Towards a Deontic Cognitive Event Ontology Miroslav VACURA a,1 and Vojtěch SVÁTEK b a Department of Philosophy, University of Economics, Prague. b Department of Information and Knowledge Engineering, University of Economics, Prague. Abstract. Although there have been efforts to integrate Semantic Web technologies and Cognitive Science approaches, they are still relatively isolated. Herein we introduce a new ontology framework supporting rep- resentation and reasoning on complex cognitive information using Se- mantic Web tools. The framework consists of four parts: an event on- tology for information about actions and events, an epistemic ontology containing facts about knowledge, belief, perception and communica- tion, an ontology about future intentions and desires, and, finally, a de- ontic ontology for modeling obligations. The structure of the ontology framework is derived from the syntax of the Deontic Cognitive Event Calculus. Keywords. electronic camera-ready manuscript, IOS Press, LATEX, book, layout 1. Introduction One of the most difficult problems of artificial intelligence is the autonomous action of artificial agents. One of possible approaches to this problem is inspired from common-sense psychology (CSP) and mentalistic models of human behavior. Knowledge representation models are called mentalistic if they are related to mental notions like knowledge, belief, goals etc. [23]. Mentalistic models have been used for some time for user modeling in traditional knowledge representation and in some cases combined with machine learning techniques [24]. Contrasting to that, in the Semantic Web context the use of mentalistic models has been mostly limited to utilizing the concept of belief when taking care of changing knowledge [11]. It has been also argued that application of such models is likely to be particularly apt for artificial agents operating in environments where they have to communicate or even influence the behavior of other artificial or human agents [1]. When an artificial agent in such an environment communicates some infor- mation, either publicly or privately to another agent, this information cannot be taken at face value because it only represents a belief or can be even intention- 1 Corresponding Author: vacuram@vse.cz ally misleading. Such an account of communication requires mentalistic models distinguishing belief from knowledge. An artificial agent also needs to reason about the consequences of his own intended actions. On the top of that, reasoning about actions of other agents and predicted consequences of such actions is also required. In other words, models enabling such reasoning have to deal with past, current and future actions and generally with events, so flexible handling of time is necessary. Dealing with future events caused by actions of other agents requires mental- istic models of their internal mental representations of future – their desires and intentions. We call these future-involving mental states protential, based on the term ‘protention’, meaning the consciousness of future and coined by the philoso- pher E. Husserl [21]. These internal mental states directly influence the exter- nal behavior of agents – intended actions materialize and became real actions performed in an external environment. The last level of mentalistic model contains the representation of obligations. These are limitations of behavior of the reasoning agent himself and of other agents, which can be used for predicting their actions. Obligations require an agent to act (or suspend action) when some defined conditions of the environment are observed. We call the model involving obligations deontic, based on similar use of the term in the context of deontic logic [18]. Note that even if we use metalistic models and discus an agent’s behavior in mentalist terms, this does not mean that such an agent would be required to have genuine mental states; accepting a thoroughly instrumentalist view of mental states of artificial agents is sufficient for our purposes. We can now summarize the requirements for (or, directly, components of) a minimal model enabling artificial agents’ reasoning in the above defined context: • Model of events and actions • Epistemic mentalistic model • Protential mentalistic model • Deontic mentalistic model As we have said above, there have only been very limited efforts to use men- talistic approach in the Semantic Web context. However there has been a number of approaches to model events in general, on the semantic web; for a most recent overview see [9]. Outside the Semantic Web, a mentalistic approach has been suc- cessfully deployed by means of the Deontic Cognitive Event Calculus (DCEC ∗ ), which provided inspiration and a starting point for our approach. However, trans- forming a calculus to an ontology is not an easy or straightforward task. One such effort to provide an ontology representation of an event calculus was the Discrete Event Ontology, for which an OWL2 ontology, SWRL3 rules and a resolver have been provided. However, only a simple version of event calculus has been cov- ered, and the resulting ontology only comprised three classes (Events, Fluents, Timepoints) and a couple of rules [19, 20]. The Section 2 of the paper provides a general overview of the architecture of the proposed framework. Section 3 describes the Deontic Cognitive Event Calculus 2 https://www.w3.org/TR/owl2-syntax/ 3 https://www.w3.org/Submission/SWRL/ DCEO SWRL Rules SWRL Rule engine DCEO OWL Ontology DL reasoner Tbox DCEO OWL Ontology Temporal Maintenance Abox Perception Motoric Processor Processor Figure 1. Prospective Application Architecture based on the DCEO (DCEC ∗ ). Section 4 describes the proposed ontology framework itself. Section 5 discusses open problems and possible future extensions of the framework. Finally, the last section provides some conclusions. 