Sebastian Wallkötter
email: sebastianwallkötter@it.uu.se

Silvia Tulli
email: silvia.tulli@gaips.inesc-id.pt

Ana Paiva

Explainable Embodied Agents Through Social Cues: A Review

Keywords: CCS Concepts:, Computing methodologies → Intelligent agents, Reasoning about belief and knowledge, • Computer systems organization → Robotics, • General and reference → Metrics, • Human-centered computing → User models transparency, interpretability, explainability, accountability, expressive behaviour, intelligibility, legibility, predictability, explainable agency, embodied social agents, robots

) providing an overview of existing definitions, (2) showing how explainability is implemented and how it exploits different social cues, and (3) showing how the impact of explainability is measured. Additionally, we present a list of open questions and challenges that highlight areas that require further investigation by the community. This provides the interested reader with an overview of the current state-of-the-art.

INTRODUCTION

Embodied agents are capable of engaging in face-to-face interaction with humans through both, verbal and non-verbal behaviour. They are employed in situations in which joint activities occur, requiring teammates to be able to perceive, interpret, and reason about intentions, beliefs, desires, and goals to perform the right actions. However, even if an embodied agent is endowed with communicative behaviours, merely having them does not guarantee that its actions are understood correctly.

Enabling an embodied agent to use its own behaviours to be better understood by human partners is studied as explainability (among other terms) and is an active research area (see figure 1). Embodied agents have access to differing social cues compared to their non-embodied counterparts, which sets them apart from artificial intelligence (AI) systems that are not embodied. These social cues are handy, because users -particularly non-experts -often have limited understanding of the underlying mechanisms by which an embodied agents choose their actions. Revealing such mechanisms builds trust and is considered ethical in AI [START_REF]High-Level Expert Group on Artificial Intelligence, AI HLEG, and High-Level Expert Group on Artificial Intelligence[END_REF] and robotics alike. Embodiment-related social cues may help in this context to efficiently communicate such underlying mechanisms for a better interaction experience. Consequently, it is interesting to investigate explainability with a special focus on embodiment.

In human-robot interaction (HRI), embodiment often refers to the use of physical embodiment; however, testing a single aspect of the interaction in online studies is also common, such as the observation of the agent behavior in a simulation or the intepretation of its textual explanations. While this might introduce a confounding difference between the settings (e.g., not accounting for the effect of the embodiment) [START_REF] Li | The benefit of being physically present: A survey of experimental works comparing copresent robots, telepresent robots and virtual agents[END_REF], in such studies, the risk of introducing a confounding factor is outweighed by the possibility isolating the aspect of interest. In explainability research studies with virtual agents manipulating a single behaviour happens frequently, and typically with the aim of adding the investigated aspect to a physically embodied agent later during the research. Hence, we chose to not only review physically embodied agents but also include virtual studies.

Although the concept of explainable embodied agents has become increasingly prevalent, the idea of explaining a system's action is not new. Starting in the late 1970s scholars already began to investigate how expert systems [START_REF] Scott | Explanation Capabilities of Production-Based Consultation Systems[END_REF][START_REF] William Van Melle | MYCIN: a knowledge-based consultation program for infectious disease diagnosis[END_REF][START_REF] Warren | Implementing Prolog-Compiling Predicate Logic Programs[END_REF] or semantic nets [START_REF] Georgeff | The belief-desire-intention model of agency[END_REF][START_REF] Michael | Reconstructive expert system explanation[END_REF], which use classical search methods, encode their explanations in human readable form. Two prominent examples of such systems are MYCIN [START_REF] William Van Melle | MYCIN: a knowledge-based consultation program for infectious disease diagnosis[END_REF], a knowledge-based consultation program for the diagnosis of infectious diseases, and PROLOG, a logic-based programming language. MYCIN is particularly interesting because it has already explored the idea of interactive design to allow both, inquiry about the decision in the form of specific questions, and rule-extraction based on previous decision criteria [START_REF] William Van Melle | MYCIN: a knowledge-based consultation program for infectious disease diagnosis[END_REF].

Connecting to this history, explainable embodied agents can be considered a subcategory of explainable AI systems, as they use techniques similar to those mentioned above but with the aim of interacting autonomously with humans and the environment. Therefore, explainable embodied agents should be able to explain their reasoning (justification), explain their view of the environment (internal state) or explain their plans (intent). These abilities can, for example, support the collaboration between agents and humans or improve the agent's learning by aiding the human teacher in selecting informative instruction [START_REF] Chao | Transparent active learning for robots[END_REF]. Furthermore, explainable AI techniques might exploit attention direction to communicate points of confusion for embodied agents using gaze and verbalization [START_REF] Perlmutter | Situated Language Understanding with Human-like and Visualization-Based Transparency[END_REF]. Some examples in the literature have proposed generating explanations of reasoning processes using natural language templates Wang et al. [START_REF] Wang | The Impact of POMDP-Generated Explanations on Trust and Performance in Human-Robot Teams[END_REF]. In contrast, other work focuses on making the actions of the embodied agents explicit by design (i.e., legible) [START_REF] Anca D Dragan | Legibility and Predictability of Robot Motion[END_REF].

In this paper, we want to show how these unique social cues can be used for building explainable embodied agents, and highlight which aspects require further inquiry. As such, we contribute to the field of explainability with a review of the existing literature on explainability that organizes it by [START_REF] Kumar Akash | Improving Human-Machine Collaboration Through Transparency-based Feedback -Part I : Human Trust and Workload Model[END_REF] providing an overview of existing definitions, [START_REF] Kumar Akash | Improving Human-Machine Collaboration Through Transparency-based Feedback -Part II: Control Design and Synthesis[END_REF] showing how explainability is implemented and how it exploits different social cues, and by [START_REF] Alonso | System transparency in shared autonomy: A mini review[END_REF] showing how the effect of explainability is measured. This review aim to provide interested readers with an overview of the current state-of-the-art. Additionally, we present a list of open questions and challenges that highlight areas that require further investigation by the community.

