# A Survey on Dialogue Management in Human-Robot Interaction

Merle M. Reimann<EMAIL_ADDRESS>Vrije Universiteit AmsterdamDe Boelelaan
1111AmsterdamNorth HollandNetherlands1081 HV 0000-0003-3076-5402 ,  Florian A.
Kunneman  Vrije Universiteit AmsterdamDe Boelelaan 1111AmsterdamNorth
HollandNetherlands1081 HV 0000-0002-1932-3200 ,  Catharine Oertel  Delft
University of TechnologyP.O. Box 5031DelftSouth HollandNetherlands2600 GA
0000-0002-8273-0132 and  Koen V. Hindriks  Vrije Universiteit AmsterdamDe
Boelelaan 1111AmsterdamNorth HollandNetherlands1081 HV 0000-0002-5707-5236

###### Abstract.

As social robots see increasing deployment within the general public,
improving the interaction with those robots is essential. Spoken language
offers an intuitive interface for the human-robot interaction (HRI), with
dialogue management (DM) being a key component in those interactive systems.
Yet, to overcome current challenges and manage smooth, informative and
engaging interaction a more structural approach to combining HRI and DM is
needed. In this systematic review, we analyse the current use of DM in HRI and
focus on the type of dialogue manager used, its capabilities, evaluation
methods and the challenges specific to DM in HRI. We identify the challenges
and current scientific frontier related to the DM approach, interaction
domain, robot appearance, physical situatedness and multimodality.

spoken interaction, dialogue management, social robots

††ccs: Computing methodologies Discourse, dialogue and pragmatics††ccs:
Computer systems organization Robotics

##  1\. Introduction

For humans, spoken communication is a natural way of interacting with each
other, their smart speakers and even their pets. Social robots are robots that
are designed specifically to interact with their human users (Breazeal et al.,
2016) for example by using spoken dialogue. For social robots, the interaction
with humans plays a crucial role (Fong et al., 2002; Baraka et al., 2020), for
example in the context of elderly care (Broekens et al., 2009) or education
(Belpaeme et al., 2018).

Figure 1. The integration of the dialogue manager into a spoken dialogue
system.

The agent’s natural language understanding module processes the user’s
(spoken) input and forwards it to the dialogue manager, followed by a natural
language generation module, to create the agent’s output.

Robots that use speech as a main mode of interaction do not only need to
understand the user’s utterances, but also need to select appropriate
responses given the context. Dialogue management (DM), according to Traum and
Larsson (Traum and Larsson, 2003), is the part of a dialogue system that
performs four key functions: 1) it maintains and updates the context of the
dialogue, 2) it includes the context of the utterance for interpretation of
input, 3) it selects the timing and content of the next utterance, and 4) it
coordinates with (non-)dialogue modules. In spoken dialogue systems, the
dialogue manager receives its input from a natural language understanding
(NLU) module and forwards its results to a natural language generation (NLG)
module, which then generates the output (see fig. 1).

Figure 2. Dialogue management in human-robot interaction is influenced by
multiple factors that show high variability. Different robot appearances can
be combined with varying modalities, interaction domains and environments.
Some examples are given for each factor to illustrate it.

The concentric circles illustrate that dialogue management is influenced by
the interaction domain, environment, robot appearance and the modalities.

In contrast to general DM, DM in human-robot interaction (HRI) has to also
consider and manage the complexity added by social robots (see fig. 2). The
concentric circles of the figure describe decisions that have to be made when
designing a dialogue manager for human-robot interaction. From each circle,
one or more options can be chosen and combined with each other. The robot
appearance, the modalities of the interaction, interaction scenarios and the
physical environment influence the DM. Their combination leads to high
variability, but also great complexity. While pure neural networks are used
for dialogue management in non-HRI contexts (Brabra et al., 2021; Zhao et al.,
2019), this approach is not adopted generally for HRI, where sparse data, need
for robustness and control of high-stakes interactions pose additional
constraints.

While DM and HRI have not been studied extensively in combination, it is not
the case for the fields individually. Harms et al. (Harms et al., 2018) Brabra
et al. (Brabra et al., 2021), Deriu et al. (Deriu et al., 2021), Zhao et al.
(Zhao et al., 2019) and Trung (Trung, 2006) investigate DM, but not from a
robotics perspective, and Skantze (Skantze, 2021) looks at turn-taking, a part
of DM. While there are reviews on DM in HRI, these have a specific user-group
focus, such as patients suffering from dementia (Russo et al., 2019). An
overview of the present and future of natural language in HRI, without a
specific focus on DM, is provided by (Liu and Zhang, 2019), (Mavridis, 2015)
and (Marge et al., 2022). In contrast to those reviews, we focus on dialogue
managers which are used in physical robots and provide a general overview of
DM in HRI. With this review, we aim at giving HRI researchers an overview of
the currently used dialogue management systems and their capabilities, to help
them make a more informed decision when choosing a dialogue manager for their
system.

##  2\. Review

We conducted a systematic review using the PRISMA protocol (Moher et al.,
2015). We determined inclusion and exclusion criteria, which guided the
selection steps, based on our research questions: RQ1: Which robots are
commonly used for DM? (Section 2.1)
RQ2: Which types of dialogue managers are commonly used in HRI, and what are
the reasons? (Section 2.2)
RQ3: Which capabilities do current dialogue managers used in HRI have?
(Section 2.3)
RQ4: How are dialogue managers in HRI evaluated? (Section 2.4)
RQ5: What are the main challenges for DM in HRI? (Section 3)

We used Scopus, IEEE and ACM for the literature search using the search terms
“Robot AND ’mixed initiative’” and “Robot AND (’dialog manager’ OR ’dialogue
manager’)” resulting in 949 papers (ACM: $556$, Scopus: $278$, IEEE: $115$).
Performing a second search with the more general search terms “Robot AND
(dialog OR dialogue)”, but a restriction to relevant conferences (HRI, IUI,
ICMI, AAMAS, SIGGRAPH, HAI, SIGDIAL, RO-MAN, IROS, INTERSPEECH, DIS, MHAI,
AAAI) led to another 504 papers (ACM: $62$, Scopus: $350$, IEEE: $92$). To
make sure that also papers that use the term ’interaction’ instead of
’dialogue’ in abstract and title are included, we performed a third search
with the search term “’social interaction’ AND robot AND speech”. This led to
209 (ACM: $20$, Scopus: $134$, IEEE: $55$) papers. All papers were imported
into Rayyan (Ouzzani et al., 2016) for further processing.