2. Architecture and use of the Deontic Cognitive Event Ontology The overview of a hypothetical application architecture making use of the pro- posed Deontic Cognitive Event Ontology (DCEO) is depicted in Fig. 1. We have been inspired by the Soar cognitive architecture [15] and the classical concept of the Model Human Processor [6,22], which is in turn based on the Standard Model of human cognition [12, 26]. There are however some differences. The core of our architecture consists in the Tbox of an OWL ontology (described in this paper) and a set of SWRL rules. These represent stable knowledge about the world. Information obtained from the environment is transformed by a component that is traditionally called Per- ception processor and, in our case, produces OWL statements to be included in the Abox of the ontology. On the top of the whole ontology, consisting of a Tbox, Abox and SWRL rules, there is a SWRL rule engine and a DL reasoner, which are responsible for inferential tasks. Another component traditionally called Motoric Processor monitors the current time and retrieves the statements representing actions that are to be performed at the given time point. The last component, which we call Temporal Maintenance, carries out auxiliary operations such as re- moving old Abox axioms from the data store, thus enabling the whole system to function efficiently. Namely, the growth of the number of axioms caused by con- tinuous addition of new statements to the Abox combined with axiom production by the SWRL rule engine might be enormous; clearing of old data representing no-longer-useful knowledge would thus be necessary. While the overall architecture is inspired by the standard Model Human Pro- cessor, the internal structure of the ontology is based on the Deontic Cognitive Event Calculus (DCEC ∗ ), which has been successfully used in a number of real- world scenarios such as reasoning over a scene description [17], control of robot behavior [4] or even simulation of some features of human consciousness [5]. Object | Agent | Self @ Agent | ActionType | Action v Event | S ::= Moment | Boolean | Fluent | Numeric action : Agent × ActionType → Action initially : Fluent → Boolean holds : Fluent × Moment → Boolean happens : Event × Moment → Boolean clipped : Moment × Fluent × Moment → Boolean f ::= initiates : Event × Fluent × Moment → Boolean terminates : Event × Fluent × Moment → Boolean prior : Moment × Moment → Boolean interval : Moment × Boolean * : Agent → Self payoff : Agent × ActionType × Moment → Numeric t ::= x : S | c : S | f (t1 , ..., tn ) p : Boolean | ¬φ | φ ∧ ψ | φ ∨ ψ | φ → ψ | φ ↔ ψ | ∀x : S.φ | ∃x : S.φ | P(a, t, φ) | K(a, t, φ) | C(t, φ) | S(a, b, t, φ) | S(a, t, φ) | φ ::= B(a, t, φ) | D(a, t, holds(f, t0 )) | I(a, t, happens(action(a∗ , α), t0 )) | O(a, t, φ, happens(action(a∗ , α), t0 )) Figure 2. DCEC ∗ Syntax 3. Deontic Cognitive Event Calculus (DCEC ∗ ) Deontic Cognitive Event Calculus (DCEC ∗ ) is a multi-sorted quantified modal logic developed at Rensselaer Polytechnic Institute, which has a well-defined syn- tax and a proof calculus. Detailed information about multi-sorted first order logic (MSL) can be found in a book by M. Manzano [16]. DCEC ∗ syntax includes a system of sorts S, a signature f , a grammar for terms t, and a grammar for sen- tences φ; these are shown in Fig. 2. An overview of the formal syntax of DCEC ∗ can be found in the original works [2, 3]. The proof calculus is based on natural deduction [10] and includes all the introduction and elimination rules of first-order logic as well as rules for modal operators. In this paper we only focus on the ontological representation of DCEC ∗ , which covers its syntax using the apparatus of OWL, and omit the inferential rules [2], which would mostly have to be expressed using SWRL. DCEC ∗ is based on the Event Calculus (EC), which was first introduced by Kowalski and Sergot as a logic programming formalism for representing events and their effects [13], and later also presented in a simplified version [13]. A detailed presentation of EC can by found in the work of Shanahan [25]. DCEC ∗ adapts three sorts from EC : Event, Moment and Fluent. The sort Boolean is only used to capture truth values. The following elements of the sig- nature f of the syntax of DCEC ∗ are adapted from EC: initially, holds, happens, clipped, initiates, terminates and prior. For the relation prior, which introduces an orders over time points, EC sometimes uses the simple symbol <. We will now briefly describe the components of DCEC ∗ . A