# Full Text: Aligning Active Inference Ontology to SUMO > Extracted from `2024_OntologySUMO.pdf` --- ## Page 1 Aligning Active Inference Ontology to SUMO Authors: David S. Douglass1,2 https://orcid.org/0000-0001-7894-8019; Adam Pease3 https://orcid.org/0000-0001-9772-1266; Daniel Friedman1 https://orcid.org/0000-0001-6232-9096; Jessica Balbuena1 https://orcid.org/0000-0003-2741-4476; Rhea Chokhalingam1 https://orcid.org/0009-0007-2061-3022; Ana Magdelena Hurtado4,1 https://orcid.org/0000-0003-4064-1876; Maria Luiza Iennaco5,1 https://orcid.org/0000-0002-5407-4852; V. Bleu Knight1 https://orcid.org/0000-0002-9894-1989; Ali Rahmjoo1 https://orcid.org/0000-0002-3244-7419; Paulo Duare Andrade Sayeg5,1 https://orcid.org/0009-0006-0658-7684; Jakub Smékal7 https://orcid.org/0000-0003-4989-4968; Dean Tickles8,1 https://orcid.org/0000-0003-2213-0773; Alex Vyatkin1 https://orcid.org/0000-0003- 1306-4620 1 Active Inference Institute 2 American Society for Cybernetics 3 Naval Postgraduate School 4 Arizona State University 5 University of São Paulo 6 University of Oxford 7 Stanford University 8 Professional Initiatives Programming DOI: 10.5281/zenodo.11459323 Contact: DavidSDouglass@ActiveInference.Institute ABSTRACT Active Inference and its associated concepts (notably predictive coding, predictive processing, and the Free Energy Principle) act instrumentally as a conceptual system that provides structure to a large and growing number of scientific and humanistic fields; and realistically as candidate real-world properties and their mutual affordances. In 2021, the Active Inference Institute adopted SUMO, the Suggested Upper Merged Ontology, as the ontology with which to map its inventory of topics and their interrelations, along with the corre- sponding multilingual technical terms. This document aims to act as a scaffolding, displaying a tentative alignment of Active Inference terms with SUMO entities, in order to facilitate the anticipated process of rigorously mapping Active Inference to SUMO. For a subset of Active Inference terms, we identify published SUMO files (i.e. .kif files) likely to contain correctly delineated mappings of those topics; likely SUMO supersets of the desired targets; some exemplary SUMO subsets of the targets; and indications of relationships among the identified SUMO topics. KEYWORDS: Active Inference Ontology, SUMO Ontology ## Page 2 OUTLINE OF CONTENTS 1. Notes on Presentation of Active Inference Data. 2. Notes on Presentation of SUMO Data. 3. An “alignment” table. This shows, for many core terms used in the Active Inference Ontology, areas of SUMO that are likely to contain corresponding topics; or which are portions of SUMO where topics should be created to more nearly match Active Inference concepts. 1. Notes on Presentation of Active Inference Data. Some of the following items occur only in the external full table of alignments of Active Inference to SUMO, i.e. “Active Inference Topics Aligned to SUMO.xlsx” Concept. A concept is a persisting process of reproducing (copying, rehearsing, …) a relation, either perfectly or with constrained mutation. See Pask and Scott (1973). Topic. A topic is a shared concept. Distinguished from term. Term. A term is a linguistic artifact, written or spoken, in a specific natural or formal language, used to indicate (activate, represent, …) a topic. Module. Active Inference topics are grouped into a small number of modules (also called sub-theories or subontologies). These are high-level clusters of topics that show a high degree of inter- definition, interdependence, and collocation in texts. In a typical course, members of a module would taught together. As of 2024, the inventory of modules in the Active Inference ontology is rather informal. The module, with some of their notable members, are: Action Action Planning, Action Prediction, Agency, Behavior, Policy, Policy selection, Preference Agents in the Niche Affordance, Agent, Cognition, Culture, Ensemble, Generative Model, Generative Process, Narrative, Niche, Non-Equilibrium Steady State, Particle, Recognition Model… Bayesian Statistics Accuracy, Ambiguity, Bayes Theorem, Bayesian Inference, Belief, Belief updating, Complexity, Data, Expectation, Inference, Information, Learning, Outcome, Posterior… Free Energy Active Inference, Epistemic value, Expected Free Energy, Free Energy Principle, Generalized Free Energy, Pragmatic Value, Process Theory, Variational, Variational Free Energy Information Cue, Information Geometry Markov Partitioning Active States, Blanket State, External State, Friston Blanket, Hidden State, Internal State, Markov Blanket, Markov Decision Process, Sense State