The formal exclusion criteria were chosen to filter out papers shorter than
$4$ pages, reviews, demonstrator papers, results which are not scientific
papers or did not include a physical robot. Furthermore, we excluded papers
written before $2005$ since the NLU modules and DM capabilities have improved
a lot since then. Additionally, the papers needed a focus on spoken DM or DM
capabilities of the dialogue manager. DM capabilities are the conversational
abilities the dialogue manager possesses. We decided to scope the review to
only include papers that describe a whole dialogue manager. While other
components like dialogue state trackers do help the dialogue manager, by
providing information that the dialogue manager can then use to decide what
its next action should be, they are out of scope for this survey. After
duplicate deletion and keyword-based filtering (“dialog”, “dialogue”,
“dialogs”, “dialogues”, “conversation”, “conversational”, “conversations” and
“discourse”), all authors independently performed a manual abstract screening
of the remaining $753$ papers, excluding papers that do not sufficiently focus
on spoken human-robot interaction. Conflicting decisions were discussed until
an agreement was reached. A subsequent paper screening led to a total of $68$
papers analysed for this review.

###  2.1. Robot appearance

We classify the $69$ robots found in the $68$ papers by their appearance using
the taxonomy in (Baraka et al., 2020). Figure 3 shows the resulting
distribution of types of robots found. Humanoid robots ($41\%$) are one of the
more dominant categories in spoken human-robot interaction. Within this
category the NAO robot, used in $10$ papers, is the most commonly used
humanoid robot (Chai et al., 2014; Cuayáhuitl et al., 2014; Lison, 2015, 2012;
Bohus and Horvitz, 2014; Lison, 2013; Gervits and Scheutz, 2018; Devillers et
al., 2015; Ondas et al., 2017; Aly and Tapus, 2013). The Pepper robot (Ion et
al., 2020; Zeng et al., 2018; Nuccio et al., 2018) and Maggie (Alonso-Martín
et al., 2013, 2015; Gorostiza and Salichs, 2010) are used in three papers
each. A robot bartender (Petrick et al., 2012; Foster et al., 2012a), Armar 3
(Prommer et al., 2006; Holzapfel, 2008) and the PR2 robot (Gervits et al.,
2020; Williams et al., 2015) are mentioned twice while all other humanoid
robot appearances occur just once (Dino et al., 2019; Torrey et al., 2006; Lee
et al., 2010; Jiang et al., 2011; Matsuyama et al., 2010; Chao and Thomaz,
2016; Li et al., 2006a). The second biggest category consists of functional
robot appearances ($30\%$), the design of those robots is influenced by the
task they are used for. The three Segway-based robots in this category are
used for delivery and navigation (Amiri et al., 2019; Thomason et al., 2015;
Zhang and Stone, 2015). Mobility plays a role for all robots in this category
as they are used to help with information and action requests based on
instructions (Quinderé et al., 2007a, b, 2013; Edirisinghe et al., 2018; Chen
et al., 2010; Rybski et al., 2007; Lee et al., 2008; Lucignano et al., 2013;
Aguilar and Pineda, 2009; Avilés et al., 2010), act as a guide (Vogiatzis and
Karkaletsis, 2008; Li et al., 2006b; Rosenthal and Veloso, 2010; Gervits et
al., 2021; Li et al., 2006a; Lu et al., 2017) or learn location names (Nakano
et al., 2010; Funakoshi et al., 2007).

Figure 3. Appearance of the robots used in the surveyed papers.

The plot shows the distribution of appearances in the 68 surveyed papers. 29
robots are humanoid, 21 have a functional appearance, 10 are body parts, 4 are
artifact shaped, 4 are android robots and one was not described in sufficient
detail to classify it.

For the robots based on body parts, two robotic heads were used: Flobi
(Carlmeyer et al., 2014; Kipp and Kummert, 2014) and Furhat (Campos et al.,
2018; Skantze et al., 2013; Johansson and Skantze, 2015). In contrast to
robotic heads, that do not have manipulators, robotic arms are used for object
manipulation and grasping. We found robotic arms in five of the papers
(Peltason and Wrede, 2010, 2012; Shen and Inkpen, 2016; She et al., 2014;
Sugiura et al., 2010). Robots based on other body parts were not found in any
of the papers. All of the artifact shaped robots found in the papers are
robotic wheelchairs (Doshi and Roy, 2007; Hemachandra et al., 2014;
Hemachandra and Walter, 2015; Vale and Mast, 2013), which are used for
navigation. The papers using android robots, all use ERICA as the robot (Lala
et al., 2017, 2018; Milhorat et al., 2019; Inoue et al., 2020). One of the
robots was not described sufficiently to classify it (Gieselmann and
Ostendorf, 2007).

Based on the diversity of robot types used, we can conclude that the use of
spoken dialogue systems is not restricted to specific robots with certain
shapes or features, which adds to the high variability of factors influencing
the DM (see fig. 3).

###  2.2. Types of DM in HRI

While the first dialogue management approaches starting in the 60s were purely
handcrafted, there has been a development into the direction of probabilistic
and hybrid approaches. We use the framework provided by Harms et al. (Harms et
al., 2018) as a basis for the classification of dialogue managers into
handcrafted, probabilistic and hybrid approaches. In total, $45$ of the $68$
papers use handcrafted approaches, $12$ use probabilistic ones and $11$ make
use of a hybrid approach (see fig. 4). From 2006 to 2010, $17$ of the $23$
included dialogue managers were handcrafted, with model-based approaches
making up over $50\%$ in total. Model-based approaches stayed popular from
2011 to 2015, but (partially observable) Markov decision processes ((PO)MDPs)
saw an increased use as well. From 2016 onward, those two approaches were
surpassed by hybrid ones that can combine the advantages of both.

Figure 4. The dialogue management approaches over the years used in the
surveyed papers.

From 2006-2010 23 papers were selected, from 2011-2015 27 and from 2016-2021
18. In the first two intervals model-based DM was the most common one, in the
third interval hybrid methods had the greatest popularity.

