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A.2 Glossary of Specialized Terms 331

• Embodied Communication Prior: The class of prior distributions over (goal, environ-
ment pairs), that are imposed by placing an intelligent system in an environment where
most of its tasks involve controlling a spatially localized body in a complex world, and in-
teracting with other intelligent spatially localized bodies. It is hypothesized that many key
aspects of human-like intelligence (e.g. the use of different subsystems for different memory
types, and cognitive synergy between the dynamics associated with these subsystems) are
consequences of this prior assumption. This is related to the Mind-World Correspondence
Principle.
• Embodiment: Colloquially, in an OpenCog context, this usually means the use of an AI
software system to control a spatially localized body in a complex (usually 3D) world. There
are also possible "borderline cases" of embodiment, such as a search agent on the Internet.
In a sense any AI is embodied, because it occupies some physical system (e.g. computer
hardware) and has some way of interfacing with the outside world.
• Emergence: A property or pattern in a system is emergent if it arises via the combination
of other system components or aspects, in such a way that its details would be very difficult
(not necessarily impossible in principle) to predict from these other system components or
aspects.
• Emotion: Emotions are system-wide responses to the system's current and predicted state.
Dorner's Psi theory of emotion contains explanations of many human emotions in terms
of underlying dynamics and motivations, and most of these explanations make sense in a
CogPrime context, due to CogPrime's use of OpenPsi (modeled on Psi) for motivation and
action selection.
• Episodic Knowledge: Knowledge about episodes in an agent's life-history, or the life-
history of other agents. CogPrime includes a special dimensional embedding space only for
episodic knowledge, easing organization and recall.
• Evolutionary Learning: Learning that proceeds via the rough process of iterated differen-
tial reproduction based on fitness. incorporating variations of reproduced entities. MOSES
is an explicitly evolutionary-learning-based portion of CogPrime; but CogPrime's dynamics
as a whole may also be conceived as evolutionary.
• Exemplar: (in the context of imitation learning) - When the owner wants to teach an
OpenCog controlled agent a behavior by imitation, he/she gives the pet an exemplar. To
teach a virtual pet "fetch" for instance, the owner is going to throw a stick, run to it, grab
it with his/her mouth and come back to its initial position.
• Exemplar: (in the context of MOSES) – Candidate chosen as the core of a new deme, or
as the central program within a deme, to be varied by representation building for ongoing
exploration of program space.
• Explicit Knowledge Representation: Knowledge representation in which individual,
easily humanly identifiable pieces of knowledge correspond to individual elements in a knowl-
edge store (elements that are explicitly there in the software and accessible via very rapid,
deterministic operations)
• Extension: In PLN, the extension of a node refers to the instances of the category that
the node represents. In contrast is the intension.
• Fishgram (Frequent and Interesting Sub-hypergraph Mining): A pattern mining
algorithm for identifying frequent and/or interesting sub-hypergraphs in the Atomspace.
• First-Order Inference (FOI): The subset of PLN that handles Logical Links not in-
volving VariableAtoms or higher-order functions. The other aspect of PLN, Higher-Order
Inference, uses Truth Value formulas derived from First-Order Inference.

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