Fluent is anything the value of which can change over time. Typically it is a truth value of a propo- sition (e.g., “Peter is student”). A fluent can be also a numerical value of a prop- erty that is subject to variation, e.g., temperature, but such a value can be easily transformed to a proposition (e.g., “The temperature is between 5 and 10 degrees Celsius.”) so EC usually confines its focus to propositional fluents. A Moment is a point in time. Points in time are ordered by the relation prior. The expression prior(t1 , t2 ) means that the time point t1 precedes the time point t2 (e.g., t1 = 01/01/2015 precedes t2 = 01/01/2016). The term holds(f, t) says that fluent f holds at a given time t (e.g., f = “Peter is student” holds at t = 01/01/2016). The expression initially(f ) indicates that the fluent f holds from time 0. Several signature members describe relations between events and fluents. The general idea of EC is that events cause changes of truth values of fluents. The ex- pression happens(e, t) thus informs that event e happened in time t (e.g., e = “Pe- ter concluded his studies” at t = 05/03/2016). The expression terminates(e, f, t) states that following event e fluent f ceased to hold at time t (e.g., after e = “Peter concluded his studies” at t = 05/03/2016, proposition f = “Peter is a student” was no longer true). Similarly, the expression initiates(e, f, t) states that after event e fluent f started to hold at time t (e.g., after e = “Peter was inau- gurated” at t = 02/03/2011, proposition f = “Peter is a student” started to be true). The expression clipped(t1 , f, t2 ) says that fluent f is terminated between time t1 and time t2 (e.g., f = “Peter is a student” is terminated between t1 = 01/01/2016 and t2 = 01/01/2017). DCEC ∗ introduces a mechanism to deal with epistemic information on the top of the event conceptualization of EC. DCEC ∗ has a classical monotonic view of the agents’ knowledge of the world. The knowledge possessed by agents is considered to be unchanging, so if an agent knows φ at some time t, then the agent will continue to know φ for all time. On the other hand, an agents’ beliefs can change as time passes. This marks a fundamental difference in understanding knowledge vs. belief in DCEC ∗ . The epistemic predicate C(t, φ) indicates common knowledge (possessed by all agents) of φ at time t. The predicate K(a, t, φ) says that agent a knows φ at time t. The predicate B(a, t, φ) says that agent a believes in φ at time t. Finally, the predicate P(a, t, φ) says that agent a perceives φ at time t. DCEC ∗ also introduces tools for capturing the communication of agents. The predicate S(a, b, t, φ) describes the communication of information φ from agent a to agent b at time t. Public communication of information φ at time t by agent a is denoted as S(a, t, φ). There is another set of predicates, which we may call behavioral: the predicate D(a, t, holds(f, t0 )) says that agent a desires that fluent f would hold at time t0 . Similarly, to say that agent a at time t intends to perform an action of type α at time t0 , we use the predicate I(a, t, happens(action(a, α), t0 )). These predicates are based on work by Goble [8] and McNamara [18]. Event Ontology terminatesWhat Interval Termination includesMoment when dateTimeFrom dateTimeTo terminatedBy initiatesWhat Object Event initiatedBy Initiation prior when when Moment dateTimeMoment Payoff Action now clippedFrom payoffAction when payoffValue clippedTo Hold payoffAgent typeOfAction truthValue who holds / ofFluent-1 Agent ActionType Fluent self when initially fluentRelationFrom fluentRelationTo what Epistemic communicationTo FluentRelation what Ontology who Knowledge when relationType who Communication Belief CommonKnowledge when what what when Perception Protential indendsWhen desiresWhen what Ontology indendsActionType when Intention Desire who Deontic obligedToActionType Ontology when who Obligation what obligedWhen Figure 3. Deontic Cognitive Event Ontology (DCEO) Finally, the deontic predicate O(a, t, φ, happens(action(a, α), t0 )) should be in- terpreted according to authors of DCEC ∗ as: “If it is the case that a at time t believes φ then that α is obligatory for a and this [obligation] is known by a.” The semantics of this predicate is based on a study by Castañeda [7]. These predicates also require the introduction of the sort ActionTypes covering the general types of action, and of the function action(a, α) = b, expressing that for a given agent a, an action type α produces a specific action b. The operator ∗ is used to point out the reasoning agent himself among other agents in the universe of discourse. The operator payoff(a, α, t) is used to evaluate an action of type α performed by agent a at time t; the result of such an evaluation is of the Numerical sort. 4. Deontic Cognitive Event Ontology The proposed Deontic Cognitive Event Ontology (DCEO) will be described in de- tail in this section. Fig. 3 depicts the general overview of the ontology, while Table 1 summarizes the object properties of the ontology and their features (domain, Table 1. Object properties of the Deontic Cognitive Event Ontology Relation Domain Range Symm. Refl. Trans. Func. when D1 a Moment