any process closely related to intelligence; but more often it’s used specifically to refer to
more abstract reasoning/learning/etc, as distinct from lower-level perception and action.
• Cognitive Architecture: This refers to the logical division of an AI system like OpenCog
into interacting parts and processes representing different conceptual aspects of intelligence.
328 A Glossary
It’s different from the software architecture, though of course certain cognitive architectures
and certain software architectures fit more naturally together.
• Cognitive Cycle: The basic ”loop” of operations that an OpenCog system, used to control
an agent interacting with a world, goes through rapidly each ”subjective moment.” Typically
a cognitive cycle should be completed in a second or less. It minimally involves perceiving
data from the world, storing data in memory, and deciding what if any new actions need
to be taken based on the data perceived. It may also involve other processes like deliberative
thinking or metacognition. Not all OpenCog processing needs to take place within a
cognitive cycle.
• Cognitive Schematic: An implication of the form ”Context AND Procedure IMPLIES
goal”. Learning and utilization of these is key to CogPrime’s cognitive process.
• Cognitive Synergy: The phenomenon by which different cognitive processes, controlling a
single agent, work together in such a way as to help each other be more intelligent. Typically,
if one has cognitive processes that are individually susceptible to combinatorial explosions,
cognitive synergy involves coupling them together in such a way that they can help one
another overcome each other’s internal combinatorial explosions. The CogPrime design is
reliant on the hypothesis that its key learning algorithms will display dramatic cognitive
synergy when utilized for agent control in appropriate environments.
• CogPrime : The name for the AGI design presented in this book, which is designed specifically
for implementation within the OpenCog software framework (and this implementation
is OpenCogPrime).
• CogServer: A piece of software, within OpenCog, that wraps up an Atomspace and a
number of MindAgents, along with other mechanisms like a Scheduler for controlling the
activity of the MindAgents, and code for important and exporting data from the Atomspace.
• Cognitive Equation: The principle, identified in Ben Goertzel’s 1994 book "Chaotic
Logic", that minds are collections of pattern-recognition elements, that work by iteratively
recognizing patterns in each other and then embodying these patterns as new system elements.
This is seen as distinguishing mind from ”self-organization” in general, as the latter
is not so focused on continual pattern recognition. Colloquially this means that ”a mind is
a system continually creating itself via recognizing patterns in itself.”
• Combo: The programming language used internally by MOSES to represent the programs
it evolves. SchemaNodes may refer to Combo programs, whether the latter are learned via
MOSES or via some other means. The textual realization of Combo resembles LISP with
less syntactic sugar. Internally a Combo program is represented as a program tree.
• Composer: In the PLN design, a rule is denoted a composer if it needs premises for
generating its consequent. See generator.
• CogBuntu: an Ubuntu Linux remix that contains all required packages and tools to test
and develop OpenCog.
• Concept Creation: A general term for cognitive processes that create new ConceptNodes,
PredicateNodes or concept maps representing new concepts.
• Conceptual Blending: A process of creating new concepts via judiciously combining
pieces of old concepts. This may occur in OpenCog in many ways, among them the explicit
use of a ConceptBlending MindAgent, that blends two or more ConceptNodes into a new
one.
• Confidence: A component of an OpenCog/PLN TruthValue, which is a scaling into the
interval [0,1] of the weight of evidence associated with a truth value. In the simplest case
(of a probabilistic Simple Truth Value), one uses confidence c = n / (n+k), where n is
A.2 Glossary of Specialized Terms 329
the weight of evidence and k is a parameter. In the case of an Indefinite Truth Value, the
confidence is associated with the width of the probability interval.
• Confidence Decay: The process by which the confidence of an Atom decreases over time,
as the observations on which the Atom’s truth value is based become increasingly obsolete.
This may be carried out by a special MindAgent. The rate of confidence decay is subtle and
contextually determined, and must be estimated via inference rather than simply assumed
a priori.
• Consciousness: CogPrime is not predicated on any particular conceptual theory of consciousness.
Informally, the AttentionalFocus is sometimes referred to as the ”conscious”
mind of a CogPrime system, with the rest of the Atomspace as ”unconscious” but this is
just an informal usage, not intended to tie the CogPrime design to any particular theory of
consciousness. The primary originator of the CogPrime
design (Ben Goertzel) tends toward panpsychism, as it happens.
• Context: In addition to its general common-sensical meaning, in CogPrime the term Context
also refers to an Atom that is used as the first argument of a ContextLink. The second
argument of the ContextLink then contains Links or Nodes, with TruthValues calculated
restricted to the context defined by the first argument. For instance, (ContextLink USA
(InheritanceLink person obese )).