Perception Attention, Evidence, Novelty, Observation, Regime of Attention, Salience Systems Hierarchical Model, Living system, Multi-scale system List. Each individual topics has been assigned to one of three “lists.” This is a porous partition, created to help organize exposition. Core topics should be mastered in a general course in Active Inference concepts, aimed at upper-division college undergraduates in technical curricula. A textbook might spend from half a page to one section of a chapter, on each core topic. Omitting or failing to master a core topic can be regarded as a failure of presentation or learning. Each entailed topic is essential to understanding at least one core topics. An instructor or textbook author can assume that some of these topics are already understood by a typical adequately- prepared student. Nonetheless, usage of each entailed topic should be indicated, i.e. “defined,” in at least as much detail as is typically found in a technical glossary in a textbook or review article. Source: Definitions of core terms. ## Page 3 Supplemental topics define notions typical of “extra credit” or “advanced topics” material. As conceived by the Active Inference Ontology working group, adding a supplemental topic to a presentation expands the scope of ideas covered, but need not be considered essential to mastery of Active Inference and its major cohorts (notably Predictive Process and the Free Energy Principle). Definitions. We restrict ourselves to two senses or three senses of a given topic name. Deeper examination may show that some of these “senses” should be separated into distinct topics (which may be analogs of one another). Expositions. The best single source for understanding Active Inference is textbook “Active Inference: The Free Energy Principle in Mind, Brain, and Behavior" by Thomas Parr, Giovanni Pezzulo, and Karl J. Friston (2022, MIT Press). This work is accessible online at https://direct.mit.edu/books/oa-monograph/5299/Active-InferenceThe-Free-Energy-Principle-in-Mind A suitable instructor-run course might run for half a year. An example is the online course “Active Inference Textbook Group,” anchored in Parr, Pezullo, Friston. Two presentations of this course are viewable at https://www.youtube.com/watch?v=G9GfOMjF4g0&list=PLNm0u2n1Iwdob1pSM1q9yzDboE3uvQqq- and https://www.youtube.com/watch?v=3U8AXcIaUFI&list=PLNm0u2n1Iwdpm1wcq9DOGSdKDDvnEt_xG ## Page 4 2. Notes on Presentation of SUMO Data. (1) General remarks. A 2001 overview of SUMO can be found at https://www.adampease.org/FOIS.pdf Unlike taxonomies or knowledge graphs, SUMO concepts are defined axiomatically in SUMO. The identifiers mean only what their formal axioms say that they mean in mathematical logic, without recourse to human interpretation. The existing set of 100,000 formal statements can be added to in order to define additional terms specific to any domain. A playlists of Adam Pease's videos, discussing "how to do SUMO," appears at https://www.youtube.com/watch?v=SkruxPmN0kk&list=PLpBQIgki3izcrbaiuOH_OWWguY_duSumA&ab_channel=OntologyTalkwithAdamPease (2) Class and relation names. SUMO identifiers use the convention of CamelCase. Relations that are not functions have an initial lowercase letter. All other identifiers begin with an uppercase letter. Sumo variable names are prefixed with “?”. In this document, we often flag terms that do not yet belong to SUMO by prefixing an asterisk (as in “*ActiveState”). (3) Column “SUMO superclass, instanceOf, superrelation.” In the tables aligning Active Inference to SUMO terms (both the full table presented separately and the except below), this column situates a conceptual entity in the whole SUMO ontology by showing the term’s place one or more “upward” or “including” or contextual or more-general relationships: superclass. (A neologism introduced in this present document.) See the inverse of superclass, i.e. “subclass,” below. instanceOf. SUMO describes the instance relation as follows: "An object is an instance of a Class if it is included in that Class. An individual may be an instance of many classes, some of which may be subclasses of others. Thus, there is no assumption in the meaning of instance about specificity or uniqueness.") superrelation. (A neologism introduced in this present document.) See the inverse of superrelation, i.e. “subrelation,” below. (4) Column “SUMO subclasses, subrelations.” This refines the reader’s appreciation of a SUMO entity name, by giving terms that are “included-in” or less-general than the named target entity: Subclass. The subclass relation resembles the word “subset” (as used commonparlance to show relations among concepts, e.g. by means of Venn diagrams), but there is no arbitrariness, nor exceptions to inheritance: The attributes and relations of a superclasses are inherited by their subclasses. SUMO describes the subclass relation as follows: "’ CLASS1 is a subclass of CLASS2’ means that every instance of CLASS1 is also an instance of CLASS2. A Class may have multiple superclasses and subclasses." subrelation. SUMO describes the subrelation relation as follows: "’REL1 is a subrelation of REL2’ means that ‘every tuple of REL1 is also a tuple of REL2.’ In other words, if the Relation REL1 holds for some arguments arg_1, arg_2, ... arg_n, then the Relation REL2 holds for the same arguments. A consequence of this is that a Relation and its subrelations must have the same number of arguments.” One further consequence is to distinguish some specfic subrelations from relevant metaphorical relations (in which an argument in the target of the metaphor may range freely across possible values without invalidating the statement of metaphoricality). ## Page 5 (5) Column “Domains.” This column breaks down a relation that’s relevant to the Active Inference term in question, by going through the related terms, and stating the domain (type, class) of each “term of the relation,” from first onward. SUMO describes the domain relation as follows: "Provides a computationally and heuristically convenient mechanism for declaring the argument types of a given relation. The formula (domain REL INT CLASS) means that the INT'th element of each tuple in the relation REL must be an instance of CLASS. Specifying argument types is very helpful in maintaining ontologies. Representation systems can use these specifications to classify terms and check integrity constraints. If the restriction on the argument type of a Relation is not captured by a Class already defined in the ontology, one can specify a Class compositionally with the functions UnionFn, IntersectionFn, etc.") (6) Column “Other SUMO relations.” This column states a variety of relations among SUMO topics and terms. Sometimes the intention is simply to motivate the suggested aligment with Active Inference. ## Page 6 3. SELECTED ACTIVE INFERENCE TOPICS ALIGNED WITH SUMO CLASSES AND RELATIONS Topic Module Proposed Definition 1 Proposed Definition 2 SUMO superclass, instanceOf, su- perrelation Domains, Roles Other SUMO relations abstractCounter- part (abstractCounterpart ?AB ?PHYS) relates a Physical entity to an Abstract one which is an idealized model in some di- mension of the Physical entity. Example: an Abstract GraphNode could be stated to be the counterpart of an actual Computer in a ComputerNet- work. abstractCounterpart is a subrelation of represents. abstractCounterpart is an instance of binary predicate. The number 1 argument of ab- stractCounterpart is an instance of abstract. The number 2 argument of ab- stractCounterpart is an instance of physical. Accuracy Bayesian Statistics Broad sense: how “close to the mark” an Estimator is. Narrow sense: the expected or realized extent of Surprise on an estimation, usually about Sense State reflecting the Recognition density *Accuracy is a subclass of Psycho- logicalAttribute An instance of *Accuracy is the number 2 argument of ab- stractCounterpart. *Accuracy is internally related to TruthValue Action Action Broad sense: The dynamics, mechanisms, and measure- ments of Behavior Narrow sense: The sequence of Active States enacted by an Agent via Policy selection from Affordance *AbstractAction is a subclass of In- tentionalProcess. Action Planning Action The selection of an Affordance based upon Inference of Ex- pected Free Energy Planning is a subclass of Intention- alPsychologicalProcess. Action Prediction Action Inference on current and future Expectation of Action Predicting is a subclass of in- tentional psychological pro- cess Active actionTendency is an instance of bi- nary relation. The number 1 argument of ac- tionTendency is an instance of emotional state. The number 2 argument of ac- tionTendency is a subclass of emotional behavioral process. @active is internally related to actionTendency. Active Inference Free Energy Active Inference is a Process Theory related to Free Energy Principle. Judging is a subclass of Selecting *ActiveInference is internally related to Judging. Active States Markov Par- titioning In the Friston Blanket formal- ism, the Blanket State are the Sense State (incoming Sensory *ActiveState is a subclass of PhysiologicProcess. ## Page 7 input) and Active States (out- going influence of Policy selec- tion) Active Vision refers to the process of visual perceptions, in terms of oculo- motor