=Paper=
{{Paper
|id=Vol-2632/MIREL-19_paper_3
|storemode=property
|title=A Methodology for Encoding Regulatory Rules
|pdfUrl=https://ceur-ws.org/Vol-2632/MIREL-19_paper_3.pdf
|volume=Vol-2632
|dblpUrl=https://dblp.org/rec/conf/jurix/BhuiyanOGIBR19
}}
==A Methodology for Encoding Regulatory Rules==
https://ceur-ws.org/Vol-2632/MIREL-19_paper_3.pdf
A Methodology for Encoding Regulatory Rules
Hanif Bhuiyan1,2 , Francesco Olivieri1 , Guido Governatori1 , Mohammad Badiul
Islam1 , Andy Bond2 , and Andry Rakotonirainy2
1
Data61, CSIRO
{hanif.bhuiyan,francesco.olivieri,guido.governatori,badiul.islam}@data61.csiro.au
2
Queensland University of Technology (QUT), Centre for Accident Research and
Road Safety (CARRS-Q), Queensland, Australia
{h.bhuiyan,andy.bond,r.andry}@qut.edu.au
Abstract. This paper introduces a methodology for the encoding of
rules into a semantic logical format to facilitate the automated reasoning
process. We demonstrate how to identify, capture, combine, and thus
formulate all the components from rules into a computationally-oriented
formalism. The need for the methodology is motivated by the desire for
automated reasoning of automated vehicle information regarding traffic
rules. We use Defeasible Deontic Logic as a formal foundation of our
methodology. The overtaking traffic rule is our use-case to illustrate the
usefulness of our methodology. Through this use-case, it is seen that the
logical semantic representation of the traffic rules seems conceivable to
support automated reasoning. This paper includes the source materials,
the use cases, proposed methodology, and the example of encoding.
· · ·
Keywords: Rules Terms Legal norms Defeasible Deontic Logic.
1 Introduction
Road crash is a major concern of global public health due to the epidemic growth
of road fatalities. Every day, more than 3,7001 people die due to road crashes.
From world road crash statistics, it was found that the driver’s behaviour is the
main contributing factor for 90% of these crashes [1]. In Australia, around 30%
of road crashes occurred due to speeding2 . From 2013-20173 , in Queensland, the
average death due to high speed was 58 per year. To overcome these driver’s
behaviour related errors, Automated Vehicles (AVs) can be introduced to follow
traffic rules properly, which can reduce road fatalities and injuries, and improve
road safety [17]. As AVs are designed and programmed to follow traffic rules [20],
1
https://www.who.int/violence injury prevention/road traffic/en/
2
https://www.budgetdirect.com.au/car-insurance/research/car-accident-st
atistics.html
3
https://streetsmarts.initiatives.qld.gov.au/speeding/factsheet
Copyright © 2020 for this paper by its authors. Use permitted under Creative
Commons License Attribution 4.0 International (CC BY 4.0).
2 Hanif Bhuiyan et al.
therefore, it is suggested that AVs would be the solution to traffic violations [17].
However, traffic rules are expressed in natural language, therefore it is required
to encode them into a machine-computable format to be processable by AVs.
Only then it might be effective for monitoring or validating AV driving action
through automatic traffic rule reasoning [2,29,26].
Rule encoding is one of the important requirements for compliance checking,
automated reasoning, and legal validation [31,30]. However, rule encoding is a
complex task due to its domain-specific, sentence length, clause embedding, and
structure. Moreover, rules include thousands of provisions and complex norms,
[4], which make the encoding task more challenging. Several research works
have been done to address these issues [28,21]. Standards for the representation
of norms and legal knowledge have been proposed [24]. For example, XML
serialisations have been presented for business contracts [10], business process
compliance [13], GDPR regulations [5,25] and building regulations [8]. There
are also some commercial products available (Oracle Policy Automation 4 ) that
provide services to translate regulations into executable language and also served
with an interactive natural language interface on the web.
Traffic rules are often detailed and complex and, therefore, it is a big challenge
to encode them. There have been several efforts to encode traffic rules for different
purposes [7,26]. Costescu [6] proposed a traffic rules formalisation method using
Higher Order Language (HOL) to keep the AV accountable. Shardin et al [29]
presented an expert system to formalise the traffic rules for controlling the
autonomous vehicle in certain situations. However, none of the previous research
considers how to resolve the conflicts and how to handle norms and exceptions
in the rules, which are the most important variant features of the traffic rules.