####  2.2.1. Handcrafted approaches

The main difference between the different handcrafted approaches lies in the
information encoded in the states of the dialogue manager. Finite-state
machines (FSMs) are a simple approach to handcrafted DM, where the states of
the FSM directly correspond to the dialogue states. A simple FSM approach is
used in (Rybski et al., 2007), where the focus of the interaction lies on the
learning of tasks through dialogue. (Lee et al., 2008) use a composition of
multiple FSMs to manage the different interactions their robot is offering.
However, nowadays they are mostly used as building blocks in combination with
other additional functionalities in the surveyed papers. For example,
(Edirisinghe et al., 2018) uses an FSM with predefined dialogues for knowledge
acquisition, but aims at adding an autobiographical memory to the robot.
Dialogue managers based on state charts, which are used to implement
interaction patterns, are used in (Carlmeyer et al., 2014; Kipp and Kummert,
2014; Peltason and Wrede, 2010, 2012). Interaction patterns are based on
general sequences that can be found during dialogues and are defined before
the interaction. Another dialogue manager based on state charts is IrisTK,
which adds recursion (Skantze et al., 2013; Johansson and Skantze, 2015).
Building upon IrisTK, (Johansson and Skantze, 2015) add a data-driven turn-
taking approach to the system.

To allow the human user to take more initiative and provide information more
flexibly in dialogues, frame-based systems have been used (Bobrow et al.,
1977). Frame-based approaches employ empty slots, typically information units
required to fulfill the users request, that can be filled at any point of the
conversation. (Gorostiza and Salichs, 2010) present such a system, where
multiple slots can be filled at a time, for action requests for the robot
Maggie. A frame-based approach is also adopted in (Zeng et al., 2018), where
the user’s food preferences are determined in an interview-like conversation
by using slot-filling.

The third handcrafted approach is the model-based approach. In addition to the
frames with slots, a model-based dialogue manager has more complex states.
Those states include some form of model, for example about the user, the
environment, the situation or the context. Model-based dialogue managers often
consider the user model, including the user’s goals and intention (Chai et
al., 2014; Williams et al., 2015; Thomason et al., 2015; Quinderé et al.,
2007a, 2013; Vogiatzis and Karkaletsis, 2008; Rosenthal and Veloso, 2010).
User models that influence the dialogue management can also be based on
displayed emotion (Alonso-Martín et al., 2013; Devillers et al., 2015) or
personality (Aly and Tapus, 2013) of the user. For long-term interaction,
including a model of the conversation history is especially relevant and
enables the dialogue manager to learn from past interactions. Cobot’s dialogue
manager described in (Rosenthal and Veloso, 2010) is designed for a long-term
interaction, and therefore uses the conversation history to avoid repetitive
dialogue or unnecessary offers. In (Dino et al., 2019), the robot collects and
stores user data that influences later conversations with the user. (Li et
al., 2006b, a) use a stack to keep track of unfinished conversation sequences
for tracking the context during the interaction. The dialogue manager used in
(Nuccio et al., 2018) groups utterances into contexts, which then can be
focused during different points of the conversation.

The Information State Update approach (Traum and Larsson, 2003) used by
(Quinderé et al., 2007a, b, 2013), stores and uses amongst other things the
dialogue context, information about the possible referents of pronouns, and
information about the current turn and initiative. (She et al., 2014; Aguilar
and Pineda, 2009; Avilés et al., 2010) also use the dialogue context and
user’s intention, in addition to objects referenced in the environment, to
determine the next dialogue move. Multiple expert modules, activated based on
domain scores, are used by (Nakano et al., 2010) and (Funakoshi et al., 2007).

Plan-based systems, which belong to the model-based approaches, treat the
conversation as a planning problem and aim at reaching a goal state by
selecting from predefined actions. Plan-based dialogue management is used by
(Petrick et al., 2012), (Alonso-Martín et al., 2015) and (Foster et al.,
2012a), who use the planner for the dialogue and action generation. Other
plan-based systems are used by (Vale and Mast, 2013) for navigation of a
robotic wheelchair and by (Gervits and Scheutz, 2018), with additional rule-
based speech overlap management. (Chen et al., 2010) uses answer set
programming for their planning module, which can generate a high-level plan
based on the user’s request and a low-level plan for the motion planner. In
(Campos et al., 2018), a goal, based on novelty, dialogue history, relevance
and diversity, is used for the utterance selection, while (Gervits et al.,
2021) performs logical inference over the goals and beliefs to this end.

The dialogue manager used by (Jiang et al., 2011) can be used as a model-based
one, but a simplified version can also be used as a frame-based or FSM
dialogue manager. Two dialogue managers do not fit the classification and were
therefore classified as other handcrafted. (Chao and Thomaz, 2016) used timed
petri nets to improve mutltimodal interaction, especially in respect to turn-
taking. Petri nets are state-transition networks that have tokens, which
determine the current configuration state and whether a transition is enabled.
In addition to utilizing this approach, timed Petri nets use real time
transitions of the tokens to be able to model the configuration according to
the input. (Ondas et al., 2017) use a dialogue manager that uses XML files for
generating the dialogue in Slovac languages.

####  2.2.2. Probabilistic approaches

In contrast to handcrafted approaches, probabilistic approaches learn the
dialogue policy directly from data. A probabilistic approach used in only one
surveyed paper is the example-based approach (Torrey et al., 2006) that looks
up the most similar utterance in its database, consisting of numerous
conversations, and uses the answer given there as its own.

A popular probabilistic approach used in DM are (partially observable) Markov
decision processes ((PO)MDPs). MDPs model the dialogue as a Markov process,
moving from one state to another based on learned transition probabilities.
For POMDPs, the current state is not known and is estimated based on
observations (e.g. perceived actions and utterances) and their probabilities.
The robotic wheelchairs used in (Hemachandra et al., 2014) and (Hemachandra
and Walter, 2015) use dialogue to check their spatial semantic representation
and make sure that it corresponds to the real environment, by asking questions
about the environment. The POMDP dialogue manager in (Amiri et al., 2019)
rewards successful conversations, leading to task completion, while additional
questions, confirmations and unsuccessful conversations are penalised. Two
POMDPs, one for the DM and another one for deciding when to update the
knowledge base, are used for improving the augmentation of the knowledge on an
as-needed basis. (Cuayáhuitl et al., 2014) allow flexible navigation between
the different sub-dialogues, to enable the user to change their mind about the
order in which they want to do certain tasks. A Bayesian approach to learning
the best dialogue strategy is used by (Doshi and Roy, 2007) for a robotic
wheelchair. Integrating reasoning with probabilistic common sense knowledge
enables the robot in (Zhang and Stone, 2015) and (Lu et al., 2017) to ignore
irrelevant variables while planning. Another approach for limiting the state
space is used in (Lison, 2012), where probabilistic rules are used for
Bayesian networks. (PO)MDPs are also used for multimodal dialogue, where they
learn the strategies from multimodal instead of pure dialogue data (Prommer et
al., 2006; Holzapfel, 2008; Lucignano et al., 2013).