Manuscript submitted to ACM

Similar Reviews

Other authors have previously written about explainability in the form of position papers and reviews [START_REF] Felzmann | Robots and Transparency: The Multiple Dimensions of Transparency in the Context of Robot Technologies[END_REF][START_REF] Fischer | Increasing trust in human-robot medical interactions: Effects of transparency and adaptability[END_REF][START_REF] Jacucci | Symbiotic interaction: A critical definition and comparison to other human-computer paradigms[END_REF][START_REF] Joseph | Being Transparent about Transparency : A Model for Human-Robot Interaction[END_REF][START_REF] Spagnolli | Transparency as an Ethical Safeguard[END_REF][START_REF] Theodorou | Why is my Robot Behaving Like That? Designing Transparency for Real Time Inspection of Autonomous Robots[END_REF]. Doshi-Velez and Kim [START_REF] Doshi | Towards A Rigorous Science of Interpretable Machine Learning[END_REF] and Lipton [START_REF] Lipton | The Mythos of Model Interpretability[END_REF] sought to refine the discourse on interpretability by identifying the desiderata and methods of interpretability research. Their research focused on the interpretation of machine learning systems from a human perspective and identified trust, causality, transferability, informativeness, and fair and ethical decision-making as key aspects of their investigation. Rosenfeld and Richardson [START_REF] Rosenfeld | Explainability in human-agent systems[END_REF] provided a notation for defining explainability in relation to related terms such as interpretability, transparency, explicitness, and faithfulness. Their taxonomy encompasses the motivation behind the need for explainability (unhelpful, beneficial, critical), the importance of identifying the target of the explanation (regular user, expert user, external entity), when to provide the explanation, how to measure the effectiveness of the explanation and which interpretation of the algorithm has been used. Anjomshoae et al. [START_REF] Anjomshoae | Explainable Agents and Robots: Results from a Systematic Literature Review[END_REF] conducted a systematic review on the topic of explainable robots and agents and clustered the literature with respect to the user's demographics, the application scenario, the intended purpose of an explanation, and whether the study was grounded in social science or had a psychological background. The review summarized the methods used to implement the explanation to the user with its dynamics (context-aware, user-aware, both or none), and the types of explanation modality. Alonso and De La Puente [START_REF] Alonso | System transparency in shared autonomy: A mini review[END_REF] proposed a review of the system's explainability in a shared autonomy framework, stressing the role of explainability in flexible and efficient human-robot collaborations. Their review underlines how explainability should vary in relation to the level of system autonomy and how the exploitation of explainability mechanisms can result from an explanation, a property of an interface, or a mechanical feature.

Other reviews explored the cognitive aspects of explainability by targeting the understanding of behaviour explanations and how this helps people find meaning in social interaction with artificial agents. The book by Malle [START_REF] Bf Malle | How the mind explains behavior: folk explanations, meaning, and social interaction[END_REF] argued that people expect explanations using the same conceptual framework used to explain human behaviours. Similarly, the research of Miller [START_REF] Miller | Explanation in artificial intelligence: Insights from the social sciences[END_REF] focused on the definition of explanations in other relevant fields, such as philosophy, cognitive psychology/science, and social psychology. The author described an explanation as contrastive, selected and social, specifying that the most likely explanation is not always the best explanation for a person.

Methodology

For this review, we chose to use a keyword based search in Scopus1 database to identify relevant literature, as this method makes our search reproducible.

First, we identified a set of relevant papers in an unstructured manner based on previous knowledge of the area. From each paper, we extracted both, the indexed and the author keywords, and rank ordered each keyword by occurrence.

Using this method, we identified key search terms such as human-robot interaction, transparent, interpretable, explainable, or planning.

We then grouped these keywords by topic (see table 1) and performed a pilot search on each topic to determine how many of the initially identified papers were recovered. We then combined each group using AND, which led to a corpus of 263 papers2 . All authors participated in this initial extraction process. AND (LIMIT-TO (SUBJAREA, "COMP") OR LIMIT-TO (SUBJAREA, "ENGI") OR LIMIT-TO (SUBJAREA, "MATH") OR LIMIT-TO (SUBJAREA, "SOCI") OR LIMIT-TO (SUBJAREA, "PSYC") OR LIMIT-TO (SUBJAREA, "NEUR"))

Next, we manually filtered this list to further remove unrelated work by judging relevance based on titles, abstracts,

and full text reads. To ensure selection reliability, both main authors rated inclusion of each paper independently. If both labelled the paper as relevant, we included the paper; similarly, if both labelled it as unrelated, we excluded it. For papers with differing decisions, we discussed their relevance and made a joint decision regarding the paper's inclusion.

This left us with 32 papers for the final review 3 .

For the excluded papers, each main author indicated why a paper was excluded for the following reasons:

Increase (in %) • no article The paper was a book chapter or review paper. (∼ 15.63% excluded)

• wrong topic The paper presented work in a different focus area, e.g, material science, teleoperation, or generically making robots more expressive (without considering explainability). (∼ 45.98% excluded) • wrong language The paper was not written in English. (∼ 0.46% excluded)

• no embodiment The paper did not consider an embodied agent. (∼ 11.26% excluded)

• no autonomy The paper did not consider autonomous embodied agents. (∼ 13.33% excluded)

• no social interaction The paper did not investigate explainability in a context where a human was present.

(∼ 13.10% excluded)

Figure 1 shows a comparison between the publication rate of the identified papers (core papers) and the publication rate of the general field, which is measured, as a proxy, by papers published using the keyword human-robot interaction.

We can see that there is a growing interest in the topic reviewed here.

DEFINITIONS

Review of Definitions

Before starting to look into how different social cues are exploited, it is important to understand what different scholars mean when they talk about explainability.

Among the surveyed papers, we analyzed the terms used in the title. We extrapolated the root terms and categorized them (e.g. explanation -> explainability, expressive -> expressivity). Assuming that papers with the same authors share the same definition, we grouped them by author, and only report the most recent definition in Table 6. When the definition was from an outside source not cited before, we mentioned both the definition and the reference. Some authors that refer to transparency identify transparency as the process or the capability of revealing hidden or unclear information about the robot. Akash et al. [START_REF] Kumar Akash | Improving Human-Machine Collaboration Through Transparency-based Feedback -Part I : Human Trust and Workload Model[END_REF][2] and Ososky et al. [START_REF] Ososky | Determinants of system transparency and its influence on trust in and reliance on unmanned robotic systems[END_REF] cited the definition of Chen et al. [START_REF] Jessie | Effects of Agent Transparency on human-autonomy teaming effectiveness[END_REF], which named transparency the descriptive quality of an interface. The descriptiveness of the interface affects the operator on three levels (perception, comprehension, and projection) leveraged by Endsley's model of situation awareness [START_REF] Endsley | Toward a theory of situation awareness in dynamic systems[END_REF]. Ososky et al. [START_REF] Ososky | Determinants of system transparency and its influence on trust in and reliance on unmanned robotic systems[END_REF] further refer to the dictionary definition of transparency4 ; thus, they chose the property of being able to be seen through or easy to notice or understand as their definition. Chao et al. [START_REF] Chao | Transparent active learning for robots[END_REF] gave a similar definition in the context of robot active learning referring to transparency as revealing to the teacher unknown information about the robot.

In an extension, Floyd and Aha [START_REF] Floyd | Incorporating transparency during Trust-Guided behavior adaptation[END_REF] and Hayes and Shah [START_REF] Hayes | Improving Robot Controller Transparency Through Autonomous Policy Explanation[END_REF] refer to both explainability and transparency. Hayes and Shah [START_REF] Hayes | Improving Robot Controller Transparency Through Autonomous Policy Explanation[END_REF] describe explainability as the embodied agent's ability to synthesize policy descriptions and respond to human collaborators.