Therefore, here we propose a Defeasible Deontic Logic (DDL) based encoding
methodology to translate traffic rules (natural text) into a semantic logical
format (machine-computable). The integration of DDL makes our rule encoding
methodology more applicable and useful for handling the exceptions, situation
conflicts, ambiguities, and various forms of norms. An example of translating a
traffic rule into semantic logical format is shown in Figure 1. The use case we
used in this work is derived from the Queensland Traffic Rules, Australia5 . The
motivation for the use of DDL is provide below.
Legal reasoning has some special features, which are norms and exceptions.
Usually, norms set baseline conditions but later, they are open to exceptions.
These exceptions are also expressed as norms. Norms prescribe behaviour using
permissions, obligations, and prohibitions. Therefore, we need a logical approach
to handle such exceptions in the traffic rules. In legal reasoning, DDL has been
successfully proposed [12] to handle the exceptions and it is also seen that it
does not undergo from problems affecting other logics used for reasoning about
compliance and norms [11]. For the representation of rules, DDL is a conceptually
profound approach, and at the same time, it exhibits a computationally feasible
4
https://www.oracle.com/technetwork/apps-tech/policy-automation/overvie
w/index.html
5
https://www.legislation.qld.gov.au/view/html/inforce/current/sl-2009-0
194#sec.20
A Methodology for Encoding Regulatory Rules 3
Fig. 1. (a) A snippet of Overtaking Traffic Rules (b) Terms, norms, and if-then structure
Identification (c) Encoding of traffic rules 141 (1) using Defeasible Deontic Logic.
environment to reason about them. Furthrmore, Defeasible Deontic Logic (DDL)
is computationally feasible since we can compute the extension of a theory in
linear time [15,14] and it is suitable to efficient implementations [19,16].
4 Hanif Bhuiyan et al.
There are several situations in conflict within the Queensland traffic rules,
which may create problems for automatic traffic rule reasoning regarding AV.
For example, in Traffic rules, Part 12, Division 3, Rule 170 says
(2) A driver must not stop on a road within 20m from the nearest point
of an intersecting road at an intersection with traffic lights, unless the
driver— (a) stops at a place on a length of road, or in an area, to which
a parking control sign applies; and (b) is permitted to stop at that place
under this regulation.
Also, in Part 12, Division 1, Rule 165 recites
the driver may stops at a particular place, or in a particular way because
the condition of the driver, a passenger, or the driver’s vehicle makes it
necessary for the driver to stop in the interests of safety, and the driver
stops for no longer than is necessary in the circumstances.
These two rules can contradict each other regarding stopping on a road. The
main purpose of these examples is not to show the difference in traffic behaviour
but rather to find the solution to follow the appropriate rule in such different
situations. These type of conflicts often may be solved by the explicit priority
rules. However, Leens and Lucivero [20] noticed that subtle conflict arises in
the rules that may not be solved by rule priorities. To solve such issues, there
is one effective approach by [10], which works based on logic using a suitable
variant. Regarding such issues, approaches by [18], [22] are also well known in
nonmonotonic logic areas.
2 Traffic Rules Encoding
The proposed methodology consists of four modules as shown in Figure 2. Here,
we introduce our methodology to translate the traffic rules into the semantic
logical format. The input of the system is traffic rules (natural text) as shown in
Figure 1(a). In the first module, we define atoms from the rules. In the second
module, we determine the norms. The if-then structure is identified and generated
from the rules in the third module. Finally, we use Defeasible Deontic Logic
(DDL) on the atoms, norms and if-then structure to make the semantic logical
format. The modules are explained below. An example of making a semantic
logical format of traffic rules is shown in Figure 1.
Define Atoms This section outlines in brief that how we define atoms from rules.
An atom is a predicate symbol including constants or variables that contains no
logical connectives. Here to define atoms, we use terms of the rule sentence. A
term is a variable or an individual constant in the sentence. This work deals with
those variables and constants that refer to subject (s), predicate (p), property
(pr), object (o), and qualifier (q) (Figure 3) in the rule sentence.
