=Paper=
{{Paper
|id=Vol-2418/AIC18_paper6
|storemode=property
|title=Handling Robot Sociality: A Goal-based Normative Approach
|pdfUrl=https://ceur-ws.org/Vol-2418/paper6.pdf
|volume=Vol-2418
|authors=Patrizia Ribino,Carmelo Lodato,Ignazio Infantino
|dblpUrl=https://dblp.org/rec/conf/aic/RibinoLI18
}}
==Handling Robot Sociality: A Goal-based Normative Approach==
https://ceur-ws.org/Vol-2418/paper6.pdf
Handling robot sociality: a goal-based normative
approach
Patrizia Ribino, Carmelo Lodato, and Ignazio Infantino
Istituto di CAlcolo e Reti ad Alte prestazioni (ICAR)
Consiglio Nazionale delle Ricerche, Italy
{patrizia.ribino,carmelo.lodato,ignazio.infantino}@icar.cnr.it
Abstract. The increasing development of service robots devoted to vari-
ous functions arisen the need to demonstrate additional social capabilities
beyond their primary functionality. For improving robot sociality, among
other abilities, robots need to implement the capability to interact with
humans using the same principles as humans do following social norms.
In this work, we propose an extension of a goal-based normative frame-
work to cover new abstractions such as qualitative goals, social norms,
and expectations which constitute essential elements for handling robot
sociality. Moreover, we have integrated such extended normative frame-
work into a Nao robot platform. An implementation of the proposed
framework is described and tested in a simulated environment.
Keywords: social robots, social norms, normative reasoning
1 Introduction
As result of the increasing progress in the field of the Artificial Intelligence,
robots are expected to become more and more available in everyday environ-
ments. Among several issues, the integration of robots into the society depends
on their capability to demonstrate socially acceptable behaviours to be perceived
by humans as suitable partners in collaborations. To define socially acceptable
actions, we refer to the branch of socio-cognitive theory that has documented
the existence of two orthogonal dimensions in social judgement [1, 2].
The social judgement of an individual can be represented through two com-
ponents: social utility and social desirability. Social utility refers to individuals’
capacity to satisfy the functional requirements of a given social environment.
It varies along an incompetence-to-competence horizontal axis that corresponds
to the perceived ability of the social target to reach social success. It pertains
to adaptive traits like skilled/unskilled, proactive/passive. For example, self-
sufficiency [2] and being focused on goal achievement [3] are perceived as socially
useful behaviours. On the other hand, social desirability refers to the degree of
likeableness of a person in his/her relationships with others in a given social
environment. It varies along an unlikability-to-likability vertical axis, that cor-
responds to the perceived ability of the social target to gain social approval. It
concerns aspects such as polite, honesty, respect, only to cite a few.
2 Ribino et.al
According to Sommet et.al [4], socially useful behaviours are typically those
described as focused on the self for sustaining the practical fulfilment of one’s
goals. Conversely, socially desirable behaviours are those defined as directed on
the others, that involve benevolent interaction styles. From humans perspective,
showing the social utility of a robot can be easier than perceiving him as socially
desirable because in this latter case it is necessary to demonstrate additional so-
cial capabilities beyond robot’s primary functionality. Indeed, the social utility
of service robots deployed for various functions in public spaces such as airports,
hospitals, logistic warehouses is readily perceived by humans. Conversely, to be
socially desirable, robots [5] need to show not only ”human social” features (like
the expression of emotions, ability to conduct high-level dialogue, to develop
personality and social competencies and so on), but also capabilities to interact
using the same principles as humans do. As it arisen from cognitive and social
science, human interactions are fundamentally based on normative principles.
For example, many forms of interaction are institutionalised and pertain to the
political and economic structures of the society that are defined by rules and
prescribed by laws that enforce behaviour [6]. Other types of interaction are
based on conventions, such as on what side of the road people should drive [7].
Finally, most human interactions are often influenced by more profound social
and cultural standards, so-called social norms [8]. A social norm is commonly
seen as [8]: a rule of behaviour such that individuals prefer to conform to it on
condition that they believe that (a) most people in their relevant network con-
form to it, and (b) most people in their relevant network believe they ought to
conform to it and may sanction deviations. Norms directly identify possible ac-
tions as desirable or undesirable in a given community and a particular context
involving social expectations and guiding the choice of people’ actions [9, 8]. So-
cial expectations are people’s beliefs about other people’s behaviours and beliefs
in certain situations. Beyond social norms, expectations play an important role
in regulating social behaviours. Indeed, an individual may comply with social
norms in the presence of relevant expectations, but (s)he does not follow them
in their absence [8].