• Core: The MindOS portion of OpenCog, comprising the Atomspace, the CogServer, and
other associated ”infrastructural” code.
• Corrective Learning: When an agent learns how to do something, by having another
agent explicitly guide it in doing the thing. For instance, teaching a dog to sit by pushing
its butt to the ground.
• CSDLN: (Compositional Spatiotemporal Deep Learning Network): A hierarchical pattern
recognition network, in which each layer corresponds to a certain spatiotemporal granularity,
the nodes on a given layer correspond to spatiotemporal regions of a given size, and the
children of a node correspond to sub-regions of the region the parent corresponds to. Jeff
Hawkins’s HTM is one example CSDLN, and Itamar Arel’s DeSTIN (currently used in
OpenCog) is another.
• Declarative Knowledge: Semantic knowledge as would be expressed in propositional or
predicate logic facts or beliefs.
• Deduction: In general, this refers to the derivation of conclusions from premises using
logical rules. In PLN in particular, this often refers to the exercise of a specific inference
rule, the PLN Deduction rule (A → B, B → C, therefore A→ C)
• Deep Learning: Learning in a network of elements with multiple layers, involving feedforward
and feedback dynamics, and adaptation of the links between the elements. An example
deep learning algorithm is DeSTIN, which is being integrated with OpenCog for perception
processing.
• Defrosting: Restoring, into the RAM portion of an Atomspace, an Atom (or set thereof)
previously saved to disk.
• Demand: In CogPrime’s OpenPsi subsystem, this term is used in a manner inherited from
the Psi model of motivated action. A Demand in this context is a quantity whose value the
system is motivated to adjust. Typically the system wants to keep the Demand between
certain minimum and maximum values. An Urge develops when a Demand deviates from
its target range.
• Deme: In MOSES, an ”island” of candidate programs, closely clustered together in program
space, being evolved in an attempt to optimize a certain fitness function. The idea is that
330 A Glossary
within a deme, programs are generally similar enough that reasonable syntax-semantics
correlation obtains.
• Derived Hypergraph: The SMEPH hypergraph obtained via modeling a system in terms
of a hypergraph representing its internal states and their relationships. For instance, a
SMEPH vertex represents a collection of internal states that habitually occur in relation to
similar external situations. A SMEPH edge represents a relationship between two SMEPH
vertices (e.g. a similarity or inheritance relationship). The terminology ”edge /vertex” is
used in this context, to distinguish from the ”link / node” terminology used in the context
of the Atomspace.
• DeSTIN – Deep SpatioTemporal Inference Network: A specific CSDLN created by
Itamar Arel, tested on visual perception, and appropriate for integration within CogPrime.
• Dialogue: Linguistic interaction between two or more parties. In a CogPrime context, this
may be in English or another natural language, or it may be in Lojban or Psynese.
• Dialogue Control: The process of determining what to say at each juncture in a dialogue.
This is distinguished from the linguistic aspects of dialogue, language comprehension and
language generation. Dialogue control applies to Psynese or Lojban, as well as to human
natural language.
• Dimensional Embedding: The process of embedding entities from some non-dimensional
space (e.g. the Atomspace) into an n-dimensional Euclidean space. This can be useful in an
AI context because some sorts of queries (e.g. ”find everything similar to X”, ”find a path
between X and Y”) are much faster to carry out among points in a Euclidean space, than
among entities in a space with less geometric structure.
• Distributed Atomspace: An implementation of an Atomspace that spans multiple computational
processes; generally this is done to enable spreading an Atomspace across multiple
machines.
• Dual Network: A network of mental or informational entities with both a hierarchical
structure and a heterarchical structure, and an alignment among the two structures so that
each one helps with the maintenance of the other. This is hypothesized to be a critical
emergent structure, that must emerge in a mind (e.g. in an Atomspace) in order for it to
achieve a reasonable level of human-like general intelligence (and possibly to achieve a high
level of pragmatic general intelligence in any physical environment).
• Efficient Pragmatic General Intelligence: A formal, mathematical definition of general
intelligence (extending the pragmatic general intelligence), that ultimately boils down to:
the ability to achieve complex goals in complex environments using limited computational
resources (where there is a specifically given weighting function determining which goals
and environments have highest priority). More specifically, the definition weighted-sums the
system’s normalized goal-achieving ability over (goal, environment pairs), and where the
weights are given by some assumed measure over (goal, environment pairs), and where the
normalization is done via dividing by the (space and time) computational resources used
for achieving the goal.