In natural language a subject is refers to that term about which something is
said in the sentence. The something which is said about something is the predicate
A Methodology for Encoding Regulatory Rules 5
Fig. 2. Workflow for traffic rules encoding.
of the sentence. The predicate of a subject-predicate sentence indicates relation or
a property. The object is what that subject does something to. In another word,
object are the results of action. Qualifiers are that terms which usually enhance
or limits the other word meaning. In one sense it can be said as an adverb of the
sentence. Before generating terms, some article preprocessing are done on the text.
Like, we are not considering the verbs (auxiliary, principal and modal) for this
task. In logic, Subjects are variable or constant in the rule sentence that refers to
the entity always. Predicate always refers to the properties or action of entities.
Properties indicate the relation between subject and predicate. Object refers to
Fig. 3. Terms of the traffic rules.
6 Hanif Bhuiyan et al.
the properties of the entities. Qualifiers refer to the variable that enhances or
limits the entities.
Atom is a combination of these terms and composed as a statement which
only can be evaluated as true or false. For example “The bus breaks the traffic
rule”. According to the linguistic perspective, the term ‘bus’ is the subject of
the sentence as this sentence is about this. The term ‘traffic rule’ is the object of
the sentence as the subject is doing something to it. The term (verb) ‘breaks’
is the predicate of the sentence as it expresses the relation between the subject
(bus) and object (traffic rule). In logical approach, ‘bus’ is that variable (subject)
which refers the entity of the sentence. ‘breaks’ is the predicate constant which
refers the action of the entity. ‘traffic rule’ is an individual of the sentence which
refers the properties of the entity. So in a logical way, we can represent the above
example as Predicate (subject, object): B(b,t): Breaks (bus, truck). Therefore the
atom can be represented as Subject-Predicate-Object: bus Breaks theTrafficRule.
In traffic rules, the rule structure are not equally structured. Due to this
heterogeneity of the rules information, the atom structure varies. An example of
identifying terms and defining atom from the traffic rule is shown in Figure 1
(b) & (c). Throughout the empirical study of the Queensland Traffic Rules, we
semantically define the atom in terms of five aspects which are: Subject-Predicate-
Object; Subject-Predicate-Qualifier-Object; Subject-Property, Subject-Predicate-
Object-Object; Subject-Qualifier-Predicate-Object. Some examples are shown
below.
Example 1 : Overtaking traffic rule section 151A: 3 (c) “the rider rides in a
school zone”.
Subject Predicate Object
the rider rides in school zone
Generated Atom: rider IsRidingIn schoolZone.
Example 2 :Overtaking traffic rule section 140: b “the driver can safely overtake
the vehicle”.
Subject Predicate Qualifier Object
the driver overtake safely the vehicle
Generated Atom: driver CanSafelyOvertake vehicle.
Determining Norms Norms stipulate the conditions in the rule to perform
specific actions. Every norm is represented by one or more rules, which could
either constitutive or prescriptive rules [9]. Constitutive rules define the terms
specific to legal documents, whereas prescriptive rules are used to encode the
obligation, permission, and prohibition, . . . , and the conditions under which they
are entering into force to follow according to legal document. An obligation is a
type of legal requirement where the subject has to perform an action otherwise
a violation is triggered, whereas prohibition is about an action that cannot be
performed; if it is performed, then the result represents a violation.
A Methodology for Encoding Regulatory Rules 7
Prescriptive rules are determined based on conceptual semantic understanding
and some special keywords, which are “must”, “must not”, “should”, “ought”, etc.
For prescriptive rules, we consider only obligation, prohibition and permission
norms. We identify the constitutive rules through our knowledge of understanding
and descriptive notions in the sentences. Some examples of descriptive notions are
“it is”, “means”, “does”, “does not”, etc. An example of determining prescriptive
and constitutive norms from overtaking traffic rules, is shown Figure 4.
Fig. 4. An example of determining norms.