Starting from the research of Kulkarni et al. [START_REF] Kulkarni | Explicable planning as minimizing distance from expected behavior[END_REF] and Zhang and Liu [START_REF] Zhang | Design of autonomous navigation system based on affective cognitive learning and decision-making[END_REF], Chakraborti et al. [START_REF] Chakraborti | Plan Explanations as Model Reconciliation: Moving Beyond Explanation as Soliloquy[END_REF] and [START_REF] Chakraborti | Balancing explicability and explanations for human-aware planning[END_REF] formulated their idea of explainability as a model reconciliation problem. They use explanations to move the human model of the robot to be in conformance with the embodied agent model. Gong and Zhang [START_REF] Gong | Behavior Explanation as Intention Signaling in Human-Robot Teaming[END_REF] differentiate their work by shifting the interest in signalling robot intentions before actions occur. Along similar lines, Arnold et al. [START_REF] Arnold | FactSheets: Increasing trust in AI services through supplier's declarations of conformity[END_REF] defined explainability as a policy update given by the robot to the human to reduce the likelihood of costly or dangerous failures during joint task execution.

Baraka and Veloso [START_REF] Baraka | Mobile Service Robot State Revealing Through Expressive Lights: Formalism, Design, and Evaluation[END_REF] employed the term expression for externalizing hidden information of an agent. The work of Kwon et al. [START_REF] Kwon | Expressing Robot Incapability[END_REF] extended this notion of expressivity by targeting the communication of robot incapability. To do so, the robot should reveal the intentions and the cause of its failures. The same concept is referred to using the word communicability; e.g., Huang et al. [START_REF] Huang | Goal-oriented action planning in partially observable stochastic domains[END_REF] refer to communicate for describing the robot capability of expressing its objectives and the robots capability to enable end-users to correctly anticipate its behaviour.

Similarly, Schaefer et al. [START_REF] Schaefer | Relinquishing manual control collaboration requires the capability to understand robot intention[END_REF] investigated the understandability of the embodied agent's intentions for effective collaborations with humans. Following this idea, Grigore et al. [START_REF] Corina Grigore | Preference-Based Assistance Prediction for Human-Robot Collaboration Tasks[END_REF] referred to predictability, building upon hidden state representation.

Other studies in the literature do not refer to a specific capability of their system in the title but highlighted the application scenarios (e.g., autonomous driving [START_REF] Brown | The trouble with autopilots: Assisted and autonomous driving on the social road[END_REF], human robot teams in the army [START_REF] Evans | The future of human robot teams in the army: Factors affecting a model of human-system dialogue towards greater team collaboration[END_REF], interactive robot learning [START_REF] Lütkebohle | The curious robot -Structuring interactive robot learning[END_REF]) or mentioned behavioural dynamics [START_REF] Lamb | The Paths We Pick Together: A Behavioral Dynamics Algorithm for an HRI Pick-and-Place Task[END_REF] and human-machine confidence [START_REF] Legg | Visual analytics for collaborative human-machine confidence in human-centric active learning tasks[END_REF].

Although there exists large diversity and inconsistency in the language, there seem to be commonalities in what the authors identify as explainability. Furthermore, other authors that use terms such as transparency, expressivity, understandability, predictability and communicability present definitions that are congruent with those provided for explainability. We noticed that all the given definitions share the following aspects: (1) they all refer to an embodied agent's capability or system's module, (2) they all specify that what should be explained/signalled are the internal workings of the robot (e.g. agent's intent, policy, plan, future plans), and (3) they all consider the human as a target of the explanations. Explainability [START_REF] Arnold | FactSheets: Increasing trust in AI services through supplier's declarations of conformity[END_REF][START_REF] Chakraborti | Balancing explicability and explanations for human-aware planning[END_REF][START_REF] Chakraborti | Plan Explanations as Model Reconciliation: Moving Beyond Explanation as Soliloquy[END_REF][START_REF] Gong | Behavior Explanation as Intention Signaling in Human-Robot Teaming[END_REF][START_REF] Wang | The Impact of POMDP-Generated Explanations on Trust and Performance in Human-Robot Teams[END_REF][START_REF] Wang | Trust calibration within a human-robot team: Comparing automatically generated explanations[END_REF][START_REF] Wang | Is it my looks? Or something i said? The impact of explanations, embodiment, and expectations on trust and performance in human-robot teams[END_REF], [START_REF] Floyd | Using explanations to provide transparency during trust-guided behavior adaptation 1[END_REF][START_REF] Hayes | Improving Robot Controller Transparency Through Autonomous Policy Explanation[END_REF] Expressivity [START_REF] Baraka | Mobile Service Robot State Revealing Through Expressive Lights: Formalism, Design, and Evaluation[END_REF][START_REF] Kwon | Expressing Robot Incapability[END_REF][START_REF] Zhou | Expressive Robot Motion Timing[END_REF] Understandability [START_REF] Schaefer | Relinquishing manual control collaboration requires the capability to understand robot intention[END_REF][START_REF] Sciutti | Understanding object weight from human and humanoid lifting actions[END_REF] Predictability [START_REF] Corina Grigore | Preference-Based Assistance Prediction for Human-Robot Collaboration Tasks[END_REF][START_REF] Sheikholeslami | Prediction and Production of Human Reaching Trajectories for Human-Robot Interaction[END_REF] Communicability [START_REF] Huang | Enabling Robots to Communicate Their Objectives[END_REF][START_REF] Lee | Self-Assessing and Communicating Manipulation Proficiency Through Active Uncertainty Characterization[END_REF] None [START_REF] Brown | The trouble with autopilots: Assisted and autonomous driving on the social road[END_REF][START_REF] Khoramshahi | A dynamical system approach to task-adaptation in physical human-robot interaction[END_REF][START_REF] Lamb | The Paths We Pick Together: A Behavioral Dynamics Algorithm for an HRI Pick-and-Place Task[END_REF][START_REF] Legg | Visual analytics for collaborative human-machine confidence in human-centric active learning tasks[END_REF][START_REF] Lütkebohle | The curious robot -Structuring interactive robot learning[END_REF]

Our Definition

Therefore, we provide a definition that aims to be comprehensive for our literature. We define the explainability of embodied social agents as their ability to provide information about their inner workings using social cues, such that an observer (target user) can infer how/why the embodied agent behaves the way it does.

Motivation

While investigating the definitions, we noticed that the motivation of the experiment plays a key role in the choice of a specific definition. In particular, we identified the following reasons for investigating explainability:

• Interactive machine/robot learning investigates the need of explainability in the context of robot learning.