We did not find a system purely based on an end-to-end learning approach, but
end-to-end approaches are in fact used as parts of hybrid systems (see section
2.2, Hybrid approaches). A probabilistic approach which does not fit into
either category described earlier is used by (Sugiura et al., 2010). They
generate all possible linguistic expressions for an action, which is possible
since they use a simplified sentence structure. Then they use expected log
loss reduction and Bayesian logistic regression, predicting which utterance
should be selected. Using the best score, an utterance is selected and
produced. After the production, the appropriateness of the utterance is rated
based on the user’s reaction and the example is added to the training set.
This leads to active learning during the conversation.

####  2.2.3. Hybrid approaches

Instead of focusing on either a handcrafted or data-driven approach, in recent
years it has become more popular to use a combination of the two. These so-
called hybrid approaches can be fitted according to the needs of the target
application and the availability of data. The option to combine approaches can
compensate for their weaknesses and utilise their strengths. Hybrid approaches
are often used when the dialogue manager is distributed in a variety of
submodules which focus on different aspects of the dialogue management, like
turn-taking, engagement management or problem handling. (Lala et al., 2018)
combine a finite-state turn-taking machine with a deep neural network, in
order to improve the dialogue manager’s turn-taking capabilities. Turn-taking
is also integrated into the otherwise model-based dialogue manager in (Gervits
et al., 2020) by adding a combination of incremental processing and lexical
prediction. In (Bohus and Horvitz, 2014) an FSM approach is combined with
self-supervised learning to handle disengagement. To handle problems occurring
during the dialogue more efficiently, (Gieselmann and Ostendorf, 2007) add an
FSM with four states (Start, Normal, Help, Error) to the dialogue manager
ARIADNE (Holzapfel, 2005). (Shen and Inkpen, 2016) also use an FSM for
tracking the general state of the dialogue system, but make use of external
services for questions and declarative utterances.

(Lala et al., 2017) use logistic regression to select so-called experts, which
can have different strategies, while (Inoue et al., 2020) rely on a priority
system in combination with additional triggers for a backchannel and backup
question module. Backchannels are short responses that are integrated into the
conversation while listening, to show engagement (e.g. “hmm”, “yeah”). In
(Milhorat et al., 2019) a dialogue act tagger based on supervised machine
learning (Support Vector Machines) is used to decide whether a statement
response should be generated, based on either a decision tree or a response
using an example-based approach. If neither have a sufficiently high
confidence score, a backchannel is produced instead.

It is also possible to make use of probabilistic rules for DM to reduce the
parameter space by including expert knowledge (Lison, 2015). A combination of
a POMDP and a model-based approach is used in (Lison, 2013), where the user’s
goal and actions are modelled using Bayesian inference. A combination of an
example-based and agenda-based approach is used in (Lee et al., 2010). While
the agenda graph is designed manually, the examples which are mapped to the
nodes are learned from human-human dialogues.

###  2.3. Communication capabilities in HRI

We list common capabilities that were explicitly mentioned in the reviewed
papers in table 1.

##### Confirm

Explicit confirmations by the robot for establishing common ground are
important, especially for task-based interaction, to increase task success
(Thomason et al., 2015). Common ground describes shared beliefs, for example
about the environment or the state of the conversation (Stalnaker, 2002),
which is relevant, if the human and robot have to achieve a task together.
Example use-case scenarios include movement instructions (Gorostiza and
Salichs, 2010; Gervits et al., 2021), receptionist tasks (Jiang et al., 2011;
Quinderé et al., 2013; Holzapfel, 2008), grasping (Peltason and Wrede, 2012),
delivering items (Thomason et al., 2015) or the establishment of common ground
in situated interaction (Chai et al., 2014).

Table 1. Common conversational capabilities observed in the 68 surveyed papers. Conversational Capability | Amount
---|---
Confirm | 13
Ask for confirmation | 24
Ask for repetition/rephrasing | 10
Ask clarification questions | 17
Reference resolution/Spatial grounding | 24
Acquire knowledge | 21

##### Ask for confirmation

A clear trend across more than $\frac{1}{3}$rd of all papers is the robot’s
ability to ask for confirmation. This can happen either explicitly (e.g. “Do
you want…?”, with an option for the user to (dis-)confirm) or implicitly (e.g.
“I will now…”, with an option to cancel the action if it is not the desired
one). (Peltason and Wrede, 2010, 2012) use a correctable information request
pattern for implicit confirmations, and an explicit confirmation after an
Information Request pattern, for integrating confirmation questions into their
task-based interaction. Confirmations by the human can be used to ensure
common ground about the environment (Chai et al., 2014; Skantze et al., 2013;
Holzapfel, 2008), to elicit information about user preferences (Zeng et al.,
2018; Lu et al., 2017), to confirm the correctness of past actions (Lison,
2012) or to make sure that the conversation is not terminated too early
(Rybski et al., 2007). At the same time, unnecessary confirmation or
clarification questions can make the conversation inefficient and increase
user frustration and are therefore often associated with a cost in
reinforcement learning settings (Amiri et al., 2019; Lison, 2012; Hemachandra
and Walter, 2015; Zhang and Stone, 2015; Doshi and Roy, 2007). For example,
robots which navigate or deliver items (Lison, 2015; Amiri et al., 2019; Zhang
and Stone, 2015; Doshi and Roy, 2007), or make appointments and book something
(Jiang et al., 2011; Quinderé et al., 2013) often ask for confirmation, since
a later correction after performing the action is more costly. To avoid
unnecessary confirmation questions, (Lison, 2013) assigns a negative value to
the reward function of their reinforcement learning dialogue strategy for
additional confirmations, while the robot in (Sugiura et al., 2010) learns
when and how to confirm through active learning. Since robots have to deal
with challenging environments with noise, confirmation questions are used,
when the confidence that the utterance was understood correctly is low
(Williams et al., 2015; Gorostiza and Salichs, 2010; Quinderé et al., 2007b,
2013; Nakano et al., 2010).