• Elegant Normal Form (ENF): Used in MOSES, this is a way of putting programs in
a normal form while retaining their hierarchical structure. This is critical if one wishes
to probabilistically model the structure of a collection of programs, which is a meaningful
operation if the collection of programs is operating within a region of program space where
syntax-semantics correlation holds to a reasonable degree. The Reduct library is used to
place programs into ENF.
A.2 Glossary of Specialized Terms 331
• Embodied Communication Prior: The class of prior distributions over (goal, environment
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 interacting
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 lifehistory
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 differential
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 knowledge
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 involving
VariableAtoms or higher-order functions. The other aspect of PLN, Higher-Order
Inference, uses Truth Value formulas derived from First-Order Inference.
332 A Glossary
• Forgetting: The process of removing Atoms from the in-RAM portion of Atomspace, when
RAM gets short and they are judged not as valuable to retain in RAM as other Atoms. This
is commonly done using the LTI values of the Atoms (removing lowest LTI-Atoms, or more
complex strategies involving the LTI of groups of interconnected Atoms). May be done by
a dedicated Forgetting MindAgent. VLTI may be used to determine the fate of forgotten
Atoms.
• Forward Chainer: A control mechanism (MindAgent) for PLN inference, that works by
taking existing Atoms and deriving conclusions from them using PLN rules, and then iterating
this process. The goal is to derive new Atoms that are interesting according to some
given criterion.
• Frame2Atom: A simple system of hand-coded rules for translating the output of RelEx2Frame
(logical representation of semantic relationships using FrameNet relationships) into Atoms.
• Freezing: Saving Atoms from the in-RAM Atomspace to disk.
• General Intelligence: Often used in an informal, commonsensical sense, to mean the
ability to learn and generalize beyond specific problems or contexts. Has been formalized
in various ways as well, including formalizations of the notion of ”achieving complex goals
in complex environments” and ”achieving complex goals in complex environments using
limited resources.” Usually interpreted as a fuzzy concept, according to which absolutely
general intelligence is physically unachievable, and humans have a significant level of general
intelligence, but far from the maximally physically achievable degree.
• Generalized Hypergraph: A hypergraph with some additional features, such as links
that point to links, and nodes that are seen as ”containing” whole sub-hypergraphs. This is
the most natural and direct way to mathematically/visually model the Atomspace.
• Generator: In the PLN design, a rule is denoted a generator if it can produce its consequent
without needing premises (e.g. LookupRule, which just looks it up in the AtomSpace). See
composer.
• Global, Distributed Memory: Memory that stores items as implicit knowledge, with
each memory item spread across multiple components, stored as a pattern of organization
or activity among them.
• Glocal Memory: The storage of items in memory in a way that involves both localized
and global, distributed aspects.
• Goal: An Atom representing a function that a system (like OpenCog) is supposed to spend
a certain non-trivial percentage of its attention optimizing. The goal, informally speaking,
is to maximize the Atom’s truth value.
• Goal, Implicit: A goal that an intelligent system, in practice, strives to achieve; but that
is not explicitly represented as a goal in the system’s knowledge base.
• Goal, Explicit: A goal that an intelligent system explicitly represents in its knowledge
base, and expends some resources trying to achieve. Goal Nodes (which may be Nodes or,
e.g. ImplicationLinks) are used for this purpose in OpenCog.
• Goal-Driven Learning: Learning that is driven by the cognitive schematic i.e. by the quest
of figuring out which procedures can be expected to achieve a certain goal in a certain sort
of context.
• Grounded SchemaNode: See SchemaNode, Grounded.
• Hebbian Learning: An aspect of Attention Allocation, centered on creating and updating
HebbianLinks, which represent the simultaneous importance of the Atoms joined by the
HebbianLink.
A.2 Glossary of Specialized Terms 333
• Hebbian Links: Links recording information about the associative relationship (cooccurrence)
between Atoms. These include symmetric and asymmetric HebbianLinks.
• Heterarchical Network: A network of linked elements in which the semantic relationships
associated with the links are generally symmetrical (e.g. they may be similarity links, or
symmetrical associative links). This is one important sort of subnetwork of an intelligent
system; see Dual Network.
• Hierarchical Network: A network of linked elements in which the semantic relationships
associated with the links are generally asymmetrical, and the parent nodes of a node have
a more general scope and some measure of control over their children (though there may be
important feedback dynamics too). This is one important sort of subnetwork of an intelligent
system; see Dual Network.
• Higher-Order Inference (HOI): PLN inference involving variables or higher-order functions.
In contrast to First-Order Inference (FOI).