Generate if-then Structure Typically rules contain conditions and norms,
which control the behaviour of the subject. From a legal perspective, rules use
conditions on some actions to achieve particular behaviours. Rules can be analysed
based on the subject’s behaviour and the circumstances [9]. In AI & Law domain
it is widely accepted that norms have if-then conditional structure [27]. Therefore,
for encoding traffic rules, we make the conditional structure using the atoms and
norms (prescriptive or constitutive).
if (X1 , . . . , Xn )
then [norms]
{Y }
Here, (X1 , . . . , Xn ) is the antecedent (premises), and Y is the conclusion (con-
sequent) of the rule. In a traffic rules context, norms represent the situation
of actions. Some actions are mandatory to follow; some are not. For example,
“Rule 144A: Keeping a safe lateral distance when passing bicycle rider”. The
rule expresses an obligation for the driver while passing a bicycle rider. A simple
example of generating an if-then structure is shown in Figure 5.
8 Hanif Bhuiyan et al.
Fig. 5. An example of making if - then structure from rules.
Rules Encoding Defeasible Deontic Logic (DDL) is the combined form of
defeasible logic and deontic logic. DDL can deal with both normative and de-
feasible reasoning based on defeasible logic [23,3] and deontic logic. We identify
and combine atoms, norms, and if-then structure using DDL to create semantic
logical format (machine-computable) of the rules. A brief overview of how DDL
is used to represent traffic rules is given below.
Defeasible theory consists of five distinct knowledge foundations: strict rules,
facts, defeasible rules, superiority relations, and defeaters [3]. The theoretical
notion of defeasible logic (DL) is D (F, R, ) where, F is a set of facts, R is a set
of rules, and is a superiority relation over R.
DL consists of a finite set of literals, where a literal is either an atomic
proposition or its negation. Given a literal X, ∼X denotes its complement. That
is, if X = Y then ∼X = ¬Y , and if X = ¬Y then ∼X = Y .
Facts (F ) are conclusive and unambiguous statements. A fact is represented
either in the form of state affairs (literal or modal literal) or actions that have
been performed and are considered as always true. For example, “Honda is a
Motorbike”, is represented by: Motorbike(Honda).
A rule (r ∈ R) describes the relationship between premises and conclusion and
we can specify the strength of this relationship. Based on the relationship strength
of the rules, we can differentiate defeasible rules, strict rules and defeaters [12].
These rules are represented by the following expressions X1 , . . . , Xn → Y (Strict
Rules), X1 , . . . , Xn ⇒ Y (Defeasible Rules) and X1 , . . . , Xn ; Y (Defeaters),
where X1 , . . . , Xn is the antecedent or premises (clauses) and Y is the consequent
or conclusion (effect) of the rule. Besides these, rules also contain free variables
which are interpreted as the ground instances.
Strict rules are rules in the classical sense: whenever the premises are indis-
putable (e.g. a fact) so is the conclusion. For example, “a motorbike is vehicle,”
formally: Motorbike(Honda) → Vehicle(Honda).
Defeasible rules are rules that can be defeated by contrary evidence. An
example of a traffic rule is: “Part 11 Division 4 Section 151 B (3): the rider is
A Methodology for Encoding Regulatory Rules 9
taken to have unlawfully edge filtered along the length of road—(a) the rider
does not hold an O type licence for the class of the motorbike”. In summary, a
motorbike can edge filter if the rider holds an O type licence, so formally we can
write: Motorbike(Honda) ⇒ EdgeFilteringVehicle(Honda). From this information,
it can be concluded that a motorbike can edge filter on the road unless there is
any evidence is provided that the motorbike cannot edge filter. A defeasible rule
with empty premises would be considered as a presumption.
Table 1. A snippet of Turnip about defeasible reasoning.
Atoms:
Atom Motorbike “Honda is a motorbike”
Atom Vehicle “Honda is a vehicle”
Atom EdgeFilteringVehicle “Honda can Edge Filter”
Atom rider HoldOTypeLicence “The rider hold O Type Licence”
Atom Learner “Who is just learning driving”
Atom Provisional “Who know driving but not yet become professional driver”
Atom Professional “Who is the professional driver”
Rules:
r0:⇒ rider HoldOTypeLicence
Motorbike → Vehicle
Motorbike ⇒ EdgeFilteringVehicle
¬ rider HoldOTypeLicence & Vehicle ; ¬ EdgeFilteringVehicle
r1: Learner | Provisional → ¬ rider HoldOTypeLicence
r2: Professional → rider HoldOTypeLicence
r1 >>r0
r2 >>r1
Result:
Facts:
¬(rider HoldOTypeLicence)
Learner
Motorbike
Provisional
Learner
Vehicle
Provisional
Motorbike
Professional
Professional
rider HoldOTypeLicence
Defeaters are rules, that are used to prevent the conclusion. As an example,
¬(rider HoldOTypeLicence Vehicle(Honda)) ; ¬EdgeFilteringVehicle(Honda).