A current challenge is how to incorporate norm processing into robotic archi-
tectures, because it requires addressing several issues such as the specification of
social norms, how they can be activated, how social plans can be generated for
expressing social behaviours, the conflicts resolution and the acquisition of new
norms [9]. The contribution of this paper is to address the social desirability as
the ability of a robot to show itself as conforming to social norms. In so doing,
we extended the approach we have presented in [10] by introducing the concepts
of qualitative goals and revising the concept of achievement goals for modelling
different objectives of social robots. Then, we extended the definition of norms
to cover the peculiarities of social norms by introducing desirable operators and
expectations. Finally, we have integrated our normative framework into a robotic
platform. An implementation of our approach is tested in a simulated humanoid
robot Nao by using Choreographe [11], a user-friendly application for controlling
robots, creating behaviours and accessing data acquired by the sensors.
Improving robot sociality: a goal-based normative approach 3
The rest of the paper is organised as follows. Section 2 presents an overview of
the related works. Section 3 presents the theoretical foundations of the proposed
approach. Then, in Section 4 a case study about robot sociality is presented.
Finally, in Section 5 conclusions are drawn.
2 Related Works
Shortly, social robots will play an ever more significant role, working for and in
cooperation with humans. In so doing, they should show social capabilities [5]
such as interacting with humans naturally. An emerging challenge is to provide
a robot with the normative reasoning to behave in compliance with the same
social norms as humans do. To the best of our knowledge, only a few recent
works address such issue in an explicit and general way, and a lot of work must
still be done to incorporate sophisticated norm processing into robotic architec-
ture. In [12], authors present a framework for planning and execution of social
plans, in which social norms are explicitly represented in a domain and language-
independent form. In [13], Brinck et.al discuss the role of the social norms in the
design of human-robot interactions focusing on the dynamic information that
a robot needs to comply with social norms. In particular, they pay attention
to three elements (gaze and face, place in space, and orientation, posture and
movement) that are important as sources of social information. An initial step
toward a cognitive-computational model of norms by delineating core properties
of the human norm system, contrasting two models of a computational norm sys-
tem, and deriving implications for how robotic architectures would implement
such a norm system is described in [9].In such work, authors focus on modelling
norms as directly and indirectly connected networks discussing mechanisms of
co-activation of rules that are connected to other norms. Finally, in [14] an ap-
proach to creating a computational model of social norms based on identifying
some values that are considered relevant in some culture. Appropriate metrics
quantify such values. Social norms are used as the requirement for maximising
such metrics. In so doing, authors introduce a model for concrete beliefs of the
actors that are relevant to the social scene.
In this work, we propose a normative approach that allows exploiting the
advantages of goal modelling to make social robots able to reason about dynamic
situations pro-actively. In so doing, we suggest the concept of quality goals for
modelling the pursuit of social values by a robot. Then we define social norms by
introducing desirability operators for representing preferences about acceptable
behaviours. Finally, we define the expectations formally as a new mental concept
a robot sees as a motivator for pursuing social values by following social norms.
3 Goal-based normative framework for social robots
A widely-accepted approach for developing intelligent agents (both robots and
bodiless agents) is a cognitive approach, where agents are modelled using men-
tal concepts such as beliefs, goals, plans, rules and so on. Among them, to de-
4 Ribino et.al
velop agents able to reason about dynamic contexts pro-actively, a fundamental
abstraction is the concept of goal. Rich literature addresses issues about goal
modelling for intelligent agents, defining a great variety of goal’s types [15]. The
achievement goal is the most used kind of goal. It models the most recurring
functional requirements of this kind of systems. The well known cognitive defi-
nition is [15]: an achievement goal represents a desired state of the world that
an agent wants to reach.
On the other side, for providing intelligent agents with normative reasoning,
a fundamental abstraction to be modelled is the concept of the social norm.
Social norms are behavioural rules considered acceptable in determined contexts,
which refer to the standard of desirability in a community. Thus, social norms
are behavioural expressions of abstract social values (such as politeness, dignity,
hospitality, honesty, etc.) that underlie the preferences in a group in various
situations. For example, the norm ”everyone should to queue to ticket office”
involves the social values of equality, efficiency and respect for orderliness. In
other words, widely accepted social values provide the grounds for complying or
rejecting certain behavioural norms.