Sensorimotor Behavior and Cognitive System Genera- tive Model refers to regime of visual per- ceptions through dynamical perturbations of light over the retina. As if feeling the textured light reflected off the niche sur- faces Looking is a subclass of intentional process. Searching is a subclass of investigat- ing. *ActiveVision is a subclass of Looking. *ActiveVision is a subclass of Searching. Affordance Agents in the Niche Options or capacities for Action by an Agent (sometimes called “Affordance 3.0”) From Ecological Psychology, the Perception of a possibility for Action (sometimes called “Affordance 2.0”). resource is an instance of CaseRole. resource is a subrelation of patient. resource is an instance of case role. resource is a subrelation of pa- tient. *Affordance is equivalent to resource. Agency Action The ability of an Agent to en- gage in Action in their Niche and enact Goal-driven selection or Policy selection based upon Preference Intentional process is a subclass of process *Agency is internally related to IntentionalProcess Agent Agents in the Niche Entity as modeled by Active In- ference, with Internal State separated from External State by Blanket State agent is a subrelation of in- volvedInEvent. agent is an instance of case role. The number 1 argument of agent is an instance of Process. The number 2 argument of agent is an instance of Autono- mousAgent. A SentientAgent is an Agent that is capable of Perception and experiences some level of consciousness. Ambiguity Bayesian Statistics Broad sense: Extent to which stimuli have multiple plausible interpretations, requiring priors &/or Action for disambiguation Narrow sense: Specific model parameter used to model Un- certainty, usually about sensory Perception. *Ambiguity is a subclass of StateOfMind. Attention Perception Broad sense: Generative Model that is aware of some Stimulus, reflected by its Salience Narrow sense: Attention modu- lates the confidence on the Precision of Sense State, re- flecting Sensory input *Attention is a subclass of Inten- tionalProcess. *Attention nominalizes the at- tends CaseRole Autopoiesis Phenomena of a System that recapitulates the material and informational causes of its own composition/existence. *Autopoiesis is internally related to Relication. Replication is a subclass of Organ- ismProcess. Bayesian Inference Bayesian Statistics As opposed to frequentist anal- ysis, Bayesian Inference uses a specified Prior or Empirical *BayesianInference is a subclass of PhysiologicProcess. ## Page 8 prior to Update the distribu- tional Posterior Behavior Action The sequence of Action that an Agent is observed to enact. *Behavior is an near synonym of BodyMotion. *Behavior is an near synonym of Process. Belief Bayesian Statistics Broad sense: Felt sense by an Agent of something being true, or confidence it is the case. Narrow sense: the State of a Random variable in a Bayesian Inference scheme. *Belief is a subclass of Psychologi- calProcess. Believes is an instance of Proposi- tionalAttitude. Belief updating Bayesian Statistics Belief updating is changes in a Bayesian Inference Belief through time. *BeliefUpdating is a subclass of In- tentionalPsychologicalProcess. IntentionalPsychologicalProcess is a subclass of IntentionalProcess. Blanket State Markov Par- titioning Set of states in the Markov Blanket Partition that make In- ternal State and External State have Conditional Probability that are independent. *ThermodynamicBlanketStates are PhysicalStates. *HomeostaticBlanketStates are In- ternalAttribute. Cognition Agents in the Niche An Agent modifying the weights of its Internal State for the purpose of Action Planning and/or Belief updating. (This is a @realistCounterpart of Goal-driven selection.) CognitiveAgent is a subclass of Sen- tientAgent A CognitiveAgent is an Agent that has the ability to reason, deliberate, make plans, and experience emotions. Complexity Bayesian Statistics The extent to which an Agent must revise a Belief to explain incoming Sensory observations. The Kullback-Leibler Diver- gence between the Prior and Posterior which is used in Bayesian model selection to find the simplest (least com- plex) model and avoid overfit- ting on the noise inherent in Sensory observations. *Complexity is a subclass of Objec- tiveNorm. ## Page 9 Cue Information A Stimulus, event, object, or Guidance signal that serves to guide Behavior, such as a re- trieval cue, or that acts as a @Signal to the presentation of another stimulus, event, or ob- ject, such as an unconditioned stimulus or reinforcement. (dic- tionary.apa.org) A *Cue is internally related to an instance of Perception. AgentPatientProcess