No No No Yes what D1 R1 b No No No Noc who D2 d Agent No No No No includesMoment Interval Moment No No No No terminatedBy Termination Event No No No Yes initiatedBy Initiation Event No No No Yes payoffAction Payoff Action No No No Yes payoffAgent Payoff Agent No No No Yese typeOfAction Action ActionType No No No Yesf prior Moment Moment No No Yes No clippedFrom Fluent Moment No No No Nog clippedTo Fluent Moment No No No Noh fluentRelationFrom FluentRelation Fluent No No No Yes fluentRelationTo FluentRelation Fluent No No No Yes communicationTo Communication Agent No No No Noi desiresWhen Desire Moment No No No No intendsWhen Intention Moment No No No No obligedWhen Obligation Moment No No No No intendsActionType Intension ActionType No No No No obligedToActionType Obligation ActionType No No No No aD 1 = EpistemicState t CommonKnowledge t ProtentialState t Communication t Obligation b R = Holds t FluentRelation 1 c E.g. one event can terminate several fluents. d D = EpistemicState t ProtentialState t Communication t Obligation 2 e Actions are generally considered to be performed by individual agents. f We suppose that every action has only one ActionType. g We may have several clipping information items for a single fluent. h Dtto. i A communication can be aimed to several receiving agents. range, symmetry, reflexivity, transitivity and functionality). Specific axioms for each part of the ontology will be listed at the end of each respective section of the following text. The ontology can be downloaded from our website.4 4.1. The event section of the ontology The foundation of the ontology provides the class Moment, which consists of time points ordered by the object property prior. The property prior has the class Moment as both its domain and its range. We have introduced an additional data property dateTimeMoment, which explicitly specifies precise the date and time for those moments where it is known. The function interval of the calculus has been reified to the class Interval of the ontology, and we introduced two similar data properties as for previous 4 https://webhosting.vse.cz/vacuram/ontologies/dceo/ class, dateTimeFrom and dateTimeTo, which specify the precise date and time boundaries of an interval. The object property includesMoment connects inter- vals (members of class Interval) with moments (members of class Moment) that belong to it. The class Fluent represents propositional entities that are subject to change. It has a boolean data property initially, which corresponds to the function initially of DCEC ∗ . The class Hold represents the reified function holds of the calculus. The class Fluent is connected to the class Hold by the object property holds. The class Hold is in turn connected to the class Moment by the object property when, and it also has a data property truthValue. This whole structure allows us to represent that a fluent holds a truth value at a given moment. The same fluent can hold truth value true at one moment and the truth value false at another. This structure then can become the content of an epistemic state, for example, an agent can believe that a fluent (proposition) has the truth value false in the given moment. The data property truthValue is assumed to be boolean (truth values represented by 0 or 1) as in DCEC ∗ ; however, using this ontology design it may be easily adapted to dealing with various types of uncertainty. The class Event and its subclass Action do not have any data type properties, and together with the class Agent they constitute the core of the event-focused section of the ontology. The class Agent has one specific boolean data property, self. This corresponds to the function * and the sort Self of DCEC ∗ . The ontology in use may contain and describe number of different agents – only one of them is the reasoning agent himself and this property is used to represent this knowledge. Obviously only one agent can have this property true. We have also introduced three general object properties: what, who and when. These represent various properties of DCEC ∗ with common ranges. The range of the object property what are classes Hold and FluentRelation: aside of epis- temic states directed towards simple propositional fluents (represented by the on- tological structure around the class Hold described above) there may be epistemic states about relations between fluents. The class FluentRelation has two related object properties fluentRelationFrom and fluentRelationTo, both connected to fluents, and one data property fluentRelationType denoting the specific type of relation between fluents. The range of the object property who is the class Agent. The property usually denotes the subject of an epistemic state or the active agent of a communication or of an action. The range of the object property when is the class Moment, denoting the time of events, actions, epistemic or protential states throughout the ontology. Note that this property denotes the occasion of the actual occurrence of a protential state: the occasion when, e.g., the intention is mentally present. The protential state also has the second specification of time – the future time when something is intended (e.g., the agent now at