• Hillclimbing: A general term for greedy, local optimization techniques, including some
relatively sophisticated ones that involve ”mildly nonlocal” jumps.
• Human-Level Intelligence: General intelligence that’s ”as smart as” human general intelligence,
even if in some respects quite unlike human intelligence. An informal concept,
which generally doesn’t come up much in CogPrime work, but is used frequently by some
other AI theorists.
• Human-Like Intelligence: General intelligence with properties and capabilities broadly
resembling those of humans, but not necessarily precisely imitating human beings.
• Hypergraph: A conventional hypergraph is a collection of nodes and links, where each
link may span any number of nodes. OpenCog makes use of generalized hypergraphs (the
Atomspace is one of these).
• Imitation Learning: Learning via copying what some other agent is observed to do.
• Implication: Often refers to an ImplicationLink between two PredicateNodes, indicating
an (extensional, intensional or mixed) logical implication.
• Implicit Knowledge Representation: Representation of knowledge via having easily
humanly identifiable pieces of knowledge correspond to the pattern of organization and/or
dynamics of elements, rather than via having individual elements correspond to easily humanly
identifiable pieces of knowledge.
• Importance: A generic term for the Attention Values associated with Atoms. Most commonly
these are STI (short term importance) and LTI (long term importance) values. Other
importance values corresponding to various different time scales are also possible. In general
an importance value reflects an estimate of the likelihood an Atom will be useful to the
system over some particular future time-horizon. STI is generally relevant to processor time
allocation, whereas LTI is generally relevant to memory allocation.
• Importance Decay: The process of Atom importance values (e.g. STI and LTI) decreasing
over time, if the Atoms are not utilized. Importance decay rates may in general be contextdependent.
• Importance Spreading: A synonym for Importance Updating, intended to highlight the
similarity with ”activation spreading” in neural and semantic networks.
• Importance Updating: The CIM-Dynamic that periodically (frequently) updates the STI
and LTI values of Atoms based on their recent activity and their relationships.
• Imprecise Truth Value: Peter Walley’s imprecise truth values are intervals [L,U], interpreted
as lower and upper bounds of the means of probability distributions in an envelope
334 A Glossary
of distributions. In general, the term may be used to refer to any truth value involving
intervals or related constructs, such as indefinite probabilities.
• Indefinite Probability: An extension of a standard imprecise probability, comprising a
credible interval for the means of probability distributions governed by a given second-order
distribution.
• Indefinite Truth Value: An OpenCog TruthValue object wrapping up an indefinite probability
• Induction: In PLN, a specific inference rule (A → B, A → C, therefore B → C). In general,
the process of heuristically inferring that what has been seen in multiple examples, will be
seen again in new examples. Induction in the broad sense, may be carried out in OpenCog
by methods other than PLN induction. When emphasis needs to be laid on the particular
PLN inference rule, the phrase ”PLN Induction” is used.
• Inference: Generally speaking, the process of deriving conclusions from assumptions. In
an OpenCog context, this often refers to the PLN inference system. Inference in the broad
sense is distinguished from general learning via some specific characteristics, such as the
intrinsically incremental nature of inference: it proceeds step by step.
• Inference Control: A cognitive process that determines what logical inference rule (e.g.
what PLN rule) is applied to what data, at each point in the dynamic operation of an
inference process.
• Integrative AGI: An AGI architecture, like CogPrime, that relies on a number of different
powerful, reasonably general algorithms all cooperating together. This is different from an
AGI architecture that is centered on a single algorithm, and also different than an AGI
architecture that expects intelligent behavior to emerge from the collective interoperation
of a number of simple elements (without any sophisticated algorithms coordinating their
overall behavior).
• Integrative Cognitive Architecture: A cognitive architecture intended to support integrative
AGI.
• Intelligence: An informal, natural language concept. ”General intelligence” is one slightly
more precise specification of a related concept; ”Universal intelligence” is a fully precise
specification of a related concept. Other specifications of related concepts made in the
particular context of CogPrime research are the pragmatic general intelligence and the
efficient pragmatic general intelligence.
• Intension: In PLN, the intention of a node consists of Atoms representing properties of
the entity the node represents.
• Intentional memory: A system’s knowledge of its goals and their subgoals, and associations
between these goals and procedures and contexts (e.g. cognitive schematics).
• Internal Simulation World: A simulation engine used to simulate an external environment
(which may be physical or virtual), used by an AGI system as its ”mind’s eye” in order
to experiment with various action‘ q sequences and envision their consequences, or observe
the consequences of various hypothetical situations. Particularly important for dealing with
episodic knowledge.