is a sub- class of Process. Culture Agents in the Niche Culture is the Niche for social Agent, that structures their Re- gime of Attention *Culture is a subclass of Proposi- tion. Data Bayesian Statistics Data are a set of values of qual- itative or quantitative variables about one or more Agent or object. InformationMeasure is a subclass of ConstantQuantity. Stating is a subclass of Linguistic- Communication. UnitOfInformation is a subclass of NonCompositeUnitOfMeasure The number 1 argument of Con- tainsInformation is an instance of ContentBearingPhysical. *Data is a near synonym of In- formationMeasure. *Data is a near synonym of FactualText. *Data is a near synonym of Stating. Content bearing ob- ject is internally related to contains information. Content bearing physical is a subclass of physical. Decision-making Within Active Inference, this is the same as Policy selection Deciding is a subclass of Selecting. Selecting is a subclass of Intention- alPsychologicalProcess. Ensemble Agents in the Niche Group of more than one Agent. *Ensemble is a subclass of Collec- tion Epistemic value Free Energy Epistemic value is the value of Information gain or Expectation of reduction in Uncertainty about a State with respect to a Policy, used in Policy selection *EpistemicValue is a subclass of Psy- chologicalProcess. *EpistemicValue is a subclass of SubjectiveAssessmentAttribute. *EpistemicValue is an instance of In- ternalAttribute The abstract counterpart of an *EpistemicValue is an *Ab- stractEpistemicValue. *EpistemicValue is a relatedIn- ternalConcept to Investigat- ing. ## Page 10 Evidence Perception Data as recognized and inter- preted by Generative Model of Agent *Evidence is internally related to In- tentionalPsychologicalProcess. Expected Free En- ergy Free Energy Measure for performing Infer- ence on Action over a given time horizon (Policy selection, Action and Planning as Diver- gence Minimization). The two components of Ex- pected Free Energy are the im- perative to satisfy Preferences, and the penalty for failing to minimize Expectation of Sur- prisal. *ExpectedFreeEnergy is a subclass of RelationalAttribute. *ExpectedFreeEnergy is inter- nally related to Information- Measure. External State Markov Par- titioning States with #r65 independent from Internal State, condi- tioned on Blanket State. *ExternalState is a subset of Physio- logicProcess. Free Energy Free Energy Free Energy is an Information Theoretic quantity that consti- tutes an upper bound on Sur- prisal. Free Energy can refer to vari- ous or multiple sub-types of Free Energy: * Variational Free Energy * Expected Free Energy * Free Energy of the Expected Future * Helmholtz Free Energy *FreeEnergy is a subclass of Physi- calDimension. *FreeEnergy is a subclass of Rela- tionalAttribute. *FreeEnergy is internally re- lated to InformationMeasure. Free Energy Princi- ple Free Energy A generalization of Predictive Coding (PC) according to which organisms minimize an upper bound on the Entropy of Sen- sory input (or sensory signals) (the Free Energy). Under spe- cific assumptions, Free Energy translates toPrediction error. A set of statistical principles that describe how Agents can maintain their self-organization in the face of random fluctua- tions from the environment. *FreeEnergyPrinciple is an instance of Proposition. Friston Blanket Markov Par- titioning Markov Blanket with parti- tioned Active States and Sense State. *FristonBlanket is a subclass of ProbabilityRelation. *FristonBlanket is a subclass of Proposition. Generalized Free Energy Free Energy Past Variational Free Energy plus future Expected Free En- ergy (each totaled over Policy). *GeneralizedFreeEnergy is a sub- class of ProbabilityRelation. *GeneralizedFreeEnergy is a sub- class of Proposition. ## Page 11 Generative Model Agents in the Niche A formalism that describes the mapping between Hidden State, and Expectations of Ac- tion Prediction, Sensory out- come. Recognition Model Update In- ternal State parameter that correspond to External State (including Hidden State causes of environment states), Blanket State, and Internal State (meta- modeling). In contrast, Genera- tive Model take those same In- ternal State parameter Estima- tor and emit expected or plau- sible observations. *GenerativeModel is a subclass of Process. Generative Process Agents in the Niche Underlying @dynamical pro- cess in the Niche giving rise to Agent Observation and @agent Action Prediction. Enactive ecological process us- ing morphological computing processes where the Niche Re- gime of Attention @morpho- genesis and generative model interact to create an embodied learning