time t1 intends to move to point X later at time t2 ). This second time specification is denoted by specific separate object properties (see Sec. 4.3). The classes Termination and Initiation represent the functions terminates and initiates of DCEC ∗ . They are connected by the object property when with the class Moment, and by the object properties terminatesWhat and initiatesWhat with the class Fluent, indicating what fluent was terminated/initiated and when. The object properties terminatedBy and initiatedBy connect these classes with the class Event, representing the event that caused initiation or termination of the fluent. There are also four additional classes fulfilling auxiliary functions. The class ActionType represents types of actions and provides classification for individual actions of the class Action. It is to be mainly used by higher ontological levels, to theoretize about possible or future actions. The class Object (superclass of class Agent) is a general class for all objects. Any other objects modeled by the ontology belong to this class. The class Payoff represents the payoff (using a numeric data property payoffValue) of an action represented by the class Action connected by the data property payoffAction. The reification of payoff property to the class Payoff was necessary because the payoff value is related to the Agent, Action and Moment. The same action of the same agent done at different moments may have different payoff value. Similarly, the same action done at the same moment but different agents may have also different payoff value. The axioms of the event section of the ontology are the following: Termination≡(∃terminatedBy.Event)u(∃when.Moment)u(∃what.Fluent) (1) Initiation≡(∃initiatedBy.Event)u(∃when.Moment)u(∃what.Fluent) (2) Moment≡(∃prior.Moment) (3) Fluent≡(∃when.Moment) (4) Payoff≡(∃payoffAgent.Agent)u(∃payoffAction.Action)u(∃when.Moment) (5) Action≡(∃who.Agent) (6) Disjoint(Termination,Initiation,Moment,Fluent,Interval,Event,ActionType,Object,Payoff) (7) 4.2. The epistemic section of the ontology The core of the epistemic section of the ontology consists of three mutu- ally similar classes, Knowledge, Belief and Perception, representing three different epistemic attitudes (there is also their superclass EpistemicState, which is for brevity not depicted in Fig. 3). They are connected to the know- ing/believing/perceiving Agent using the property who. The content of the epis- temic attitude is determined by the object property what connected to the classes Hold and FluentRelation. The time of knowing/believing/perceiving is deter- mined by the property when connected to class Moment. The class CommonKnowledge represents knowledge – property what – avail- able, at a given time specified by property when, to all agents. It is similar to the epistemic states described above, but it obviously lacks the object property who, because it is know by all agents. The class Communication represents the cases of information transfer be- tween agents. It describes the communication of some content given by the property what at some time defined by the property when by an Agent de- scribed by the property who to another Agent – specified by the object property communicationTo. The axioms of the epistemic section of the ontology are the following: Communication≡(∃communicationTo.Agent)u(∃who.Agent)u(∃when.Moment)u(∃what.Fluent) (8) Knowledge≡(∃who.Agent)u(∃when.Moment)u(∃what.Fluent) (9) Belief≡(∃who.Agent)u(∃when.Moment)u(∃what.Fluent) (10) Perception≡(∃who.Agent)u(∃when.Moment)u(∃what.Fluent) (11) CommonKnowledge≡(∃when.Moment)u(∃what.Fluent) (12) 4.3. The protential section of the ontology The protential section of the ontology focuses on modeling the future. It currently only consists of two classes: Intention and Desire. The class Intention represents an intention of an Agent (connected by the property who) to perform an action of the type determined by the class ActionType (connected by the object property intentionActionType). As men- tioned above, there are two time specifications: the object property when deter- mines the time when the act of intention takes place, and the object property intendsWhen determines the time when the intended action should take place. The class Desire is similar to Intention but it does not comprise any action of an agent himself. While agent may intend to do something himself (e.g. to pickup friend at 6pm), he does desire something to happen to him (e.g. desires to be picked up at 6pm by friend) . So in the case of intention the agents role is active, while in the case of desire the agent is passive. The Desire therefore only uses the object properties what, when, who and desiresWhen. Both Intention and Desire are subclasses of the class ProtentialState (for brevity not depicted on Fig. 3). The axioms of the protential section of the ontology are the following: Intention≡(∃intendsActionType.ActionType)u(∃who.Agent)u (13) u(∃when.Moment)u(∃intendsWhen.Moment) Desire≡(∃what.Fluent)u(∃who.Agent)u(∃when.Moment)u(∃desiresWhen.Moment) (14) 4.4. The deontic section of the ontology The deontic section of the ontology consists of a single class, Obligation. It is similar to class Intention, since