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A.2 Glossary of Specialized Terms 339

• Procedure Evaluation/Execution: A general term encompassing both Schema Execu-
tion and Predicate Evaluation, both of which are similar computational processes involving
manipulation of Combo trees associated with ProcedureNodes.
• Procedure Learning: Learning of procedural knowledge, based on any method, e.g. evo-
lutionary learning (e.g. MOSES), inference (e.g. PLN), reinforcement learning (e.g. Hebbian
learning).
• Procedure Node: A SchemaNode or PredicateNode
• Psi: A model of motivated action and emotion, originated by Dietrich Dorner and further
developed by Joscha Bach, who incorporated it in his proto-AGI system MicroPsi. OpenCog-
Prime’s motivated-action component, OpenPsi, is roughly based on the Psi model.
• Psynese: A system enabling different OpenCog instances to communicate without using
natural language, via directly exchanging Atom subgraphs, using a special system to map
references in the speaker’s mind into matching references in the listener’s mind.
• Psynet Model: An early version of the theory of mind underlying CogPrime, referred to
in some early writings on the Webmind AI Engine and Novamente Cognition Engine. The
concepts underlying the psynet model are still part of the theory underlying CogPrime, but
the name has been deprecated as it never really caught on.
• Reasoning: See inference
• Reduct: A code library, used within MOSES, applying a collection of hand-coded rewrite
rules that transform Combo programs into Elegant Normal Form.
• Region Connection Calculus: A mathematical formalism describing a system of basic
operations among spatial regions. Used in CogPrime as part of spatial inference to provide
relations and rules to be referenced via PLN and potentially other subsystems.
• Reinforcement Learning: Learning procedures via experience, in a manner explicitly
guided to cause the learning of procedures that will maximize the system’s expected future
reward. CogPrime does this implicitly whenever it tries to learn procedures that will maxi-
mize some Goal whose Truth Value is estimated via an expected reward calculation (where
“reward” may mean simply the Truth Value of some Atom defined as “reward”). Goal-driven
learning is more general than reinforcement learning as thus defined; and the learning that
CogPrime does, which is only partially goal-driven, is yet more general.
• RelEx: A software system used in OpenCog as part of natural language comprehension, to
map the output of the link parser into more abstract semantic relationships. These more
abstract relationships may then be entered directly into the Atomspace, or they may be
further abstracted before being entered into the Atomspace, e.g. by RelEx2Frame rules.
• RelEx2Frame: A system of rules for translating RelEx output into Atoms, based on the
FrameNet ontology. The output of the RelEx2Frame rules make use of the FrameNet library
of semantic relationships. The current (2012) RelEx2Frame rule-based is problematic and
the RelEx2Frame system is deprecated as a result, in favor of Link2Atom. However, the
ideas embodied in these rules may be useful; if cleaned up the rules might profitably be
ported into the Atomspace as ImplicationLinks.
• Representation Building: A stage within MOSES, wherein a candidate Combo program
tree (within a deme) is modified by replacing one or more tree nodes with alternative tree
nodes, thus obtaining a new, different candidate program within that deme. This process
currently relies on hand-coded knowledge regarding which types of tree nodes a given tree
node should be experimentally replaced with (e.g. an AND node might sensibly be replaced
with an OR node, but not so sensibly replaced with a node representing a ”kick” action).

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