Among several functions that social norms serve in the society, they mainly
provide guidelines for expected modes of social behaviour. Thus, for keeping
society functioning, an important role is played not only by the direct rules but
also by the expectations about the conduct of the members of the society. If
few members of the group follow the norm (e.g., do not use a cellphone during
class), then the norm is weakened, and it may be no longer treated as binding.
If few members of the group expect others to follow the instruction, it becomes
optional and loses its character as a norm. These peculiarities distinguish norms
from goals because the latter can hold even when individuals disregard entirely
other community expectations.
A cognitive definition of social norm [16, 9] states that:
- An agent represents the instruction to [not] perform a specific action or
general class of action.
- An agent believes that a number of individuals in the group in fact (do not)
follow the norm.
- An agent believes that a sufficient number of individuals in the groups ex-
pects others in the group to (not) follow the norm.
In this work, we want to add a further condition to the previous ones. So that
a social robot conforms its behaviour to social norms of a group as humans do,
it has also to share the same social values as the members of that community.
Thus, we extend the previous definition with the following condition:
- An agent wants to pursue a social value.
To incorporate norm processing into social robots, we propose a goal-based
normative approach that extends our previous work [10] for covering different
aspects both functional and non-functional of a social robot. In particular, to im-
plement the feature of sociality, we extended the definition of the norm to cover
Improving robot sociality: a goal-based normative approach 5
also the peculiarities of the social norms by introducing the concept of expecta-
tion. Moreover, we have proposed the notion of qualitative goals for modelling
the pursuit of social values.
Before defining norms and goals, we need to introduce the definition of the
state of the world [10] that is fundamental for the following. The state of the
world represents a set of declarative information about events occurring within
the environment and relations among events at a specific time. An event can be
defined as the occurrence of some fact that can be perceived by or be commu-
nicated to an intelligent agent. Events can be used to represent any information
that can characterise the situation of an interacting user as well as a set of
circumstances in which the intelligent agent operates at a specific time.
Definition 1 (State of the world).
Let D be the set of concepts defining a domain. Let L be a first-order logic defined on D
with > a tautology and ⊥ a logical contradiction, where an atomic formula p(t1 , t2 ..., tn )∈L
is represented by a predicate applied to a tuple of terms (t1 , t2 ..., tn )∈D and the predicate
is a property of or relation between such terms that can be true or false.
A state of the world in a given time t (W t ) is a subset of atomic formulae whose values
are true at the time t:
W t = [p1 (t1 , t2 , ..., th ), ..., pn (t1 , t2 , ..., tm )]
Definition 1 is based on the close world hypothesis that assumes all facts that
are not in the state of the world are considered false. In the next sections, we
introduce the elements of the proposed approach.
3.1 Types of Goals
An Achievement goal represents the desired state that has to be achieved. They
express goals which are not currently fulfilled, and which the agent, pursuing
the appropriate actions, acts to reach them. To define, an achievement goal we
extended the general definition of goal proposed in [10].
Definition 2 (Achievement Goal). Let D, L and p(t1 , t2 ..., tn )∈L be as previ-
ously introduced in the definition 1. Let tc ∈L, fs ∈L and fc ∈L be formulae that may be
composed of atomic formulae by means of logic connectives AND(∧), OR (∨) and NOT
(¬). An Achievement Goal is a triple htc , fs , fc i where tc (trigger condition) is a condition
to evaluate over a state of the world W t when the goal may be actively pursued, fs (final
state) is a condition to evaluate over a state of the world W t+∆t when it is eventually
addressed, fc (failure condition) is a condition to evaluate over a state of the world W t+∆t
when the goal is no longer applicable. An achievement goal is:
i) active if tc (W t ) ∧ ¬fs (W t ) = true
ii) addressed if fs (W t+∆t ) = true
iii) is dropped if fc (W t+∆t ) = true
On the contrary, a qualitative goal is a kind of goal that is perceived more
than fulfilled. It is a goal for which satisfaction criteria are not defined in a
clear-cut way.
6 Ribino et.al
Definition 3 (Qualitative goal). Let D, L and p(t1 , t2 ..., tn )∈L be as previously
introduced in the definition 1. Let tc ∈L and fs ∈L be formulae that may be composed of
atomic formulae by means of logic connectives AND(∧), OR (∨) and NOT (¬).