From this rule we can state that Honda is an edge filtering vehicle but if the
rider of a Honda does not hold a O type licence, then it cannot edge filter on
the road. This statement can prevent the conclusion of edge filtering. This is not
also supporting the no edge filtering.
10 Hanif Bhuiyan et al.
Defeasible Logic is a non-monotonic, skeptical approach that does not support
a contradictory conclusion. It aims to resolve the conflicts between knowledge.
For example, suppose there is information, and it has support to conclude A,
but also there is information which does not support A and prevents it from
concluding A. If the support for A has priority over ¬A , then it might be possible
to conclude A. In such scenarios no conclusion can be made unless the rules are
prioritized. The superiority relation ( ) used the priority set among the rules,
where one rule may override the conclusion of other rules. An example of edge
filtering concept using the Turnip Engine is shown in Table 1.
In Table 1, we can see that, no conclusive decision can be made until we use
the superiority relation . Without using the superiority relation, we can only
conclude that Honda is an Edge Filtering vehicle. After using the superiority
relation like r1 r0 and r2 r1, then we can conclude that (as in the result
section of Table 1) a professional rider can Edge Filter if he/she has a O Type
Licence but learner or provisional rider cannot edge filtering.
In addition to defeasibility, traffic rules also engage with deontic concepts,
which are obligation (O), permission (P), and prohibition (F). For example,
considering the concept “overtake”, we can define these notions as:
[F]overtake ≡ [O]¬overtake
[O]overtake ≡ [F]¬overtake
[P]overtake ≡ ¬[O]¬overtake
An example of representing traffic rules using DDL is given below. For this
example, we use the Queensland Overtaking traffic rules (Part 11 Division 3 rule
140 in Traffic Rules).
∅ (Empty Set) ⇒ [F] Overtake
driver (HasClearViewOf approachingTraffic)
∧ driver (CanSafelyOvertake vehicle) ⇒ [P] Overtake
A simple and complete example of encoding Overtaking Traffic Rules: 141 (1)
using Defeasible Deontic Logic (DDL) is shown in Figure 1.
3 Limitation
This work uses Defeasible Deontic Logic (DDL) to define a logical semantic
representation of traffic rules; however, there are some issues regarding the
accuracy and completeness of the representation. Given the sophisticated and
varied nature of traffic rules, identifying all the terms, norms, rule types and
conditions is a challenging task, as these components found in explicit or implicit
linguistic forms. As noted, the generic interpretation is not represented here. Only
five different aspects of combination between subject, predicate, object, property,
A Methodology for Encoding Regulatory Rules 11
and qualifier are considered for defining atoms. In terms of norms determination
from rules, only explicit types of norms (obligation, permission, and prohibition)
are considered, although there might be different types of permission and other
normative effects [14]. There are some questions which may arise regarding the use
of defeasible deontic logic in this work. As we did not evaluate the methodology
based on any gold standard or any other approaches. Despite the above issues,
there are significant advantages of our proposed encoding methodology, which are
domain independence and scope of applicability. This methodology can be used
in other domains such as anti-money laundering rules and regulations, university
rules and regulations, etc.
4 Conclusion
Encoding the complex and varying nature of traffic rules is a challenging task. Any
wrong encoding of the rule can adversely effect on the reasoning process. Therefore,
we proposed a Defeasible Deontic Logic (DDL) based encoding methodology for
translating traffic rules into a semantic logical format (machine-computable),
which can be used for automatic traffic rule reasoning to validate automated
vehicle legal behaviour. The methodology incorporates the components and
behaviour of regulations such as atoms (defined from terms), norms, and if-then
structure to analyse the rule content as well as identify the actions and activities
of the rules. DDL is applied to the characteristics of the rules to resolve conflicts
and understand the norms and exceptions more explicitly. In the future, we plan
to enhance the scale and scope of this proposed methodology. We intend to cover
all possible combinations of terms and normative effects for this task. Besides
work on the overall traffic rules, we also plan to work on other domains to make
this encoding methodology more efficient and standard in the field of Law and
AI research.
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