The main idea is that revealing the embodied agent's internal states allows the human teacher to provide more informative examples [START_REF] Arnold | FactSheets: Increasing trust in AI services through supplier's declarations of conformity[END_REF][START_REF] Chao | Transparent active learning for robots[END_REF][START_REF] Lütkebohle | The curious robot -Structuring interactive robot learning[END_REF][START_REF] Thomaz | Teachable robots: Understanding human teaching behavior to build more effective robot learners[END_REF]. • Human Trust states that adding explainability increases human trust and system reliability. This motivation empathizes the importance of communicating the agent's uncertainty, incapability, and existence of internally conflicting goals [START_REF] Kwon | Expressing Robot Incapability[END_REF][START_REF] Roncone | Transparent role assignment and task allocation in human robot collaboration[END_REF][START_REF] Schaefer | Relinquishing manual control collaboration requires the capability to understand robot intention[END_REF][START_REF] Wang | Trust calibration within a human-robot team: Comparing automatically generated explanations[END_REF].

• Teamwork underlines the value of explainability in human-robot collaboration scenarios to build shared mental models and predict the embodied agent's behaviour [START_REF] Chakraborti | Plan Explanations as Model Reconciliation: Moving Beyond Explanation as Soliloquy[END_REF][START_REF] Hayes | Improving Robot Controller Transparency Through Autonomous Policy Explanation[END_REF][START_REF] Huang | Enabling Robots to Communicate Their Objectives[END_REF][START_REF] Legg | Visual analytics for collaborative human-machine confidence in human-centric active learning tasks[END_REF][START_REF] Sciutti | Understanding object weight from human and humanoid lifting actions[END_REF].

• Ethical decision-making suggests that communicating the embodied agent's decision-making processes and capabilities, paired with situational awareness, increases a user's ability to make good decisions [START_REF] Kumar Akash | Improving Human-Machine Collaboration Through Transparency-based Feedback -Part I : Human Trust and Workload Model[END_REF][START_REF] Kwon | Expressing Robot Incapability[END_REF][START_REF] Poulsen | Care Robot Transparency Isn't Enough for Trust[END_REF].

We have aggregated the individual definitions used in each paper and the motivations behind them in table 6.

Guidance and dialogue with a human tutor are aspects that are important for interactive machine/robot learning ("dialog to guide human actions" [START_REF] Lütkebohle | The curious robot -Structuring interactive robot learning[END_REF], "revealing to the teacher what is known and what is unclear" [START_REF] Chao | Transparent active learning for robots[END_REF]). Providing information about the level of uncertainty and expressing robot incapability are core concepts of explainability that enhance human trust ("communicate uncertainty" [START_REF] Wang | Trust calibration within a human-robot team: Comparing automatically generated explanations[END_REF], "provide appropriate measures of uncertainty" [START_REF] Roncone | Transparent role assignment and task allocation in human robot collaboration[END_REF], "express robot incapability" [START_REF] Kwon | Expressing Robot Incapability[END_REF]). The ability to anticipate an embodied agent's behaviour and establish a two-way collaborative dialogue by identifying relevant differences between the humans' and the robots' model are shared elements of the definitions [START_REF] Huang | Enabling Robots to Communicate Their Objectives[END_REF], "establish two-way collaborative dialogue" [START_REF] Legg | Visual analytics for collaborative human-machine confidence in human-centric active learning tasks[END_REF], "reconcile the relevant differences between the humans' and robot's model" [START_REF] Chakraborti | Explicability? Legibility? Predictability? Transparency? Privacy? Security? The Emerging Landscape of Interpretable Agent Behavior[END_REF], "share expectations" [START_REF] Hayes | Improving Robot Controller Transparency Through Autonomous Policy Explanation[END_REF]). Authors that refer to ethical decision-making identify the communication of intentions and context-dependent recommendations as crucial information ("robot's real capabilities, intentions, goals and limitations" [START_REF] Poulsen | Care Robot Transparency Isn't Enough for Trust[END_REF], "context-dependent recommendations based on the level of trust and workload" [START_REF] Kumar Akash | Improving Human-Machine Collaboration Through Transparency-based Feedback -Part II: Control Design and Synthesis[END_REF]).

SOCIAL CUES

We have claimed above that embodied agents can become explainable using unique types of social cues that are not available to agents lacking such embodiment. One example is the ability to point to important objects in a scene -assuming the agent has an extremity that affords pointing. A non-embodied agent has to use a different way to communicate the importance of that object.

Hence, we screened the core papers to check which modality the authors deployed to make the agent more explainable.

We then logically grouped the core papers based on these types of social cues and identified five groups:

• Speech A lexical statement uttered verbally using a text-to-speech mechanism.

• Text A lexical statement displayed as a string presented as an element of a typically screen-based user interface.

• Movement A movement that is either purely communicative, or that alters an existing movement to make it more communicative.

• Imagery A drawing or image (often annotated) presented as an element of a user interface (typically screenbased).

• Other/Unspecified All papers that use social cues that do not fit within above set of categories, or where the authors did not explicitly specify the modality (the latter is marked with an asterisk*). This grouping is shown in table 3. Surprisingly, our search did not yield any papers that investigate non-lexical utterances (beeping noise, prosody, etc.), which was contrary to our expectations. A possible explanation for this could be that our search terms did not capture a broad enough scope, because experiments investigating such utterances may use yet again a different terminology. Another possibility could be that it seems much harder to communicate an explanation through beeps and boops instead of using speech; especially when considering the wide availability of text-to-speech synthesizers (TSSs).

The wide availability of TTS synthesizers may also explain another interesting result of this analysis. Many works focus on lexical utterances (speech and text). Potentially, such utterances are seen as easier to work with when giving explanations because of the high expressivity of natural language.

On the other hand, lexical utterances may add additional complexity to the interaction, because a sentence has to be interpreted and understood, whereas, other social cues may be faster/easier to interpret. While there does exist work Manuscript submitted to ACM that investigates the added cognitive load of having explainability versus not having explainability [START_REF] Wang | Trust calibration within a human-robot team: Comparing automatically generated explanations[END_REF], comparing lexical utterances with other forms of explainability is currently underexplored. This prompts the question of whether lexical utterances are always superior to achieve explainability and, if not, under what circumstances other social cues perform better.

EXPLAINABILITY MECHANISMS

Next, we report and discuss the methods employed to achieve explainable behaviours in social agents with a specific focus on embodied agents. From an HRI perspective, introducing explainability mechanisms is challenging, as uncertainty is inherent to the whole process of interaction from perception to decision and action. In addition, the methods used to implement explainability require explicit consideration of the human capability to correctly infer the agent goals, intentions or actions from the observable cues.

Looking at one specific implementation, Thomaz and Breazeal [START_REF] Thomaz | Transparency and Socially Guided Machine Learning[END_REF] introduced the socially guided machine learning (SG-ML) framework which seeks to augment traditional machine learning models by enabling them to interact with humans. Two interrelated questions are investigated: (1) how do people want to teach embodied agents and (2) how do people design embodied agents that learn effectively from natural interaction and instruction. This framework considers a reciprocal and tightly coupled interaction; the machine learner and human instructor cooperate to simplify the task for each other. SG-ML considers explainability to be a communicative act that helps humans understand the machine's internal state, intent, or objective during the learning process.