##### Ask for repetition/rephrasing

In case of speech recognition problems, a repetition of the utterance can
already help resolving them, while rephrasing gives the human the option to
produce a different utterance which is easier to understand for the robot than
the first one. To determine whether repetition is needed, different strategies
are available. Robots ask users to repeat an utterance when the recognition is
below a threshold (Gorostiza and Salichs, 2010), no match for the input was
found (Gervits et al., 2021; Carlmeyer et al., 2014) or in case of
underspecification (Lison, 2015, 2013). If no grounding is achieved for object
names that are referred to by a gesture accompanying a pronoun, the robot in
(Li et al., 2006b) asks for repetition. Like repetition, rephrasing can be
used when the dialogue manager finds no match for the input (Ion et al., 2020;
Lee et al., 2010; Gervits et al., 2021), if the dialogue manager is uncertain
either about the dialogue state (Amiri et al., 2019) or about the correct
recognition of the utterance (Gorostiza and Salichs, 2010).

##### Ask clarification questions

Clarification questions are asked in those cases where the robot is unsure if
it has understood the utterance correctly (Amiri et al., 2019; Thomason et
al., 2015; Quinderé et al., 2007a, b; Chao and Thomaz, 2016) or when it is
missing information (Zeng et al., 2018; Gorostiza and Salichs, 2010; Gervits
et al., 2021; Gieselmann and Ostendorf, 2007; Holzapfel, 2008). Additional
information to make complete plans (Chen et al., 2010) or determine the
current context (Williams et al., 2015) can be acquired through clarification
questions. If most parts of a user request are already specified and only some
need additional clarification, (Carlmeyer et al., 2014) suggest to let the
robot already start the action while clarifying the missing parts.

##### Reference resolution/spatial grounding

Something which distinguishes robots from conversational agents is that they
are physically embodied and situated in an environment. That is why
situational grounding is important for robots that, for example, engage in
navigation (Lison, 2015; Rosenthal and Veloso, 2010; Hemachandra et al., 2014;
Hemachandra and Walter, 2015), deliveries (Thomason et al., 2015) or give a
tour in the environment (Rybski et al., 2007; Li et al., 2006b; Peltason and
Wrede, 2010). Situational grounding refers to the robot’s ability to ground
concepts and referenced objects in the real world (”Give me the red cup” would
refer to a specific red cup that is present in the environment, not to the
concept of a red cup in general). The robot in (Chai et al., 2014) engages in
a naming game of objects in the environment with the user and focuses on
establishing common ground. To do so, it makes its knowledge explicit, such
that the human can correct it if necessary. Robotic arms that grasp and move
objects, need to identify the object the user is talking about correctly
(Peltason and Wrede, 2012; She et al., 2014; Sugiura et al., 2010). For
multimodal dialogue where the human refers to objects in the environment that
are related to the robot’s task, the robot also needs to be aware which object
the human is referencing using the available modalities (e.g. gestures,
speech, gaze) (Holzapfel, 2008; Prommer et al., 2006; Aguilar and Pineda,
2009; Avilés et al., 2010).

##### Acquire knowledge

If the human is talking to the robot about previously unseen or unheard
objects or people, the robot should be able to make sense of them. Knowledge
augmentation allows for new knowledge to be added to the knowledge base of the
robot. For navigating robots, questions about their environment improve their
knowledge about the spatial semantic representation (Hemachandra et al., 2014;
Hemachandra and Walter, 2015). In the delivery domain, knowledge augmentation
is used to learn names of objects or people which have to be delivered/should
receive the delivery but are not yet in the knowledge base (Amiri et al.,
2019; Thomason et al., 2015). Another context in which knowledge from previous
interactions and users is used, is for recommendations based on (assumed) user
preferences (Vogiatzis and Karkaletsis, 2008; Lu et al., 2017). The robot in
(Quinderé et al., 2013) is able to acquire and store knowledge, which is
explicitly declared. In some cases the interaction with the robot is used to
teach it new actions (She et al., 2014), grasping methods in combination with
object names (Peltason and Wrede, 2012), or following instructive tasks
(Rybski et al., 2007). Other forms of knowledge acquisition include active
learning from past interactions (Sugiura et al., 2010), the internet (Nuccio
et al., 2018) or updates of the world model (Chen et al., 2010) in order to
avoid failures of the interaction. Another case of knowledge acquisition takes
place when the robot learns personal information about the human and stores it
in a knowledge base (Edirisinghe et al., 2018; Alonso-Martín et al., 2013).

###  2.4. Evaluation methods of DM in HRI

Different techniques have been used for evaluating the performance of dialogue
managers in general. A detailed survey of them can be found in (Deriu et al.,
2021). For the evaluation of dialogue managers in HRI, subjective and
objective measures can be used (see table 2). Performing user studies for the
evaluation has the advantage that they allow to evaluate the performance of
the dialogue manager and robot in a real interaction, while allowing the users
to give their opinions in questionnaires, like the Godspeed questionnaire
(Bartneck et al., 2009). That way, user frustration and other subjective
measurements can be included in the evaluation. However, user studies are
resource- and time-intensive. In the reviewed papers, 35 user studies were
reported, with a few (3) of those being in-the-wild studies (Campos et al.,
2018; Bohus and Horvitz, 2014; Johansson and Skantze, 2015). Other evaluation
methods include simulations (7), evaluation based on a corpus of previously
gathered data (10), while some papers just describe possible interaction
scenarios (17). The participant numbers reported for the user studies differ
greatly, ranging from 2 to 97 participants, with an average of 21 participants
(SD 18.4). Three studies (Gorostiza and Salichs, 2010; Sugiura et al., 2010;
Lu et al., 2017) mention tests with users, but do not report any specifics
about the number of participants. For in-the-wild studies it is more difficult
to report exact participant numbers, since it is not always clear if every
user interacted only once, which is why (Campos et al., 2018; Bohus and
Horvitz, 2014; Johansson and Skantze, 2015) report the total number of
recorded interactions instead.

For the evaluation of DM in HRI, task success is a common metric, since the
dialogue and task are often tightly coupled in task-based systems and the
dialogue is needed to achieve the goal. Since 58 of the 68 papers report on
task-based interactions, task success offers an easily accessible option to
evaluate the interaction. Due to the integration of the dialogue manager into
a bigger system, the dialogue manager is often evaluated in combination with
the other modules of the robot. For example, the task success can include the
dialogue, but also the motor functions, to achieve the task. While the
integration of dialogue managers into a robot opens up the possibility of
using evaluation methods that asses the whole system, it complicates the
evaluation. An example is that the dialogue manager gets the information from
the speech recognition module, which can introduce misunderstandings that the
dialogue manager then has to deal with. To avoid those problems, simulations
or corpus-based evaluations can be used. An evaluation of the dialogue manager
alone does not necessarily reflect how it would work in combination with the
robot’s other parts, especially if the robot makes use of multimodal data.

