=Paper= {{Paper |id=Vol-1660/caos-paper3 |storemode=property |title=Towards a Deontic Cognitive Event Ontology |pdfUrl=https://ceur-ws.org/Vol-1660/caos-paper3.pdf |volume=Vol-1660 |authors=Miroslav Vacura,Vojtěch Svátek |dblpUrl=https://dblp.org/rec/conf/fois/VacuraS16 }} ==Towards a Deontic Cognitive Event Ontology== https://ceur-ws.org/Vol-1660/caos-paper3.pdf

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. Acknowledgments.
This work has been supported by the long-term institutional support of research
activities by the Faculty of Informatics and Statistics, University of Economics,
Prague.
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