An qualitative goal is a tuple htc , qs , sc , fc , i where tc (trigger condition) is a condition
to evaluate over a state of the world W t when the quality goal may be actively pursued,
qs (qualitative state) is the state to head toward, sc (suspending condition) is a condition
to evaluate over a state of the world W t+∆t when the quality goal has to be suspended,
fc (failure condition) is the condition to evaluate over a state of the world W t+∆t when
the goal is no longer applicable. A qualitative goal is:
i) active if tc (W t ) ∧ ¬mc (W t ) ∧ ¬sc (W t ) ∧ ¬fc (W t ) = true
ii) suspended if sc (W t+∆t ) ∧ ¬fc (W t+∆t ) = true
iii) dropped if fc (W t+∆t ) = true
A qualitative goal never quite reach the state it is heading toward, but instead
get closer and closer. Thus, a qualitative goal when activated will be continuously
pursued until it is suspended or dropped.
In the context of social robots, the concept of achievement goals allows us to
represent functional requirements that a social robot has to be able to satisfy.
Conversely, a qualitative goal enables us to describe the pursuit of a social value
that can not be described by mean a clear condition to be reached. An agent has
to continuously perform actions that give positive contributions to sustaining a
quality state.
3.2 Social Norms
In the following, we provide an explicit representation of social norms and robot’s
expectations. In particular, we adapt the definition of norm presented in [10]
for representing social norms introducing the desirability operators: Desirable,
Undesirable and Indifferent [17]. Desirability operators represent preference in
a wide sense. The following principles underpin the desirable operators:
ρ↔ψ then Des ρ ↔ Des ψ (1)
Des ρ → ¬Des ¬ρ (2)
Des ρ → ¬Undes ρ (3)
In our context, ρ designates a proposition that asserts that an act or a state
of affair is done or reached. Thus, Des ρ is read as ”it is desirable that the
situation described by the descriptive sentence ρ is realised”. In particular, (3)
expresses that something cannot be desirable and undesirable at the same time.
Definition 4 (Social Norm). Let D, L, and p(t1 , t2 ..., tn )∈L be as previously
introduced in the definition 1. Let φ∈L and ρ∈L be formulae that may be composed of
atomic formula by means of logic connectives AND(∧), OR (∨) and NOT (¬). Moreover,
let Desop = {Desirable, U ndesirable, Indif f erent} be the set of desirability operators.
A Social Norm is defined by the elements of the following tuple:
n = hp, qs, ρ, φ, d i where
Improving robot sociality: a goal-based normative approach 7
- p is a Position the norm refers to. A Position indicates a status of an individual in a
society. The symbol means that norms refers to anyone.
- qs is a quality state. It represents the social value the norm underlying.
- ρ∈L is a formula expressing the set of actions and/or state of affairs that the norm
disciplines.
- φ∈L is a logic condition (to evaluate over a state of the world W t ) under which the norm
is applicable.
- d ∈Dop is the desirability operator applied to ρ that the norm prescribes for sustaining the
quality state qs in a state of the world W t+∆t :
t+∆t
ρ(W
) = true if d = Desirable
d(ρ) ≡ ¬ρ(W t+∆t ) = true if d = U ndesirable (4)
ρ(W t+∆t ) ∨ ¬ρ(W t+∆t ) = true if d = Indif f erent
Let us consider a society where the politeness is considered a shared social
value. Thus, a social norm such as the following ”It is desirable that a guy gives up
his seat if an elderly person is standing up” prescribes an acceptable behaviour.
According to the definition 5, the previous norm is applied to a guy (i.e., the
position) and it prescribes that, in a given state of the world where an elderly
person is standing up (i.e. : φ(W t ) = true) then the action ”give up own seat” is
expected to be true in a consecutive state of the world (i.e. : ρ(W t+∆t ) = true).
As said before, a further important role is played by the expectations. By
definition, the preference to conform to a social norm is conditional, it implies
that one may comply with a social norm in the presence of the relevant expecta-
tions, but do not obey the norm in the absence of such expectations. We initially
considered such expectations in a broad sense as a motivator for pursuing a so-
cial value, thus leading an agent to follow the related social norms. Conversely,
repeated negative feedbacks about its expectations cause the loss interest in that
social value, thus ignoring social norms 1 . In this work, an expectation is gener-
ated by certain circumstances, and it is satisfied when the expected state is true
in a consecutive state of the world before its time to fulfil (if any).
Definition 5 (Expectations). Let D, L, and p(t1 , t2 ..., tn )∈L be as previously
introduced in the definition 1. Let n = hp, qs, ρ, φ, d i be a Social Norm. Let tc ∈L and
es ∈L be formulae that may be composed of atomic formulae by means of logic connec-
tives AND(∧), OR (∨) and NOT (¬). An Expectation is a couple hn, es i where n =
hp, qs, ρ, φ, d i is the social norm generating the expectation and es (expected state) is
a condition to evaluate over a state of the world W t+∆t when expectation is eventually
satisfied. Moreover, an expectation may have time to fulfilled (ttf ), that is the time within
which the expected status must occur so that the expectation can be considered satisfied.