• Interval Algebra: Allen Interval Algebra, a mathematical theory of the relationships between
time intervals. CogPrime utilizes a fuzzified version of classic Interval Algebra.
• IRC Learning (Imitation, Reinforcement, Correction): Learning via interaction with
a teacher, involving a combination of imitating the teacher, getting explicit reinforcement
signals from the teacher, and having one’s incorrect or suboptimal behaviors guided toward
betterness by the teacher in real-time. This is a large part of how young humans learn.
A.2 Glossary of Specialized Terms 335
• Knowledge Base: A shorthand for the totality of knowledge possessed by an intelligent
system during a certain interval of time (whether or not this knowledge is explicitly represented).
Put differently: this is an intelligence’s total memory contents (inclusive of all
types of memory) during an interval of time.
• Language Comprehension: The process of mapping natural language speech or text into
a more ”cognitive”, largely language-independent representation. In OpenCog this has been
done by various pipelines consisting of dedicated natural language processing tools, e.g. a
pipeline: text → Link Parser → RelEx → RelEx2Frame → Frame2Atom Atomspace; and
alternatively a pipeline Link Parser → Link2Atom → Atomspace. It would also be possible
to do language comprehension purely via PLN and other generic OpenCog processes,
without using specialized language processing tools.
• Language Generation: The process of mapping (largely language-independent) cognitive
content into speech or text. In OpenCog this has been done by various pipelines consisting of
dedicated natural language processing tools, e.g. a pipeline: Atomspace → NLGen → text;
or more recently Atomspace → Atom2Link → surface realization → text. It would also be
possible to do language generation purely via PLN and other generic OpenCog processes,
without using specialized language processing tools.
• Language Processing: Processing of human language is decomposed, in CogPrime, into
Language Comprehension, Language Generation, and Dialogue Control.
• Learning: In general, the process of a system adapting based on experience, in a way that
increases its intelligence (its ability to achieve its goals). The theory underlying CogPrime
doesn’t distinguish learning from reasoning, associating, or other aspects of intelligence.
• Learning Server: In some OpenCog configurations, this refers to a software server that
performs ”offline” learning tasks (e.g. using MOSES or hillclimbing), and is in communication
with an Operational Agent Controller software server that performs real-time agent
control and dispatches learning tasks to and receives results from the Learning Server.
• Linguistic Links: A catch-all term for Atoms explicitly representing linguistic content,
e.g. WordNode, SentenceNode, CharacterNode.
• Link: A type of Atom, representing a relationship among one or more Atoms. Links and
Nodes are the two basic kinds of Atoms.
• Link Parser: A natural language syntax parser, created by Sleator and Temperley at
Carnegie-Mellon University, and currently used as part of OpenCogPrime’s natural language
comprehension and natural language generation system.
• Link2Atom: A system for translating link parser links into Atoms. It attempts to resolve
precisely as much ambiguity as needed in order to translate a given assemblage of link parser
links into a unique Atom structure.
• Lobe: A term sometimes used to refer to a portion of a distributed Atomspace that lives
in a single computational process. Often different lobes will live on different machines.
• Localized Memory: Memory that stores each item using a small number of closelyconnected
elements.
• Logic: In an OpenCog context, this usually refers to a set of formal rules for translating
certain combinations of Atoms into ”conclusion” Atoms. The paradigm case at present is the
PLN probabilistic logic system, but OpenCog can also be used together with other logics.
• Logical Links: Any Atoms whose truth values are primarily determined or adjusted via
logical rules, e.g. PLN’s InheritanceLink, SimilarityLink, ImplicationLink, etc. The term
isn’t usually applied to other links like HebbianLinks whose semantics isn’t primarily logic-
336 A Glossary
based, even though these other links can be processed via (e.g. PLN) logical inference via
interpreting them logically.
• Lojban: A constructed human language, with a completely formalized syntax and a highly
formalized semantics, and a small but active community of speakers. In principle this seems
an extremely good method for communication between humans and early-stage AGI systems.
• Lojban++: A variant of Lojban that incorporates English words, enabling more flexible
expression without the need for frequent invention of new Lojban words.
• Long Term Importance (LTI): A value associated with each Atom, indicating roughly
the expected utility to the system of keeping that Atom in RAM rather than saving it to
disk or deleting it. It’s possible to have multiple LTI values pertaining to different time
scales, but so far practical implementation and most theory has centered on the option of
a single LTI value.
• LTI: Long Term Importance
• Map: A collection of Atoms that are interconnected in such a way that they tend to be
commonly active (i.e. to have high STI, e.g. enough to be in the AttentionalFocus, at the
same time).
