Glossary of Specialized Terms 337
MOSES (Meta-Optimizing Semantic Evolutionary Search): An algorithm for proce- dure 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 gen- eration, 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, math- ematically, can be thought of as "O-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. Grounded- PredicateNodes 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 Cog- Prime 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 soft- ware 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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