An Expectation is:
- generated if ρ(W t ) = true
- satisfied if es (W t+∆t ) = true
1
In this paper, we give a simple role to the expectations, but we conceived them to
be employed in most complex reasoning
8 Ribino et.al
3.3 Reasoning on Social Norms
The following algorithms provide the reasoner for a social robot for deciding to
comply with social norms according to its expectations. Algorithm 1 is the core
of the reasoner. The triple of elements it works is: the state of the world W t , the
set of social values the robots wants to pursue represented by a set of qualitative
goals (QG), and a set of social norms N . The state of the world W t may change
during system execution because the robot may perform some actions, it may
perceive environmental changes, or it may capture events deriving from human
interactions. For each active qualitative goal, the set of the related active social
norms are considered. The most simple case is the presence of a single norm
(Step A ). In this case, a desired final state is created according to the desirable
operators. Analogously, Step B allows for creating a desired final state by merg-
ing the different state of affairs the norms discipline. Thus, an achievement goal
is generated by starting from the quality goal for reaching the new desired final
state. After pursuing such goal, the new state of the world is updated.
Algorithm 1: Follow Social Norms
Data: W t , QG, N
foreach QualitGoali ∈ QG do
QualitGoali ← htci , qsi , sci , fci i;
if QualitGoali is active then
Ni ← {n ∈ N : n = hp, qsi , ρ, φ, di ∧ φ(W t ) = true};
A if card{Ni } = 1 then
n ← hp, qsi , ρ, φ, di ;
if d = Des then
fs ← ρ;
if d = Undes then
fs ← ¬ρ;
if d = Indiff then
fs ← ρ ∨ ¬ρ;
B if card{Ni } > 1 then
fs ← ∅ ;
foreach nh ∈ Ni do
nh ← hp, qsi , ρh , φh , dh i;
if dh = Des then
fs ← fs ∧ ρ;
if dh = Undes then
fs ← fs ∧ ¬ρ;
if dh = Des then
fs ← fs ∧ (ρ ∨ ¬ρ);
AchievGoal ← htci , fs , fci i;
pursue(AchievGoal);
update(W t , fs );
Algorithm 2 allows for evaluating the expectations that following a given
norm may raise. In this first implementation, we consider that a robot has a
satisfaction threshold that is decreased each time its expectation has not been
satisfied after the time to fulfil. When its satisfaction reaches its lowest value,
the related quality goal is dropped.
Improving robot sociality: a goal-based normative approach 9
Algorithm 2: Evaluate Expectations
Data: W t , EX P
foreach expk ∈ EX P do
hn, esk i ← expk ;
n ← hp, qs, ρ, φ, di;
QG ← htc , qs , sc , fc i;
if expk is generated then
initT imeQs ← getCurrentT ime();
update(W t , SensorData);
satisf ied ← evaluate(esk , W t );
if (currentT ime − initT imeQs) > ttf ∧ satisf ied = f alse then
T hresholdQs = T hresholdQs − 1;
if T hresholdQs = 0 then
update(W t , fc );
We want to highlight that the robot, in this case, does not follow all norms
that are underlying the dropped goal. For example, if a robot wants to be polite,
it tries to follow the social norms related to politeness such as say hello, thank
you, sorry, etc. When the robot says hello, it expects that people greet it. Anal-
ogously, if the robot helps someone, it expects that the other person say thank
you. If its expectations are continuously unsatisfied, similarly it could not say
sorry when bumping into someone. It loses the motivation to follow the corre-
lated norms because it thinks in this case that politeness is not a social value
for the community of people it is interacting.
4 Case Study: Modelling robot sociality
In this section, we provide a simple case study for describing how a social robot
may behave in some situations that involve social norms. To model robot and
human objectives, we use the goal model diagram [20] where goals can be anal-
ysed, from the perspective of an actor, by Boolean decomposition, Contribution
analysis, and Means-end analysis. Decomposition is a ternary relationship which
defines a generic boolean decomposition of a root goal into sub-goals, that can
be an AND or an OR decomposition. Contribution analysis identifies goals that
can contribute positively or negatively towards the fulfilment of other goals. Fi-
nally, the Means-end relationship is also a ternary relationship defined among
an Actor a goal and a task, representing the means which will be able to satisfy
that goal. Practically, it provides the operationalisation of the goal.