Interactive situations. As humans interact with robots, explainability becomes a key element. Different interactive scenarios have been explored in the analysed papers, in particular, scenarios where humans shape the behaviour of a robot by providing instructions and/or social cues through interactive learning techniques. Despite of a strong emphasis on interactive robot learning scenarios, there are also other types of tasks as illustrated by Figure 2. For example, the behaviour shaping explored in [START_REF] Knox | Interactively Shaping Agents via Human Reinforcement: The TAMER Framework[END_REF][START_REF] Najar | Interactively shaping robot behaviour with unlabeled human instructions[END_REF] aims to exploit instructions and/or social cues to steer robot actions towards desired behaviours. Various interaction schemes have been proposed including instructions [START_REF] Grizou | Robot learning simultaneously a task and how to interpret human instructions[END_REF][START_REF] Victor Paléologue | Semantic-based interaction for teaching robot behavior compositions[END_REF], advice [START_REF] Griffith | Policy shaping: Integrating human feedback with Reinforcement Learning[END_REF], demonstrations [START_REF] Argall | A survey of robot learning from demonstration[END_REF], guidance [START_REF] Najar | Training a robot with evaluative feedback and unlabeled guidance signals[END_REF][START_REF] Halit | Effect of human guidance and state space size on Interactive Reinforcement Learning[END_REF], and evaluative feedback [START_REF] Knox | Training a Robot via Human Feedback: A Case Study[END_REF][START_REF] Najar | Training a robot with evaluative feedback and unlabeled guidance signals[END_REF]. Then, computational models, mostly based on machine learning, are exploited to modify agent states 𝑠 and actions 𝑎 to achieve a certain goal 𝑔.

As mentioned by Broekens and Chetouani [START_REF] Broekens | Towards Transparent Robot Learning through TDRL-based Emotional Expressions[END_REF], most of computational approaches for social agents consider a primary task, e.g., learning to pick an object, and explainability arises as a secondary task by either communicating the agent's internal states, intentions, or future goals. Existing works distinguish the nature of actions performed by the agent, such as task -oriented actions 𝑎 T and communication -oriented actions 𝑎 C . 𝑎 T are used to achieve a goal 𝑔 such as sorting objects. 𝑎 C are used by the agent to communicate with humans such as queries or pointing to objects. This follows from the speech act theory [START_REF] Koller | Speech Acts: An Essay in the Philosophy of Language[END_REF], which treats communication as actions that have an intent and an effect (a change of mind by the receiver of the communication).

In such a context, explainability mechanisms are employed to reduce uncertainty during the shaping process using communicative actions 𝑎 C before, during, or after performing a task action 𝑎 T , which will change the agent's next state 𝑠 ′ . The challenge for explainability mechanisms is then to transform agent states 𝑠 and task oriented actions 𝑎 T into communicative actions using either using natural language or non-verbal cues. To tackle this challenge, several explainability mechanisms have been proposed for embodied agents.

Computational paradigms. Various computational paradigms are employed ranging from supervised learning to reinforcement learning. In supervised learning, a machine is trained using data, e.g., different kinds of objects, which are labelled by a human supervisor. In the case of interactive robot learning, the supervisor is a human teacher and provides labelled examples based on embodied agent queries or explanations. In addition, the level of expertise of the human is rarely questioned and considered ground truth. To tackle these challenges, Chao et al. [START_REF] Chao | Transparent active learning for robots[END_REF] proposed an active learning framework for teaching embodied agents to classify pairs of objects (𝑎 T ). Active learning is a type of interactive machine learning that allows the learner to interactively query the supervisor to obtain labels for new data (𝑎 C ). By doing so, the robot improves both learning and the explainability by communicating about uncertainty. Often, active learning is a form of semi-supervised learning, that combines human supervision and processing of unlabelled data.

Another paradigm is reinforcement learning (RL) [START_REF] Richard | Reinforcement learning: An introduction[END_REF], which is one of the three basic machine learning paradigms.

Here, an agent acts in an environment, observing its state 𝑠 and receiving a reward 𝑟 . Learning is performed by a trial-and-error process through interaction with the environment and leverages the Markov decision process (MDP) framework. MDPs are used to model the agent's policy and help with decision making under uncertainty. This paradigm allows one to represent, plan, or learn an optimal policy -a mapping from current state to action. Analysing this policy provides insights into future and current states and actions. Hayes and Shah [START_REF] Hayes | Improving Robot Controller Transparency Through Autonomous Policy Explanation[END_REF] developed a policy explanation framework based on the analysis of execution traces of an RL agent. The method generates explanations (𝑎 C ) about the learned policy (𝑎 T ) in a way that is understandable to humans. In RL, theoretical links between (task) learning schemes and emotional theories could be performed. Broekens and Chetouani [START_REF] Broekens | Towards Transparent Robot Learning through TDRL-based Emotional Expressions[END_REF] investigated how temporal difference learning [START_REF] Richard | Reinforcement learning: An introduction[END_REF] could be employed to develop an emotionally expressive learning robot that is capable of generating explainable behaviours via emotions.

To increase the understanding of robot intentions by humans, the notion of legibility is often introduced in robotics.

Legibility and explainability are considered similar notions that aim to reduce ambiguity over possible goals that might be achieved. One key concept for achieving legibility/explainability is to explicitly consider a model of the human observer, and find plans that disambiguate possible goals. Dragan et al. [START_REF] Anca D Dragan | Legibility and Predictability of Robot Motion[END_REF] proposed a mathematical model able to distinguish between legibility and predictability. Legibility is defined as the ability to anticipate the goal, and predictability, is defined as the ability to predict the trajectory. The mathematical model exploits observer expectations to generate legible/explainable plans. Huang et al. [START_REF] Huang | Enabling Robots to Communicate Their Objectives[END_REF] propose modelling how people infer objectives from observed behaviour, and then selecting those behaviours that are maximally informative. Inverse reinforcement learning is used to model observer capability of inferring intentions from the observation of agent behaviours. Explainability implementations based on these methods consider that task-oriented actions (𝑎 T ) and communicative actions (𝑎 C ) are performed through the same channel, e.g., movement of the robot's arm both achieves a task and communicates the goal [START_REF] Sheikholeslami | Prediction and Production of Human Reaching Trajectories for Human-Robot Interaction[END_REF], [START_REF] Sciutti | Understanding object weight from human and humanoid lifting actions[END_REF].