dynamic. *GenerativeProcess is a subclass of ProbabilityRelation. *GenerativeProcess is a subclass of Proposition. *GenerativeProcess is a subclass of Process. Hidden State Markov Par- titioning Unobserved variable in Bayes- ian Inference, can reflect a La- tent cause. *AbstractHiddenState is a subclass of ProbabilityRelation. Hierarchical Model Systems A hierarchy of Estimators, which operate at different spa- tiotemporal timescales (so they track features at different scales); all carrying out Predic- tive Processing *HierarchicalModel is a subclass of ProbabilityRelation. *HierarchicalModel is a subclass of Proposition. *HierarchicalModel is a subclass of Process. Inference Bayesian Statistics Process of reaching a (local or global) conclusion within a Model, for example with Bayes- ian Inference. The process of using a Sensory observation (observed variable, data) along with a known set of parameters to determine the state of an unknown, Latent cause (unobserved variable). *AbstractInference is a subclass of Learning. (This looks wrong. Abstract classes are non-temporal, and Learning changes across time.) ## Page 12 Information Bayesian Statistics Measured in bits, the reduction of Uncertainty on a Belief dis- tribution of some type. Usually Syntactic (Shannon) but also can be Semantic (e.g. Bayes- ian). *Information is internally re- lated to InformationMeasure. Internal State Markov Par- titioning States with #r65 independent from External State, condi- tioned on Blanket State. *InternalState is a subset of Physio- logicProcess. Learning Bayesian Statistics Broad sense: Process of an Agent engaged in Updates to Cognition (and possibly) Behav- ior. Narrow sense: Process of Bayesian Inference where Gen- erative Model parameters un- dergo Belief updating Learning is a subclass of intentional psychological process Living system Systems Agent engaged in Autopoiesis *Attractor is internally related to SubjectiveAssessmentAt- tribute. Logic Logical operator is a subclass of predicate Markov Blanket Markov Par- titioning Markov Partitioning Model of System, reflecting Agent as de- lineated from the Niche via an Interface. The Markov Blanket Blanket State reflect the State(s) upon which Internal State and Exter- nal State are conditionally inde- pendent. *ThermodynamicSystem is a sub- class of *System Markov Decision Process Markov Par- titioning Bayesian Inference Model where Agent Generative Model can implement Policy selection on Affordances reflected by Ac- tive States, while other fea- tures of the Generative Process are outside the Control (states) of the Agent. Deciding is a subclass of Selecting ## Page 13 Niche Agents in the Niche Ecology System constituting the Generative Process (as Par- titioned from the Agent who in- stantiates a Generative Model). Attribute subsumes *Niche. Non-Equilibrium Steady State Agents in the Niche Technically, a Non-Equilibrium Steady State requires a solution to the Fokker Planck equation (i.e., density dynamics). A nonequilibrium steady-state solution entails solenoidal (i.e., conservative or divergence free) dynamics that break de- tailed balance (and underwrite stochastic chaos). Generally, a Non-Equilibrium Steady State refers to a System with dynamics that are un- changing, or at Stationarity in some State. *NonEquilibriumSteadyState is a subset of Attribute. Novelty Perception The Internal State assumed by an Agent’s epistemic Af- fordance, when unable to im- mediately (e.g. locally) resolve Uncertainty about the contin- gencies, i.e. the opportunity to resolve Uncertainty about ”what would happen if I did that?” *Novelty is a subclass of Subjective- AssessmentAttribute. SubjectiveAssessmentAttribute is a subclass of NormativeAttribute Observation Perception The Belief updating of an Inter- nal State registered by a Sen- sory input, given the weighting assigned to that class of input in comparison with weighting of the competing Priors. (This is a narrow sense of “observa- tion,” where the Agent is “look- ing for this kind of input.”) Any Sensory input, either dis- crete-valued or continuous-val- ued. (This is a broad sense.) *Observation is internally re- lated to CognitiveAgent. Perception Perception Posterior State Inference after each new Observation. Perception is a subclass of psycho- logical process ## Page 14 Policy Action Sequence of Actions, reflected by series of Active States as im- plemented in Policy selection which is Action Prediction or Action and Planning as Diver- gence Minimization Policy is a subclass of Proposition. Posterior Bayesian Statistics The Update to the Prior after Observation has occurred In Bayes”' theorem, the Poste- rior