The goal diagram showed in Fig. 1 represents the goal model related to a
robot and human and their relations for the proposed case study. In the scenario
under study, a robot has to go to a postal office for sending something on behalf
of its owner. Thus, the robot has to reach the office, wait its turn sitting if there
is a free chair, then talk with the postal employee for sending its item. In such
scenario, some social norms regarding public behaviour have been made known
to the robot. Such as i) It is desirable to kindly greets when you meet someone;
ii) It is desirable to say ”I’m sorry” if you hit or bump into someone by accident;
iii) It is desirable to be kind to the elderly, giving up your seat. Thus, besides
its functional objectives related to the physical tasks the robot is involved, a
10 Ribino et.al
Robot Goals
Human Goals Human Robot
AND
OR
- To be social Send a Packet
Human wants Human does not
interact with want interact - + + AND AND
Robot with Robot
OR
To be
Go to the Talk with
To be To be compliant with Wait for turn
Postal office Employee
compliant with uncompliant with Social Norms
Social Norms Social Norms
Follow Evaluate Move
Social Expectat to Dialog
Norms ions target
Sit Stand
Wait
Down Up
Keys
Achievement Qualitative Actor AND/OR Means-ends Positive Negative
Task Decomposition Link Contribution Link + Contribution Link -
Goal Goal Boundary Link
Fig. 1: Goal Model
qualitative goal represents the interest of the robot to be social. A goal that can
contribute positively to reach this qualitative goal is to be compliant with social
norms. Such goal is reached by accomplishing two tasks: follow social norms 2
and validate its expectations. Conversely, from the perspective of an individual,
(s)he may have the interest to interact or not with the robot. In the latter case,
the lacking of interactions negatively contributes to the fulfilment of the robot’s
qualitative goal because it lacks the fundamental requirement for being social
that are the interactions. Conversely, an individual can have the interest to
interact with a robot, but he/she can choose to be compliant with social norms
or not thus violating the expectation of the robot. An individual that behaves
conforming to the social norms favourites the robot’s sociality because they
satisfy the generated expectations of the robot. From the robot’s perspective,
the satisfaction of its expectations is a measure of the appropriateness of its
behaviour. Conversely, individuals that do not follow social norms, thus not
satisfying the expectations of the robot, could weaken the robot’s belief about
the appropriateness of the adopted social behaviour and cause him to change his
attitude. In the initial implementation presented in this work, the robot suspends
its interest in pursuing the social value underlying the disregarded social norms.
According to our formalisation, the goal ”To be social ” and the above norms
can be represented as follows 3 .
G1 : qualitative goal(condition(want(be social)),state(be social),
condition(), condition(¬ want(be social))
2
This is a generic task meaning that according to the social norms the appropriate
task will be performed. In our case study, for example, the robot has to be able to
greet, apologize and standing up for giving its seat.
3
For space concern, we avoid to represent the achievement goals that are not relevant
for understanding the case study.
Improving robot sociality: a goal-based normative approach 11
N1 : norm(position( ),state(be social),state(greet),condition(is(person)),
type(desirable))
N2 : norm(position( ),state(be social), state(sorry),condition(bumped(
person),type(desirable))
N3 : norm(position( ),state(be social), state(standup),condition(
and([is(person,old),state(sitted)])),type(desirable))
The proposed case study has been tested in Choreographe by using a simu-
lated Nao Robot. As we can see in Fig.2, the behaviour of the robot is not de-
scribed using a predefined work-flow as it is commonly done in the Choregraphe
environment. All the possible concrete tasks a robot may perform are linked to
a normative reasoning component. Such component defines the behaviour of the
robot according to its goals, thus deciding what tasks to be performed according
to the specific circumstances the robot is working. As we can see, we implemented
not only a set of concrete tasks the robot may use for satisfying its achievement
goals, but also a set of tasks the robot may perform to be compliant with the pre-
vious set of social norms. Moreover, we developed a simple graphical interface to
simulate some events such as met or bumped into someone or some conditions of
the environment such as ”there is a free chair.” Such elements are perceived as be-
liefs by the robot which updates its knowledge about the state of the world. Each
simulated scenario starts under the same conditions: the robot wants to send a
packet and it wants to be social, W 0 ={want(be social),want(send,packet)}. The
expectations of the robot are met by the gratitude and politeness shown by the
human. Each scenario presents three sections: a brief description of the scenario,
the initial behaviour, the robot plan for reaching its achievement goals, and a
description of the dynamic execution of the scenario.