Explainability mechanisms. In artificial intelligence (AI), explainability addresses the understanding of the mechanisms by which a model works aiming to reduce the model's black box [START_REF] Barredo Arrieta | Explainable Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI[END_REF]. Deep learning is a typical black-box machine learning method that achieves data representation learning using multiple nonlinear transformations. In contrast, a linear model is considered as explainable since the model is fully understandable and explorable by means of mathematical analysis and methods. In Barredo Arrieta et al. [START_REF] Barredo Arrieta | Explainable Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI[END_REF], the authors argue that a model is considered to be explainable if by itself it is understandable and propose various levels of model explainability: (1) simulatability, the ability to be simulated or conceptualized strictly by a human, (2) decomposability, the ability to explain each part of the model, and

(3) algorithmic transparency, the ability of the user to understand the process followed by the model to produce any given output from its input data. Intrinsic explainability refers to models that are explainable by design (Figure 2). Post-hoc (external) explainability refers to explainability mechanisms that are applied after the decision or execution of actions. Post-hoc methods are decoupled from the model and aim to enhance the explainability of models that are not explainable by design (intrinsic) [START_REF] Lipton | The Mythos of Model Interpretability[END_REF]. Post hoc explainability mechanisms such as visualization, mapping the policy to natural language, or explanation are used to convert a non-explainable model into a more explainable one.

Manuscript submitted to ACM

A large body of work aiming to achieve explainability in human-agent interaction does not explicitly refer to definitions that originate from machine learning. Explainability can be either performed by external mechanisms that are separable from the task execution (visualization) [START_REF] Perlmutter | Situated Language Understanding with Human-like and Visualization-Based Transparency[END_REF][START_REF] Sciutti | Understanding object weight from human and humanoid lifting actions[END_REF][START_REF] Zhou | Expressive Robot Motion Timing[END_REF] or intrinsically computed by the agent policy (e.g. query learning, communicative gestures) [START_REF] Chao | Transparent active learning for robots[END_REF][START_REF] Sheikholeslami | Prediction and Production of Human Reaching Trajectories for Human-Robot Interaction[END_REF].

The implementation of explainability can also be done at several levels. For example, via the situation-awarenessbased agent transparency (SAT) model, which is based on a Belief, Desire, Intention (BDI) architecture, considers three levels of explainability: Level 1-Basic Information (current status/plan); Level 2-Reasoning Information; Level 3-Outcome Projections [START_REF] Boyce | Effects of Agent Transparency on Operator Trust[END_REF].

Mapping agent policy (𝑎 T ) to natural language (𝑎 C ) is a methodology that is increasingly employed in AI to design explainable AI [START_REF] Barredo Arrieta | Explainable Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI[END_REF]. In HRI, a similar trend is observed [START_REF] Kumar Akash | Improving Human-Machine Collaboration Through Transparency-based Feedback -Part II: Control Design and Synthesis[END_REF][START_REF] Arnold | FactSheets: Increasing trust in AI services through supplier's declarations of conformity[END_REF][START_REF] Hayes | Improving Robot Controller Transparency Through Autonomous Policy Explanation[END_REF][START_REF] Wang | Trust calibration within a human-robot team: Comparing automatically generated explanations[END_REF]. The challenge will be to map agent policy to both verbal and non-verbal cues (see also section 3).

EVALUATION METHODS

Existing works assess the effects of explainability on a variety of measures including but not limited to, self-reported understanding of the agent [START_REF] Gong | Behavior Explanation as Intention Signaling in Human-Robot Teaming[END_REF], number of successful task completions [START_REF] Wang | Trust calibration within a human-robot team: Comparing automatically generated explanations[END_REF], number of false decisions [START_REF] Wang | Trust calibration within a human-robot team: Comparing automatically generated explanations[END_REF], task completion time [START_REF] Chao | Transparent active learning for robots[END_REF], number of irredeemable mistakes [START_REF] Arnold | FactSheets: Increasing trust in AI services through supplier's declarations of conformity[END_REF] or trust in automation [START_REF] Boyce | Effects of Agent Transparency on Operator Trust[END_REF]. During our review three major categories of measurements emerged:

• Trust measures how willing a user is to agree with a decision of a robot -based on the provided explanation -, how confident a user is about the embodied agent's internal workings (internal state), or if the user agrees with the plan provided by the robot (intent). It is measured using a self-report scale.

• Robustness measures the avoidance of failure during the interaction. Typically researchers want to determine whether the embodied agent's intent has been communicated correctly. It is often measured observationally, e.g., by counting the frequency of successful achievements of a goal. • Efficiency measures how quickly the task is completed. The common hypothesis behind using this measure is that the user can adapt better to a more explainable robot, and form a more efficient team. It is commonly measured by wall clock time, or number of steps until the goal.

Among these measures, trust received the most attention. While there is large variance in which scale is used (often scales are self-made), a common element in the studies is the use of self-report questionnaires.

Although the consensus is that the presence of explainability generally increases trust (see Table 4), how effective a particular social cue is in doing so has received much less attention. Comparisons that do exist often fail to find a significant difference between them [START_REF] Boyce | Effects of Agent Transparency on Operator Trust[END_REF][START_REF] Wang | Trust calibration within a human-robot team: Comparing automatically generated explanations[END_REF]. Similarly, due to the large range of mechanisms tested -and the even larger array of scenarios -, there is little work on how robust a specific mechanism performs across multiple scenarios.

Hence, while some form of explainability seems to be clearly better then none, which specific mechanism to choose for which specific situation remains an open question.

Less studied, but no less important, is the effect of explainability on the robustness of an interaction. Research on the interplay between explainability and robustness uses tasks where mistakes are possible, and measures how often these mistakes occur [START_REF] Boyce | Effects of Agent Transparency on Operator Trust[END_REF][START_REF] Perlmutter | Situated Language Understanding with Human-like and Visualization-Based Transparency[END_REF]. The core idea is that participants create better mental models of the robot when it is using explainability mechanisms. Better models will lead to better predictions of the embodied agent's future behaviour, allowing participants to anticipate low performance of the robot, and to avoid mistakes in task execution. However, experimental evidence on this hypothesis is not always congruent, with the majority of studies showing support for the idea, e.g., [START_REF] Perlmutter | Situated Language Understanding with Human-like and Visualization-Based Transparency[END_REF], and other studies finding no significant difference, e.g., [START_REF] Boyce | Effects of Agent Transparency on Operator Trust[END_REF]. As the majority does find a positive effect, we can conclude that explainability does help improve reliability, although not in all circumstances. A more detailed account of when it does or does not remains a subject for future experimental work.

Finally, efficiency is a metric that some researchers have considered while manipulating explainability. It has been operationalized by comparing wall clock time until task completion across conditions [START_REF] Chao | Transparent active learning for robots[END_REF], or time until human response [START_REF] Jessie | Effects of Agent Transparency on human-autonomy teaming effectiveness[END_REF]. Of the three types of measures, this type has received the least attention, and the findings are quite mixed. Approximately half of the analysed papers find that making embodied agents explainable makes the team more efficient, while the other half find no difference. However, a clear explanation for these conflicting findings remains a topic of future work.