is equal to the product of the Likelihood and Prior divided by the model evidence. *Posterior is a subclass of Proposi- tion. Pragmatic Value Free Energy Pragmatic Value is the benefit to an organism of a given Policy or Action, measured in terms of probability of a Policy leading to Expectation of Random vari- able values that are aligned with the Preference of the Agent Pragmatic value describes the extent to which a given action is aligned with rewarding pref- erences over sensory out- comes. *PragmaticValue is a subclass of StateOfMind. *PragmaticValue is a re- latedInternalConcept to Se- lecting. Principle Principle *Principle is an instance of Proposi- tion. *Principle is internally related to Reasoning. Reasoning is an instance of Proposit IntentionalPsycholog- icalProcess Propositional atti- tude attitudeForFormula is an instance of ternary relation. The number 1 argument of atti- tudeForFormula is an instance of EmotionalState. The number 2 argument of atti- tudeForFormula is an instance of agent. The number 3 argument of atti- tudeForFormula is an instance of Formula. ## Page 15 Recognition Model Agents in the Niche Recognition Model is the kind of Model that affords Varia- tional Inference, which lets us calculate or approximate a probability distribution. Recog- nition Model is a synonym for Variational Model. In Dayan and Abbot (2001), the probability of a Hidden State (causes) given Sensory Data (ef- fects) under some parameter. Recognition Model is a subclass of Modeling. Modeling is a subclass of intentional process *Recognition is internally re- lated to Realization. Recognition Model is inter- nally related to Variational. Representation Agents in the Niche A structural correspondence between some Random varia- ble inside a System and some Random variable outside the System (isomorphism being the strongest kind of correspond- ence), such that the System en- gages in Inference carried out by the System maintains the correspondence represents is a subrelation of refers. refers is a relation from an Entity to an Entity. * If a process is an instance of expressing and an agent is an agent of the process, * then there exists an attrib- ute such that the attribute is an instance of state of mind and the attribute is an attrib- ute of the agent and the pro- cess representsthe attribute represents SUMO relation (represents ?THING ?ENTITY) means that ?THING in some way indicates, expresses, connotes, pictures, describes, etc. ?ENTITY. The Predicates containsInfor- mation and realization are subrelations of represents. represents is an instance of binary predicate. represents is a subrelation of refers. The number 1 argument of re- fers is an instance of Entity. The number 2 argument of re- fers is an instance of Entity. Sense State Markov Par- titioning In the Friston Blanket formal- ism, the Blanket State are the Sense State (incoming Sensory input) and Active States (out- going influence of Policy selec- tion ) *SenseState is a subset of Physio- logicProcess State Bayesian Statistics is the statistical, computa- tional, or mathematical value for a parameter within the State space of a Model. Attribute is a subclass of abstract. “State” is a near synonym of Attribute. State space Bayesian Statistics Set of variables/parameters that describe a System. A state space is the set of all possible configurations of a sys- tem *StateSpace is a subset of Attribute ## Page 16 System Systems Set of relations described by State space of a Model. Differentiable and Integratable in terms of Variables and func- tions. *System is a subclass of Agent ACKNOWLEDGMENT To the late Prof. Gordon Pask, who taught me how to think about thinking. REFERENCES Douglass, David S. et al. (2024). Active Inference Topics Aligned to SUMO. DOI 10.5281/zenodo.11459323 Douglass, David S. et al. (2024). Aligning Spatial Web Terminology to SUMO. https://zenodo.org/doi/10.5281/zenodo.11055372 Pask, Gordon and Bernard C.E. Scott (1973). CASTE: A System for Exhibiting Learning Strategies and Regulating Uncertainties. Int. J. Man-Machine Studies (1973) 5, 17-52. https://www.sciencedirect.com/science/article/abs/pii/S0020737373800082?via%3Dihub Niles, I., & Pease, A., (2001). Toward a Standard Upper Ontology, in Proceedings of the 2nd International Conference on Formal Ontology in Information Systems (FOIS-2001), Chris Welty and Barry Smith, eds, pp 2-9. https://dl.acm.org/doi/10.1145/505168.505170 Pease, A., (2011). Ontology: A Practical Guide. Articulate Software Press, Angwin, CA. ISBN 978-1-889455-10-5 Pease, Adam (2024). Suggested Upper Merged Ontology (SUMO). https://ontologyportal.org/ --- *Extraction method: pymupdf*