Number of turns is a metric that can be used system-independent and would aid
in comparability of different systems and tasks if reported for all studies.
For task-based studies we observed a high number of papers reporting the task-
success rate; a metric also observed as a common evaluation method by (Deriu
et al., 2021). However, the evaluation of the system is still depending on the
type of system used (Deriu et al., 2021). Even though, this can partly account
for the high number of different evaluation metrics, it would be advantageous
if common metrics, like the number of turns, used would be integrated into the
evaluation of all studies, to make them more comparable.

Table 2. Subjective and objective measures used for evaluating DM in HRI.
Subjective measures
---
User satisfaction (Campos et al., 2018; Thomason et al., 2015; Devillers et
al., 2015)
User frustration (Amiri et al., 2019; Thomason et al., 2015)
Perceived interaction pace (Cuayáhuitl et al., 2014)
Perceived understanding (Lison, 2015; Thomason et al., 2015)
Scales from social psychology/communication (Torrey et al., 2006)
Naturalness and pleasantness (Lison, 2015; Lucignano et al., 2013)
Ease of use (Cuayáhuitl et al., 2014; Thomason et al., 2015)
Likability of the robot (Gieselmann and Ostendorf, 2007; Torrey et al., 2006;
Dino et al., 2019; Lala et al., 2017; Li et al., 2006b)
(Modified version of) the Godspeed questionnaire (Milhorat et al., 2019;
Foster et al., 2012a)
Items from the Interpersonal Communication Satisfactory Inventory (Campos et
al., 2018)
Perceived common ground (Chai et al., 2014)
Likelihood of usage in the future (Amiri et al., 2019; Cuayáhuitl et al.,
2014; Thomason et al., 2015)
Appropriateness of the answer (Lison, 2015; Gervits et al., 2021)
Objective measures
Number of turns (Chai et al., 2014; Lison, 2015; Thomason et al., 2015; Zhang
and Stone, 2015; Gieselmann and Ostendorf, 2007; Lison, 2013; Foster et al.,
2012a; Lee et al., 2010; Holzapfel, 2008; Prommer et al., 2006)
Number of repair turns (Lison, 2015; Li et al., 2006a)
Number of fallback utterances (Milhorat et al., 2019)
Number of rejected utterances (Quinderé et al., 2013)
Precision and recall (Lala et al., 2018; Milhorat et al., 2019)
Accuracy (Amiri et al., 2019; Lison, 2012; Hemachandra and Walter, 2015; Zhang
and Stone, 2015; Shen and Inkpen, 2016; Lala et al., 2018)
F1 scores (Amiri et al., 2019; Lala et al., 2018)
Task success rate (Chai et al., 2014; Amiri et al., 2019; Lee et al., 2010;
Quinderé et al., 2013; Thomason et al., 2015; Gieselmann and Ostendorf, 2007;
Skantze et al., 2013; Foster et al., 2012a; Holzapfel, 2008; Lucignano et al.,
2013; Chao and Thomaz, 2016; Prommer et al., 2006; Foster et al., 2012b)
Entropy reduction (Hemachandra et al., 2014; Hemachandra and Walter, 2015)
Latency and false cut-in rate (Lala et al., 2018)
Reward/Cost functions (Amiri et al., 2019; Zhang and Stone, 2015; Doshi and
Roy, 2007; Lu et al., 2017; Prommer et al., 2006)
Dialogue duration (Lison, 2015)

###  2.5. Challenges

Dialogue managers which are integrated into a robot do not act independently,
but are part of a bigger architecture. While this is also true for dialogue
managers used without robots, robots often make use of additional sensors and
actuators to integrate multimodal in- and output into the system. Currently,
there is no commonly used off-the-shelf solution for spoken human-robot
interaction, but instead there is a variety of different frameworks, not only
for dialogue management, but also for the related modules. This variety makes
it challenging to select the most appropriate solution in cases where an
existing framework is used. Due to the tight coupling of modules in a robot,
the dialogue managers are also influenced by the problems of other modules,
such as speech recognition errors (Inoue et al., 2020; Lala et al., 2017;
Funakoshi et al., 2007; Quinderé et al., 2013). Apart from improving the
speech recognition module directly, it is possible to include mechanisms into
the dialogue manager that are responsible for dealing with speech recognition
errors (Funakoshi et al., 2007).

Talking to a robot raises expectations about their conversational
capabilities. When those expectations are not met, the user can become
frustrated (Amiri et al., 2019). Expectations are shaped in part by the
morphology of the robot (Kunold et al., 2023), making the consideration of the
morphology on the expected conversational capabilities necessary. For example,
(Milhorat et al., 2019) use the android robot ERICA and state that “Erica’s
realistic physical appearance implies that her spoken dialogue system must
have the ability to hold a conversation in a similarly human-like manner by
displaying conversational aspects such as backchannels, turn-taking and
fillers”. The robot morphology does not only affect the expectations, for
example regarding the human-likeness of its conversational capabilities, but
also limits the tasks the robot can do, impacting the domains the robot has to
be able to converse about.

In human-human conversations, common ground plays an important role and helps
to reduce miscommunication (Stalnaker, 2002). In spoken HRI this is even more
challenging since the perception of humans and robots is not the same.
Expectations based on human-human conversations do not necessarily hold for
human-robot conversations. This can lead to a gap between the perceived versus
the real common ground (Chai et al., 2014).

Interactions with robots are not purely speech based, but they can also make
use of multimodal cues like gestures, gaze or facial expressions. When the
robot is using multimodal input for deciding the next dialogue move, it is not
enough to simply detect the multimodal cues, but they also need to be
integrated for further processing. A separate model for multimodal fusion can
be used that then forwards the fused information to the dialogue manager, as
is done for example in (Holzapfel, 2008; Lucignano et al., 2013; Alonso-Martín
et al., 2013; Li et al., 2006a). Before integrating multimodal cues, a
decision has to be made regarding the required modalities to decide which ones
are necessary or expected in the specific type of interaction and should be
used by the robot. The robot morphology is impacting the multimodal cue
generation as well, as robotic heads, for example, cannot generate pointing
gestures. Therefore, the robot’s appearance is adding both restrictions and
expectations and should be chosen carefully.

