpenCog, but some NCE code was not included in OpenCog; and now OpenCog includes multiple aspects and plenty of code that was not in NCE.
• OpenCog: A software framework intended for development of AGI systems, and also for narrow-AI application using tools that have AGI applications. Co-designed with the CogPrime cognitive architecture, but not exclusively bound to it.
• OpenCog Prime (OCP): The implementation of the CogPrime cognitive architecture within the OpenCog software framework.
• OpenPsi: CogPrime's architecture for motivation-driven action selection, which is based on adapting Dorner's Psi model for use in the OpenCog framework.
• Operational Agent Controller (OAC): In some OpenCog configurations, this is a software server containing a CogServer devoted to real-time control of an agent (e.g. a virtual world agent, or a robot). Background, offline learning tasks may then be dispatched to other software processes, e.g. to a Learning Server.

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