Manuscript submitted to ACM Table 4 shows the core papers grouped by the evaluation methods discussed above and indicates whether the effect of explainability on it was positive, negative, or non-significant. One important note is that many papers introduce a measurement called accuracy; however, usage of this term differs between authors. For example, Chao et al. [START_REF] Chao | Transparent active learning for robots[END_REF] used accuracy to refer to the embodied agent's performance after a teaching interaction; hence it was being a measure of robustness, whereas Baraka and Veloso [START_REF] Baraka | Mobile Service Robot State Revealing Through Expressive Lights: Formalism, Design, and Evaluation[END_REF]'s accuracy referred to people's self-rated ability to predict the robot's move correctly, a measure of trust.

In summary, there is enough evidence that explainability offers a clear benefit to virtual embodied agents in building trust, with some support for physical embodied agents. Additionally, there is evidence that explainability can decrease the chance of an unsuccessful interaction (improve robustness). However, papers looking to improve the efficiency of the interaction find mixed results. A possible explanation for this could be that while explainability makes the interaction more robust, the time added for the embodied agents to display and for the human to digest the additional information nullifies the gain in efficiency.

In addition to the above analysis, this section identified the following open questions: (1) Is a particular explainability mechanism best suited for a specific type of embodied agent, a specific type of scenario, or both? (2) What are good objective measures with which we can measure trust in the context of explainability? (3) Why does explainability have a mixed impact on the efficiency of the interaction?

DISCUSSION

In the above sections we provided a focused view on four key aspects of the field: (1) definitions used, and the large diversity thereof, (2) which social cues and (3) algorithms are used to link explainability mechanisms to the embodied agent's state or intent, and (4) the measurements to assess explainability mechanisms. What is missing is a discussion of how these aspects relate to each other when looked at from a 10, 000 foot view, and a discussion of the limitations of our work.

It is almost self-explanatory that the scenario chosen to study a certain explainability mechanism depends on the author's research goal. As such, it is unsurprising that we can find a large diversity of tasks, starting from evaluation in pure simulation [START_REF] Hayes | Improving Robot Controller Transparency Through Autonomous Policy Explanation[END_REF], or discussions of hypothetical scenarios [START_REF] Kyung | Gracefully mitigating breakdowns in robotic services[END_REF][START_REF] Poulsen | Care Robot Transparency Isn't Enough for Trust[END_REF] all the way to joint furniture assembly [START_REF] Roncone | Transparent role assignment and task allocation in human robot collaboration[END_REF].

The most dominant strand of research has its origin in decision making, and mainly views the robot as a support for human decisions [START_REF] Kumar Akash | Improving Human-Machine Collaboration Through Transparency-based Feedback -Part I : Human Trust and Workload Model[END_REF][START_REF] Kumar Akash | Improving Human-Machine Collaboration Through Transparency-based Feedback -Part II: Control Design and Synthesis[END_REF][START_REF] Boyce | Effects of Agent Transparency on Operator Trust[END_REF][START_REF] Jessie | Effects of Agent Transparency on human-autonomy teaming effectiveness[END_REF][START_REF] Floyd | Incorporating transparency during Trust-Guided behavior adaptation[END_REF][START_REF] Floyd | Using explanations to provide transparency during trust-guided behavior adaptation 1[END_REF][START_REF] Wang | The Impact of POMDP-Generated Explanations on Trust and Performance in Human-Robot Teams[END_REF][START_REF] Wang | Trust calibration within a human-robot team: Comparing automatically generated explanations[END_REF][START_REF] Wang | Is it my looks? Or something i said? The impact of explanations, embodiment, and expectations on trust and performance in human-robot teams[END_REF]. In this line of research, explainability is mostly commonly defined via the SAT-model (i.e., Situation Awareness-Based Agent) [START_REF] Jessie | Situation awarenessbased agent transparency and human-autonomy teaming effectiveness[END_REF]. One of the key questions is how much a person will trust the embodied agent's suggestions, based on how detailed the given justification for the embodied agent's decision is. While these studies generally test a virtual agent shaped like a robot, the findings here can be easily generalized to the field of human-computer-interaction (HCI), due to their design. Hence, SAT model-based explanations can help foster trust not only in HRI, but also in the domain of expert systems and AI. Hence, this work partially overlaps with the domain of explainable AI (XAI).

The second strand of research sets itself apart by using humans as pure observers [7, 14-16, 30, 36, 44, 65, 67, 83].

Common scenarios focus on communicating the embodied agent's internal state or intent by having humans observe a physical robot [START_REF] Baraka | Enhancing human understanding of a mobile robot's state and actions using expressive Lights[END_REF][START_REF] Sciutti | Understanding object weight from human and humanoid lifting actions[END_REF][START_REF] Sheikholeslami | Prediction and Production of Human Reaching Trajectories for Human-Robot Interaction[END_REF] or video recordings/simulations of them [START_REF] Baraka | Enhancing human understanding of a mobile robot's state and actions using expressive Lights[END_REF][START_REF] Kwon | Expressing Robot Incapability[END_REF][START_REF] Zhou | Expressive Robot Motion Timing[END_REF]. Other researchers choose to show maps of plans generated by the robot and explanations thereof [START_REF] Chakraborti | Balancing explicability and explanations for human-aware planning[END_REF][START_REF] Chakraborti | Plan Explanations as Model Reconciliation: Moving Beyond Explanation as Soliloquy[END_REF][START_REF] Gong | Behavior Explanation as Intention Signaling in Human-Robot Teaming[END_REF][START_REF] Huang | Enabling Robots to Communicate Their Objectives[END_REF]; the researchers' aim here is to communicate the robot's intent. In all scenarios, the goal is typically to improve robustness, although other measures have been tested.

Particularly well done here is the work of Baraka et al. [START_REF] Baraka | Enhancing human understanding of a mobile robot's state and actions using expressive Lights[END_REF], who first describe how to enhance a robot with LED lights to display its internal state, use crowd sourcing to generate expressive patterns for the LEDs, and then validate Manuscript submitted to ACM the pattern's utility in both a virtual and a physical user study. This pattern of having participants -typically from Amazon Mechanical Turk (AMT) -generate expressive patterns in a first survey, and then validate them in a follow-up study was also employed by Sheikholeslami et al. [START_REF] Sheikholeslami | Prediction and Production of Human Reaching Trajectories for Human-Robot Interaction[END_REF] in a pick-and-place scenario. We think that this crowdsourcing approach deserves special attention, as it will likely lead to a larger diversity of candidate patterns compared to an individual researcher generating them. Considering the wide availability of online platforms, such as AMT and Polific, this is a tool that future researchers should leverage.