• Map Encapsulation: The process of automatically identifying maps in the Atomspace,
and creating Atoms that ”encapsulate” them; the Atom encapsulation a map would link to
all the Atoms in the map. This is a way of making global memory into local memory, thus
making the system’s memory glocal and explicitly manifesting the ”cognitive equation.”
This may be carried out via a dedicated MapEncapsulation MindAgent.
• Map Formation: The process via which maps form in the Atomspace. This need not be
explicit; maps may form implicitly via the action of Hebbian Learning. It will commonly
occur that Atoms frequently co-occurring in the AttentionalFocus, will come to be joined
together in a map.
• Memory Types: In CogPrime
this generally refers to the different types of memory that are embodied in different data
structures or processes in the CogPrime
architecture, e.g. declarative (semantic), procedural, attentional, intentional, episodic, sensorimotor.
• Mind-World Correspondence Principle: The principle that, for a mind to display
efficient pragmatic general intelligence relative to a world, it should display many of the
same key structural properties as that world. This can be formalized by modeling the world
and mind as probabilistic state transition graphs, and saying that the categories implicit
in the state transition graphs of the mind and world should be inter-mappable via a highprobability
morphism.
• Mind OS: A synonym for the OpenCog Core.
• MindAgent: An OpenCog software object, residing in the CogServer, that carries out
some processes in interaction with the Atomspace. A given conceptual cognitive process
(e.g. PLN inference, Attention allocation, etc.) may be carried out by a number of different
MindAgents designed to work together.
• Mindspace: A model of the set of states of an intelligent system as a geometrical space,
imposed by assuming some metric on the set of mind-states. This may be used as a tool for
formulating general principles about the dynamics of generally intelligent systems.
• Modulators: Parameters in the Psi model of motivated, emotional cognition, that modulate
the way a system perceives, reasons about and interacts with the world.
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. 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 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.
338 A Glossary
• Pattern: In a CogPrime context, the term ”pattern” is generally used to refer to a process
that produces some entity, and is judged simpler than that entity.
• Pattern Mining: Pattern mining is the process of extracting an (often large) number of
patterns from some body of information, subject to some criterion regarding which patterns
are of interest. Often (but not exclusively) it refers to algorithms that are rapid or ”greedy”,
finding a large number of simple patterns relatively inexpensively.
• Pattern Recognition: The process of identifying and representing a pattern in some
substrate (e.g. some collection of Atoms, or some raw perceptual data, etc.).
• Patternism: The philosophical principle holding that, from the perspective of engineering
intelligent systems, it is sufficient and useful to think about mental processes in terms of
(static and dynamical) patterns.
• Perception: The process of understanding data from sensors. When natural language is
ingested in textual format, this is generally not considered perceptual. Perception may be
taken to encompass both pre-processing that prepares sensory data for ingestion into the
Atomspace, processing via specialized perception processing systems like DeSTIN that are
connected to the Atomspace, and more cognitive-level process within the Atomspace that
is oriented toward understanding what has been sensed.
• Piagetan Stages: A series of stages of cognitive development hypothesized by developmental
psychologist Jean Piaget, which are easy to interpret in the context of developing
CogPrime systems. The basic stages are: Infantile, Pre-operational, Concrete Operational
and Formal. Post-formal stages have been discussed by theorists since Piaget and seem
relevant to AGI, especially advanced AGI systems capable of strong self-modification.
• PLN: short for Probabilistic Logic Networks
• PLN, First-Order: See First-Order Inference
• PLN, Higher-Order: See Higher-Order Inference
• PLN Rules: A PLN Rule takes as input one or more Atoms (the ”premises”, usually Links),
and output an Atom that is a ”logical conclusion” of those Atoms. The truth value of the
consequence is determined by a PLN Formula associated with the Rule.
• PLN Formulas: A PLN Formula, corresponding to a PLN Rule, takes the TruthValues
corresponding to the premises and produces the TruthValue corresponding to the conclusion.
A single Rule may correspond to multiple Formulas, where each Formula deals with a
different sort of TruthValue.
• Pragmatic General Intelligence: A formalization of the concept of general intelligence,
based on the concept that general intelligence is the capability to achieve goals in environments,
calculated as a weighted average over some fuzzy set of goals and environments.
• Predicate Evaluation: The process of determining the Truth Value of a predicate, embodied
in a PredicateNode. This may be recursive, as the predicate referenced internally by a
Grounded PredicateNode (and represented via a Combo program tree) may itself internally
reference other PredicateNodes.