When a robot is placed in an environment, it can encounter the same person
multiple times, or have longer interactions with the people, for example when
acting as a guide (Vogiatzis and Karkaletsis, 2008; Lu et al., 2017). In
longer conversations or long-term interactions with multiple conversations,
repetitions over time can annoy the user (Rosenthal and Veloso, 2010). To
solve this problem in long-term interactions, (Rosenthal and Veloso, 2010)
suggest different options. For example, producing a number of different
utterances for the same context which could reoccur, and remembering previous
utterances or even whole conversations. To understand the robot’s behaviour
and its decisions, additional explanations might be required (She et al.,
2014; Dino et al., 2019). Especially if the robot fails without an explanation
the user might misinterpret the situation or cause the failure again, due to
the lack of explanation.

However, this leads to the next problem, which is the resources needed for
designing complex dialogue managers. For example, for handcrafted approaches
the structure has to be defined a priori (Gorostiza and Salichs, 2010; Jiang
et al., 2011), knowledge has to be integrated (Ion et al., 2020) and even then
it will not be able to cover unknown situations (Quinderé et al., 2013;
Hemachandra et al., 2014). In contrast to handcrafted approaches, data-driven
ones need less human effort, but rely on data for learning conversational
behaviour. However, data collection in HRI is expensive (Doshi and Roy, 2007)
and for supervised learning expert feedback is needed (Lison, 2012). It is
possible that the human and the robot follow different conversational
strategies, for example, one tries to exploit previous conversation topics,
while the other one tries to explore new topics. If the robot does not notice
those differences in strategies and rigidly follows its own strategy, it can
lead to high tension in the dialogue (Campos et al., 2018). Collecting data in
an HRI setting is a challenging task, since the data is often multimodal and
the interaction has to take place in a situated setting. This means that the
data collection requires time, human participants and a system that can record
all the required signals. However, if a specific robot is used for recording
data, the generalizability to other robots is still unclear.

The reporting of user studies and their results is still lacking uniformity.
An example for this are the multiple ways of which length can be assessed in
the context of dialogues. Some papers report the number of turns (Holzapfel,
2008; Campos et al., 2018; Chai et al., 2014), while others focus on the
length of turns (Lee et al., 2010; Dino et al., 2019) or the total amount of
time (Johansson and Skantze, 2015). While the heterogeneous reporting on the
length can make comparisons challenging, there are also still papers that do
not report on the interaction length at all.

Human-robot interaction offers the additional challenge of grounding the
interaction in the environment the robot is physically located in. This is
especially the case for multimodal interaction where the referral of objects
can happen, for example, through gestures (Avilés et al., 2010; Aguilar and
Pineda, 2009) or gaze (Skantze et al., 2013). A robot that is referring to
locations around itself, needs to know its own position as well as the names
and places of the locations around it (Nakano et al., 2010; Funakoshi et al.,
2007; Hemachandra et al., 2014; Han et al., 2013). The physical environment of
the robot also contains objects, which the robot or human can refer to.
Especially for robots that can perform object manipulation, it is important to
be able to understand and produce references to those objects (Peltason and
Wrede, 2012; Sugiura et al., 2010; She et al., 2014; Li et al., 2006a; Lison,
2013). Grounding interactions in the environment is especially challenging
since the environment can change over time, with objects being placed and
removed, people appearing and disappearing, and also the robot itself moving
in the environment.

To date, handcrafted dialogue management approaches are still very common in
HRI, due to lack of data, the aforementioned challenges due to the physical
embodiment, but also because of the advantages it offers. If the robot’s
conversation domain is small and transparency is required, a handcrafted
approach might be a good option, whereas a probabilistic approach has the
advantage that the actions can be learned from real interaction data, leading
to less expert knowledge being required.

##  3\. Discussion

Our survey of literature that details DM in the context of HRI research
revealed a variety of used DM approaches, influenced by the task the robot
should perform and the needed reliability and capabilities. In this section,
we will discuss the key challenges that have to be faced to make progress in
dialogue management for social robots.

For task-based human-robot dialogues, the majority of systems are using
handcrafted approaches (see section 2.3) among which model-based approaches
are the most popular (see fig. 4). End-to-end approaches, like they are used
for non-HRI task-based DM (Zhao et al., 2019), have not been seen at all in
the included papers. The resources needed for designing complicated dialogue
managers constitute a challenge. For handcrafted approaches the structure has
to be defined a priori (Gorostiza and Salichs, 2010; Jiang et al., 2011),
knowledge has to be integrated (Ion et al., 2020) and even then they will not
be able to cover unknown situations (Quinderé et al., 2013; Hemachandra et
al., 2014). In contrast to handcrafted approaches, data-driven dialogue
managers need less human effort, but rely on data for learning conversational
behaviour. However, data collection in HRI is expensive (Doshi and Roy, 2007)
and expert feedback is needed for supervised learning (Lison, 2012).

In human-human conversations, common ground plays an important role and helps
to reduce miscommunication (Stalnaker, 2002). In spoken HRI this is even more
challenging since the perception of humans and robots is not the same.
Expectations learned from human-human conversations do not necessarily hold
for human-robot conversations. This can lead to a gap between the perceived
versus the real common-ground (Chai et al., 2014). The physical presence of
robots has an effect on the interaction (Li, 2015), however, it is not clear
how exactly those influences look like for the different combinations as they
are illustrated in 2. This makes it challenging to transfer knowledge gained
from human-human or even human-agent interaction to human-robot interaction.

While the possible interaction domains for DM in HRI span a wide area and can
be combined with varying robots and environments (see fig. 2), most of them
are task-based, for example for deliveries or navigation. Being in control of
the interaction seems to be an important factor for DM in HRI, which makes
handcrafted approaches a compelling option, especially in task-based
interaction domains. One trend we observed is that hybrid approaches divide
the dialogue manager into smaller expert modules that are responsible for
specific tasks within the dialogue manager. The different experts use
different DM strategies and approaches themselves, based on the task that is
assigned to them. This overall strategy of distributing the task to experts
leads to more flexibility in the construction of the interaction, since the
parts that are responsible for different tasks can use the approach that is
best for that specific task.