Fig. 2: Choreographe Components
12 Ribino et.al
SCENARIO 1
Description - In this scenario the robot arrives at the office, sees a free chair then it
sits down and waits for its turn, after that it talks to the clerk. Such scenario shows the
most simple situation where there are no applicable norms. Thus, any change occurs in
the normal behaviour of the robot. The robot pursues its triggered achievement goals
by following its initial planned behaviour.
Initial Behaviour
Move To Sit Down Wait Stand Up Dialog
is(myTurn)
Start End
Execution
Task: Move to postal office
Task: Sit Down
Task:Wait
Expected Event: is(MyTurn)
Task: Stand up
Task: Dialog
SCENARIO 2
Description - In such scenario, the robot, moving to the postal office, bumps into a
person. Such event triggers the norms N2 . Thus the robot changes its planned behaviour
by adding the task of apologising. Then, the behaviour continues as in the previous
scenario.
Initial Behaviour
Move To Sit Down Wait Stand Up Dialog
is(myTurn)
Start End
Execution
Task: Move to postal office
Unexpected Event: bumped(person)
Applicable norm: norm(position( ),state(be social), state(sorry),condition(
bumped(person),type(desirable))
Update Behaviour:
Move To Sorry Sit Down Wait Stand Up Dialog
bumped is(myTurn)
Start (person) End
Task: Say Sorry
Unexpected Event: said(person,"Not at all")
Task: Wait
Expected Event: is(MyTurn)
Task: Dialog
SCENARIO 3
Description - In such scenario, the robot arrives at the postal office, sees a free chair
then it sits down. An older adult arrives at the postal office. The robot changes its
plan by following the norm N3 . Thus, it stands up and waits for its turn.
Improving robot sociality: a goal-based normative approach 13
Initial Behaviour
Move To Sit Down Wait Stand Up Dialog
is(myTurn)
Start End
Execution
Task: Move to postal office
Task: Sit Down
Unexpected Event: is(person,old)
Applicable norm: norm(position( ),state(be social), state(standup),condition(
and([is(person,old),state(sitted)])),type(desirable))
Update Behaviour:
is(person,old)
Move To Sit Down Stand Up Wait Dialog
is(myTurn)
Start End
Task: Stand up
Unexpected Event: said(person,"Thanks")
Task: Wait
Expected Event: is(MyTurn)
Task: Dialog
SCENARIO 4
Description - In such scenario, the robot, moving to the postal office, bumps into a
person. Such event triggers the norms N2 . Thus the robot changes its planned behaviour
by adding the task of apologising. Then, it sees a free chair, and it sits down. An older
adult arrives at the postal office. The robot changes its plan again by following the
norm N3 . Thus it stands up and waits for its turn.
Initial Behaviour
Move To Sit Down Wait Stand Up Dialog
is(myTurn)
Start End
Execution
Task: Move to postal office
Unexpected Event: bumped(person)
Applicable norm: norm(position( ),state(be social), state(sorry),condition(
bumped(person),type(desirable))
Update Behaviour:
Move To Sorry Sit Down Wait Stand Up Dialog
bumped is(myTurn)
Start (person) End
Task: Say Sorry
Unexpected Event: said(person,"Not at all")
Task: Sit Down
Unexpected Event: is(person,old)
Applicable norm: norm(position( ),state(be social), state(standup),condition(
and([is(person,old),state(sitting)])),type(desirable))
Update Behaviour:
is(person,old)
Move To Sit Down Stand Up Wait Dialog
is(myTurn)
Start End
Task: Stand up
14 Ribino et.al
Unexpected Event: said(person,"Thanks")
Task: Wait
Expected Event: is(MyTurn)
Task: Dialog
In this section, we presented a simple case study for showing operatively
the proposed approach. In particular, we want to highlight the flexibility of the
approach. As we see, it is not necessary to define all the possible plans the
robot may perform for managing all the possible situations. Indeed we do not
implement if-then rules, but we provide the robot with the ability to reason
about a mental concept such as norm and expectations. Thus, it can manage
unexpected events that the robot does not consider in its initial plan.
5 Conclusions
Social robots interact with humans for performing specific tasks. Implementing
social capabilities, such as behave following the social norms prescribed by the
community, improves the social desirability of the robot. In this work, we propose
a normative approach that allows exploiting the advantages of goal modelling
to make social robots able to reason about dynamic situations pro-actively. In
particular, we defined social norms by introducing desirability operators for rep-
resenting preferences about acceptable behaviours and the expectations as a new
mental concept a robot sees as a motivator for pursuing social values that we
model using quality goals. Moreover, we have illustrated some scenarios about
how the robot behaves in some situations that involve social norms, showing the
flexibility of the approach to managing unexpected events.