• Probabilistic Logic Networks (PLN): A mathematical and conceptual framework for
reasoning under uncertainty, integrating aspects of predicate and term logic with extensions
of imprecise probability theory. OpenCogPrime’s central tool for symbolic reasoning.
• Procedural Knowledge: Knowledge regarding which series of actions (or action-combinations)
are useful for an agent to undertake in which circumstances. In CogPrime these may be
learned in a number of ways, e.g. via PLN or via Hebbian learning of Schema Maps, or via
explicit learning of Combo programs via MOSES or hillclimbing. Procedures are represented
as SchemaNodes or Schema Maps.
A.2 Glossary of Specialized Terms 339
• Procedure Evaluation/Execution: A general term encompassing both Schema Execution
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. evolutionary
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 maximize
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).
340 A Glossary
• Request for Services (RFS): In CogPrime’s Goal-driven action system, a RFS is a
package sent from a Goal Atom to another Atom, offering it a certain amount of STI
currency if it is able to deliver the goal what it wants (an increase in its Truth Value).
RFS’s may be passed on, e.g. from goals to subgoals to sub-subgoals, but eventually an
RFS reaches a Grounded SchemaNode, and when the corresponding Schema is executed,
the payment implicit in the RFS is made.
• Robot Preschool: An AGI Preschool in our physical world, intended for robotically embodied
AGIs.
• Robotic Embodiment: Using an AGI to control a robot. The AGI may be running on
hardware physically contained in the robot, or may run elsewhere and control the robot via
networking methods such as wifi.
• Scheduler: Part of the CogServer that controls which processes (e.g. which MindAgents)
get processor time, at which point in time.
• Schema: A ”script” describing a process to be carried out. This may be explicit, as in the
case of a GroundedSchemaNode, or implicit, as the case in Schema maps or ungrounded
SchemaNodes.
• Schema Encapsulation: The process of automatically recognizing a Schema Map in an
Atomspace, and creating a Combo (or other) program embodying the process carried out
by this Schema Map, and then storing this program in the Procedure Repository and
associating it with a particular SchemaNode. This translates distributed, global procedural
memory into localized procedural memory. It’s a special case of Map Encapsulation.
• Schema Execution: The process of ”running” a Grounded Schema, similar to running a
computer program. Or, phrased alternately: The process of executing the Schema referenced
by a Grounded SchemaNode. This may be recursive, as the predicate referenced internally by
a Grounded SchemaNode (and represented via a Combo program tree) may itself internally
reference other Grounded SchemaNodes.
• Schema, Grounded: A Schema that is associated with a specific executable program
(either a Combo program or, say, C++ code)
• Schema Map: A collection of Atoms, including SchemaNodes, that tend to be enacted
in a certain order (or set of orders), thus habitually enacting the same process. This is a
distributed, globalized way of storing and enacting procedures.
• Schema, Ungrounded: A Schema that represents an abstract procedure, not associated
with any particular executable program.
• Schematic Implication: A general, conceptual name for implications of the form ((Context
AND Procedure) IMPLIES Goal)
• SegSim: A name for the main algorithm underlying the NLGen language generation software.
The algorithm is based on segmenting a collection of Atoms into small parts, and
matching each part against memory to find, for each part, cases where similar Atomcollections
already have known linguistic expression.
• Self-Modification: A term generally used for AI systems that can purposefully modify
their core algorithms and representations. Formally and crisply distinguishing this sort of
”strong self-modification” from ”mere” learning is a tricky matter.
• Sensorimotor: Pertaining to sensory data, motoric actions, and their combination and
intersection.
• Sensory: Pertaining to data received by the AGI system from the outside world. In a
CogPrime system that perceives language directly as text, the textual input will generally
A.2 Glossary of Specialized Terms 341
not be considered as ”sensory” (on the other hand, speech audio data would be considered
as ”sensory”).
• Short Term Importance: A value associated with each Atom, indicating roughly the
expected utility to the system of keeping that Atom in RAM rather than saving it to disk
or deleting it. It’s possible to have multple LTI values pertaining to different time scales,
but so far practical implementation and most theory has centered on the option of a single
LTI value.
• Similarity: a link type indicating the probabilistic similarity between two different Atoms.
Generically this is a combination of Intensional Similarity (similarity of properties) and
Extensional Similarity (similarity of members).
• Simple Truth Value: a TruthValue involving a pair (s,d) indicating strength (e.g. probability
or fuzzy set membership) and confidence d. d may be replaced by other options such
as a count n or a weight of evidence w.