obligation in this sense is structurally similar to intention. There is an individual (who) that at some point of time (when) thinks that his obligation is to do some action of an (ActionType), in a current or fu- ture time point (obligedWhen). Note that there are again two time specifications similarly to the case of protential concepts. The obligations are modeled ‘subjectively’, so they are always related to an individual agent who is mentally aware of them as of a specific behavioral binding applicable to his actions. The obligation is always an obligation to act somehow. Another important kind of obligation, that to abstain from an action, may be only modeled indirectly as obligation to a specific passive action, e.g., “waiting at a given location”. The only axiom of the deontic section of the ontology is the following: Obligation≡(∃obligedToActionType.ActionType)u(∃what.Fluent)u (15) u(∃who.Agent)u(∃when.Moment)u(∃obligedWhen.Moment) 5. Open Problems and Possible Future Extensions The DCEC ∗ calculus is based on the concept of a fluent that has propositional character. Most foundational ontologies do not model propositional entities. It might be necessary to adapt the ontology so that it could somehow model propo- sitional entities or even their internal structure. The relation between individual fluents is now modeled using a FluentRelation, class but it might be insufficient. In the real-world applications performing in the real time the speed of rea- soning is of prime importance. The current expressiveness of the DCEO ontology ∗ is ALFC(D) . It is sufficiently fast for the most of the reasoning tasks performed. However the complexity of the ontology may raise especially after adding the han- dling of internal structure of propositions. Such an increase of complexity could lead to significant reasoning-time overhead. The protential section of the ontology currently contains the classes Intention and Desire. Both these classes concern future events and it might be possible to model those more precisely. Desires refer to foreseen future events that are pos- itively evaluated but at the same time are not agent’s own actions. Such events may be divided to those caused by the agent’s actions (directly or indirectly) and those events that are desired but are out of the scope of being influenced by the agent’s actions. Foreseen future events may also differ in terms of evaluation. There are pos- itively evaluated events – desires – but there are also neutral events that are just foreseen as part of the probable future course of events – we may call them ex- pectations – and also foreseen events that are negatively evaluated – fears. Such distinctions may be very useful for agent’s understanding of his environment and for planing and realization of his actions. This line of thoughts is connected to another possible extension of the ontol- ogy, one allowing to more precisely represent the plans of agent and courses of events that happen in his environment. In the future the deontic section of the ontology may also be extended so as to enable the handling of more general concepts of obligation than the current “subjective” obligation adapted form DCEC ∗ does. It might be useful to think of “universal” obligations applicable to all agents, or of obligations applicable to an agent but unknown to him. Another improvement may be more direct handling of obligations to abstain from an action. This type of obligation is in some context more appropriate and important. 6. Conclusions Solving the problem of an autonomous action of artificial agents is indispensable in order to progress in many areas of artificial intelligence. The research dealing with this problem in the context of the Semantic Web is very limited, and our project presents an effort to bridge the gap between Semantic Web technologies and Cognitive Science. We have presented an ontology framework based on the Deontic Cognitive Event Calculus (DCEC ∗ ) and inspired by other general cognitive architectures. We also presented a brief overview of our cognitive architecture that includes proposed ontology as its core and enables effective cognitive reasoning on top of it. The Deontic Cognitive Event Ontology (DCEO) described in this paper con- sists of four parts: event ontology, epistemic ontology, protential ontology and de- ontic ontology. The event ontology allows modeling of actions of artificial agents occurring at specific times or intervals. The epistemic ontology describes the men- tal states of these agents: belief, knowledge and perception, and their proposi- tional content. It also enables the modeling of communication between agents and of common knowledge available to all agents. The proposed protential ontology models the agents’ attitudes to future – their desires and intentions. This in turn influences their autonomous actions. Finally, the deontic ontology aims at mod- eling obligations – the actions that the agent knows he is obliged to perform at a given situation. The paper finally discusses some problems and limitations of the presented framework and proposes some possible future enhancements and extensions. 6.0.1. 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