As next step, we are working on allowing the robot to perform more complex
evaluation about the expectations and how the robot may change its behaviour
adaptively according to its expectations.
References
1. Jean-Léon Beauvois. Judgment norms, social utility, and individualism. A so-
ciocognitive approach to social norms, pages 123–147, 2003.
2. Nicole Dubois and Jean-Léon Beauvois. Normativeness and individualism. Euro-
pean Journal of Social Psychology, 35(1):123–146, 2005.
3. N Dubois. Theory of the social value of persons applied to organizations: Ty-
pologies of good leaders and recruitment. Revue Européenne de Psychologie Ap-
pliquée/European Review of Applied Psychology, 60(4):255–266, 2010.
4. Nicolas Sommet, Alain Quiamzade, and Fabrizio Butera. How would pyrrho have
been socially valued? social desirability and social utility of conflict regulation.
International Review of Social Psychology, 30(1), 2017.
5. Terrence Fong, Illah Nourbakhsh, and Kerstin Dautenhahn. A survey of socially
interactive robots. Robotics and autonomous systems, 42(3-4):143–166, 2003.
6. John R Searle. The construction of social reality. Simon and Schuster, 1995.
7. David Lewis. Convention: A philosophical study. John Wiley & Sons, 2008.
Improving robot sociality: a goal-based normative approach 15
8. Cristina Bicchieri and Ryan Muldoon. Social norms. In Edward N. Zalta, editor,
The Stanford Encyclopedia of Philosophy. Metaphysics Research Lab, Stanford
University, spring 2014 edition, 2014.
9. Bertram F Malle, Matthias Scheutz, and Joseph L Austerweil. Networks of social
and moral norms in human and robot agents. In A world with robots, pages 3–17.
Springer, 2017.
10. Patrizia Ribino, Carmelo Lodato, and Massimo Cossentino. Modeling business
rules compliance for goal-oriented business processes. In Workshop on Enterprise
and Organizational Modeling and Simulation, pages 83–99. Springer, 2017.
11. Aldebaran Robotics. Nao robot, 2015.
12. Fabio Maria Carlucci, Lorenzo Nardi, Luca Iocchi, and Daniele Nardi. Explicit
representation of social norms for social robots. In Intelligent Robots and Systems
(IROS), 2015 IEEE/RSJ International Conference on, pages 4191–4196. IEEE,
2015.
13. What Social Robots Can, Should Do J Seibt, et al. Making place for social norms
in the design of human-robot interaction. What Social Robots Can and Should Do:
Proceedings of Robophilosophy 2016/TRANSOR 2016, 290:303, 2016.
14. Ladislau Bölöni, Taranjeet Singh Bhatia, Saad Ahmad Khan, Jonathan Streater,
and Stephen M Fiore. Towards a computational model of social norms. arXiv
preprint arXiv:1801.05796, 2018.
15. Mehdi Dastani, M Birna Van Riemsdijk, and Michael Winikoff. Rich goal types in
agent programming. In The 10th International Conference on Autonomous Agents
and Multiagent Systems-Volume 1, pages 405–412. International Foundation for
Autonomous Agents and Multiagent Systems, 2011.
16. Geoffrey Brennan, Lina Eriksson, Robert E Goodin, and Nicholas Southwood.
Explaining norms. Oxford University Press, 2013.
17. Carole Adam, Benoit Gaudou, Andreas Herzig, and Dominique Longin. Occs emo-
tions: a formalization in a bdi logic. In International Conference on Artificial
Intelligence: Methodology, Systems, and Applications, pages 24–32. Springer, 2006.
18. Anand S Rao. Agentspeak (l): Bdi agents speak out in a logical computable lan-
guage. In European Workshop on Modelling Autonomous Agents in a Multi-Agent
World, pages 42–55. Springer, 1996.
19. Rafael H Bordini, Jomi Fred Hübner, and Michael Wooldridge. Programming multi-
agent systems in AgentSpeak using Jason, volume 8. John Wiley & Sons, 2007.
20. Mirko Morandini, Fabiano Dalpiaz, Cu Duy Nguyen, and Alberto Siena. The
tropos software engineering methodology. In Handbook on Agent-Oriented Design
Processes, pages 463–490. Springer, 2014.