A capability that is specific to DM in robots, is the grounding of the
dialogue in the physical environment. For DM in HRI, the user and the robot
are both physically situated in the environment, which can take different
forms (see fig. 2). Because of this, they are exposed to changes, for example,
due to people moving or objects being moved. A robot can encounter new or
already known people (Edirisinghe et al., 2018) or unknown objects (Peltason
and Wrede, 2012) during the conversation. This also relates to the question
about endowing robots with a memory. More specifically, the physical
situatedness of robots raises the question of how to manage such environmental
dynamics in a robot’s memory. Due to their situatedness in the real world,
interactions can happen more than once or for a longer time interval with the
same robot. To solve the problem of repetitions over time in long-term
interactions, (Rosenthal and Veloso, 2010) suggest different options to avoid
repetition: producing a number of different utterances for the same context
which could reoccur, either in a rule-based way or by deploying machine
learning-based natural language generation, and remembering previous
utterances or even whole conversations. The question then becomes how to endow
a robot with an effective memory to sustain such long-term interactions.

Due to its physical presence in the environment, the robot’s appearance has to
be taken into account as well (see fig. 2). When talking to a robot, their
physical appearance raises expectations about conversational capabilities,
while not making explicit which ones are actually present, and users can
easily become frustrated when those expectations are not met. Therefore, to
understand the robot’s behaviour and its decisions, additional explanations
will be required (Dino et al., 2019; She et al., 2014). To avoid future
failures, the robot should be transparent about the reason of a failure when
it happens, so that the user can try to avoid it in the future. The
implications the chosen robot morphology has on the dialogue, is rarely
discussed in the surveyed papers. Without this information, it is difficult to
judge if and how the obtained results are transferable to a different robot.
If the choice of robot and the resulting effects on the interaction would be
more commonly discussed in research papers, this would help other researchers
in choosing an appropriate robot appearance for their interactions.

Dialogue managers that are integrated into a robot are part of a bigger
system. Social robots are typically multimodal and therefore depend on
modules, such as sensors, while influencing other modules. Which modalities
are included varies from case to case, as indicated in fig. 2. By influencing
the actuators of the robot, the dialogue can have an immediate impact on the
environment. The dialogue manager does not only interact with the NLU and NLG
modules, but is often also linked to other modalities that, for example,
manage the perception and production of multi-modal cues, like gestures or
gaze. The dialogue manager is in addition influenced by the problems of
certain modules, such as speech recognition errors (Quinderé et al., 2013;
Funakoshi et al., 2007; Lala et al., 2017; Inoue et al., 2020). Apart from
improving the speech recognition module directly, it is possible to include
mechanisms into the dialogue manager that are responsible for dealing with
speech recognition errors (Funakoshi et al., 2007). Due to speech recognition
difficulties, multiple common capabilities focus on types of repair (see
section 2.3). However, while repairing problems in speech recognition with
dialogue management techniques is an option, making the reasons for the
failures explicit can help to improve the speech recognition on an HRI
context. Speech recognition in HRI is impacted by noise of the robot’s motors
and the environment the robot is placed in, especially during experiments
outside of the lab. Robots can encounter multiple people, both at the same
time and at different times, whose age, accent and way of speaking can vary.
All of those factors influence the performance of the speech recognition
module.

During conversations, it is problematic if the robot loses track of the user
or is ignoring multimodal cues (Li et al., 2006b), while the human expects the
robot to be able to process them. Since the dialogue manager is integrated
into the robot’s architecture, the whole system should be taken into account
for the evaluation. This means that it is not enough to evaluate the dialogue
manager in a decoupled manner. Rather, it is necessary to consider the effects
of the integration into the robot. Using real users for evaluations comes with
the advantage that it provides a clearer picture of how the system performs in
actual interactions.

Several of the challenges we outlined relate to the variance of interaction
domains, appearances, modalities and environments in HRI that need to be
incorporated into the DM approach. One of these challenges is the integration
into a bigger architecture, since problems in the other modules that relate to
the multi-modal features of several robot types can impact the dialogue
management and need to be accounted for. Another challenge is the lack of
datasets for human-robot interaction, to further evolve probabilistic and
hybrid approaches to DM in HRI. The data requirements are not only related to
the domain of the interaction, but also to the type of robot and the location
of the interaction, since those can impact the conversation. The problem of
lack of data could be addressed by using large language models, that only have
to be fine-tuned or can be directly prompted including recent turns in the
conversation. However, they still have difficulties with situational awareness
and the integration of the robot’s sensors and actuators (Billing et al.,
2023; Zhang and Soh, 2023). Human-robot datasets cannot easily be substituted
by human-human datasets, since robots have different abilities than humans and
lack most of the common sense abilities humans have. Datasets, moreover, even
need to take specific robot shapes and forms into account, as the capabilities
present in different robot platforms differ from each other. It is therefore
important to maximise transparency about the robot’s conversational
capabilities.

HRI researchers new to DM can make an informed decision regarding the
selection of a dialogue management approach by assessing which of the
presented approaches fits their requirements and limitations best. Based on
our observations, hybrid approaches are a good option for more complex
dialogue managers where the dialogue is an essential part of the interaction.
If the interaction is simpler from a dialogue perspective, or not enough data
is available, handcrafted approaches, especially model-based ones, are a
viable option. While a variety of robots with a range of different shapes and
appearances has been used for spoken HRI so far, it would be helpful if the
implications of robot platform choices on the dialogue would be discussed more
extensively. Especially due to the situatedness in the environment, the
required multimodal inputs need to be taken into account in advance. As a
start we would suggest to include only those, however, that are necessary for
the interaction to not overly complicate the dialogue management system. Using
already used evaluation metrics, especially those that are easy to record,
would help to compare performance of the systems used.

Even though dialogue is common in human-robot interactions, it is rarely the
main focus of the interaction design. In current research, dialogue is often
only seen as a tool to achieve a task with a robot. Moreover, in papers that
do take a dialogue management perspective, the dialogue manager is typically
evaluated in non-embodied agents, which neglects robot-specific challenges
that need to be addressed. In order to more structurally address these
challenges, it is important that the best of both fields is combined to
develop a more standardised approach for DM that can be drawn upon in the
diverse interaction scenarios that arise in HRI studies.

###### Acknowledgements.

This research was (partially) funded by the Hybrid Intelligence Center, a
10-year programme funded by the Dutch Ministry of Education, Culture and
Science through the Netherlands Organisation for Scientific Research,
https://hybrid-intelligence-centre.nl, grant number 024.004.022.

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