• Simulation World: See Internal Simulation World
• SMEPH (Self-Modifying Evolving Probabilistic Hypergraphs): a style of modeling
systems, in which each system is associated with a derived hypergraph
• SMEPH Edge: A link in a SMEPH derived hypergraph, indicating an empirically observed
relationship (e.g. inheritance or similarity) between two
• SMEPH Vertex: A node in a SMEPH derived hypergraph representing a system, indicating
a collection of system states empirically observed to arise in conjunction with the same
external stimuli
• Spatial Inference: PLN reasoning including Atoms that explicitly reference spatial relationships
• Spatiotemporal Inference: PLN reasoning including Atoms that explicitly reference spatial
and temporal relationships
• STI: Shorthand for Short Term Importance
• Strength: The main component of a TruthValue object, lying in the interval [0,1], referring
either to a probability (in cases like InheritanceLink, SimilarityLink, EquivalenceLink,
ImplicationLink, etc.) or a fuzzy value (as in MemberLink, EvaluationLink).
• Strong Self-Modification: This is generally used as synonymous with Self-Modification,
in a CogPrime context.
• Subsymbolic: Involving processing of data using elements that have no correspondence to
natural language terms, nor abstract concepts; and that are not naturally interpreted as
symbolically ”standing for” other things. Often used to refer to processes such as perception
processing or motor control, which are concerned with entities like pixels or commands like
”rotate servomotor 15 by 10 degrees theta and 55 degrees phi.” The distinction between
”symbolic” and ”subsymbolic” is conventional in the history of AI, but seems difficult to
formalize rigorously. Logic-based AI systems are typically considered ”symbolic”, yet
• Supercompilation: A technique for program optimization, which globally rewrites a program
into a usually very different looking program that does the same thing. A prototype
supercompiler was applied to Combo programs with successful results.
• Surface Realization: The process of taking a collection of Atoms and transforming them
into a series of words in a (usually natural) language. A stage in the overall process of
language generation.
• Symbol Grounding: The mapping of a symbolic term into perceptual or motoric entities
that help define the meaning of the symbolic term. For instance, the concept ”Cat” may be
342 A Glossary
grounded by images of cats, experiences of interactions with cats, imaginations of being a
cat, etc.
• Symbolic: Pertaining to the formation or manipulation of symbols, i.e. mental entities that
are explicitly constructed to represent other entities. Often contrasted with subsymbolic.
• Syntax-Semantics Correlation: In the context of MOSES and program learning more
broadly, this refers to the property via which distance in syntactic space (distance between
the syntactic structure of programs, e.g. if they’re represented as program trees) and semantic
space (distance between the behaviors of programs, e.g. if they’re represented as
sets of input/output pairs) are reasonably well correlated. This can often happen among
sets of programs that are not too widely dispersed in program space. The Reduct library
is used to place Combo programs in Elegant Normal Form, which increases the level of
syntax-semantics corellation between them. The programs in a single MOSES deme are
often closely enough clustered together that they have reasonably high syntax-semantics
correlation.
• System Activity Table: An OpenCog component that records information regarding
what a system did in the past.
• Temporal Inference: Reasoning that heavily involves Atoms representing temporal information,
e.g. information about the duration of events, or their temporal relationship
(before, after, during, beginning, ending). As implemented in CogPrime, makes use of an
uncertain version of Allen Interval Algebra.
• Truth Value: A package of information associated with an Atom, indicating its degree
of truth. SimpleTruthValue and IndefiniteTruthValue are two common, particular kinds.
Multiple truth values associated with the same Atom from different perspectives may be
grouped into CompositeTruthValue objects.
• Universal Intelligence: A technical term introduced by Shane Legg and Marcus Hutter,
describing (roughly speaking) the average capability of a system to carry out computable
goals in computable environments, where goal/environment pairs are weighted via the length
of the shortest program for computing them.
• Urge: In OpenPsi, an Urge develops when a Demand deviates from its target range.
• Very Long Term Importance (VLTI): A bit associated with Atoms, which determines
whether, when an Atom is forgotten (removed from RAM), it is saved to disk (frozen) or
simply deleted.
• Virtual AGI Preschool: A virtual world intended for AGI teaching/training/learning,
bearing broad resemblance to the preschool environments used for young humans.
• Virtual Embodiment: Using an AGI to control an agent living in a virtual world or game
world, typically (but not necessarily) a 3D world with broad similarity to the everyday
human world.
• Webmind AI Engine: A predecessor to the Novamente Cognition Engine and OpenCog,
developed 1997-2001 – with many similar concepts (and also some different ones) but quite
different algorithms and software architecture
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