A third strand of research investigates explainability in interaction between a human and a robot [START_REF] Arnold | FactSheets: Increasing trust in AI services through supplier's declarations of conformity[END_REF][START_REF] Chao | Transparent active learning for robots[END_REF][START_REF] Lamb | The Paths We Pick Together: A Behavioral Dynamics Algorithm for an HRI Pick-and-Place Task[END_REF][START_REF] Lütkebohle | The curious robot -Structuring interactive robot learning[END_REF][START_REF] Perlmutter | Situated Language Understanding with Human-like and Visualization-Based Transparency[END_REF][START_REF] Roncone | Transparent role assignment and task allocation in human robot collaboration[END_REF][START_REF] Schaefer | Relinquishing manual control collaboration requires the capability to understand robot intention[END_REF] or a human and an AI system [START_REF] Legg | Visual analytics for collaborative human-machine confidence in human-centric active learning tasks[END_REF]. Studies in this strand investigate the impact of different explainability mechanisms on various interaction scenarios and whether they are still useful when the human-robot dyad is given a concrete task. This is important, because users can focus their full attention on the explainable behaviour in the observer setting; in interaction scenarios, on the other hand, they have to divide their attention. Research in this strand is more heterogeneous, likely due to the increased design complexity of an interaction scenario. At the same time, the amount of research done, i.e., the number of papers identified, is less than the research done following the observational design above; probably because of the the above mentioned added complexity. Nevertheless, we argue that more work on this strand is needed, as we consider testing explainability mechanisms in an interaction as the gold standard for determining their utility and effectiveness.

Finally, some researchers examined participants' responses to hypothetical scenarios [START_REF] Kyung | Gracefully mitigating breakdowns in robotic services[END_REF][START_REF] Poulsen | Care Robot Transparency Isn't Enough for Trust[END_REF]. The procedure in these studies is to first describe a scenario to participants in which a robot uses an explainability mechanism during an interaction with a human. Then, participants are asked to give their opinion about this interaction, which is used to determine the utility of the mechanism. This method can be very useful during the early design stages of an interaction, and can help find potential flaws in the design before spending much time implementing them on a robot. At the same time, it may be a less optimal choice for the final evaluation, especially when compared to the other methods presented above.

CHALLENGES IN EXPLAINABILITY RESEARCH

Shifting the focus to how results are reported in research papers on explainability, we would like to address two challenges we faced while aggregating the data for this review.

The first challenge is the large diversity and inconsistency of language used in the field. Transparency, explainability, expressivity, understandability, predictability and communicability are just a few examples of words used to describe explainability mechanisms. Authors frequently introduce their own terminology when addressing the problem of explainability. While this might allow for a very nuanced differentiation between works, it becomes challenging to properly index all the work done, not only because different authors addressing the same idea may use different terminology but also especially because different authors addressing different ideas end up using the same terminology.

Other reviews on the topic have pointed this out as well [START_REF] Chakraborti | Explicability? Legibility? Predictability? Transparency? Privacy? Security? The Emerging Landscape of Interpretable Agent Behavior[END_REF][START_REF] Rosenfeld | Explainability in human-agent systems[END_REF], and it became a challenge in our review, as we cannot ensure completeness of a keyword search based approach. The most likely cause of this is because the field is seeing rapid growth, and precise terminology is still developing.

This work tries to address this first challenge by showing how different terms are used to identify similar concepts and providing a definition that aims to be comprehensive for the surveyed papers.

The second challenge was that many authors only define the explainability mechanism they investigate implicitly. We often had to refer to the concrete experimental design to infer which mechanism was studied. While all the important Manuscript submitted to ACM information is still present in each paper, we think that explicitly stating the explainability mechanism under study can help discourse regarding explainability become much more concrete.

In extension, some authors have implemented explainability mechanisms on robotic systems that are capable of adapting their behaviour or performing some kind of learning. In many cases, these learning algorithms were unique implementations, or variations of standard algorithms, e.g., reinforcement learning, which make them very interesting.

How to best incorporate an explainability mechanism into such a framework is still an open question. Unfortunately, we found that the details of the method are often underreported and that we could not extract enough data on what has been done so far. We understand that this aspect is often not the core contribution of a paper and that space is a constraint. Nevertheless, we would like to encourage future contributions to put more emphasis on how explainability mechanisms are integrated into existing learning frameworks. Technical contributions such as this could prove very valuable for defining a standardized approach to achieve explainability using embodied social agents.

OPEN QUESTIONS

While performing the review, we identified a set of open questions. For convenience we enumerate them here:

(1) What are good models to predict/track human expectations/beliefs about the embodied agent's goals and actions?

(2) What are efficient learning mechanisms to include the human in the loop when building explainability into embodied agents?

(3) How does the environment and embodiment influence the choice of social cues used for explainability?

(4) What are good objective measures by which we can measure trust in the context of explainability?

(5) Why does explainability not have a strictly positive impact on the efficiency of the interaction?

CONCLUSION

Above we conducted a systematic review of the literature on explainable embodied agents. We used keyword based search to identify 32 relevant contributions and provided a detailed analysis of them.

First, we analysed the definitions of explainability used in each piece, highlighting the heterogeneity of existing definitions and stating our definition. In the process, we identified four main motivations that lead researchers to study explainability: (1) interactive robot/machine teaching, (2) human trust, (3) teamwork, and (4) ethical decision making.

We then detailed why explainability is important for each, and identified the motivations and definition behind each of the surveyed papers. Second, we looked at social cues used as vehicles to deliver the explainability mechanism.

We identified the categories of (1) speech, (2) text, (3) movement, and (4) imagery and described how each provides explainable behaviours. Third, we took stock of the algorithms used to select which part of the interaction should be made explainable. We found that only a small fraction of the work addresses this algorithmic part and most often not in sufficient detail for an in-depth analysis. We hence extended the literature in this section, to draw from other related work to provide a better overview. Fourth, we asked how the impact of explainability is measured in the identified literature. We found that most of the literature looks at three aspects: (1) trust, (2) robustness, and (3) efficiency, of which trust and robustness have received the most attention. We looked at how these aspects are measured and formulated open questions for future work.

Looking at the big picture, we identified three strands of research. The first one has partial overlap with XAI and tries to implement decision support systems on embodied agents. The second treats humans as observers and investigates what kind of explainability mechanisms can be used to make humans understand the embodied agent's inner workings.

The third and final strand investigates how explainability mechanisms can be smoothly integrated into an interaction context (compared to treating humans as pure observers).

Finally, we provided a list of open questions and gaps in the literature that we identified during our analysis in the hope that further investigation will address this fascinating new domain of research.

Fig. 1 .

 1 Fig.1. Percentage increase in the number of publications relative to the first year (2009) in the reviewed area. For comparison, the graph also shows the percentage increase in the number of publications overall measured by the number of publications indexed in Scopus that use the keyword "Human-Robot Interaction".

Fig. 2 .

 2 Fig. 2. Example A -Kwon et al. [44], Example B -inspired by Wang et al. [75]. Agent and Explainability Mechanisms. Intrinsic explainability refers to models that are explainable by design. Post-hoc (external) explainability refers to explainability mechanisms that are applied after the decision or execution of actions. Explainability could be either performed by external mechanisms that are separable from the task execution (visualization) or intrinsically computed by the agent policy (e.g. query learning, communicative gestures). Permission to reuse figure A was kindly provided by the authors.

  Aha[START_REF] Floyd | Incorporating transparency during Trust-Guided behavior adaptation[END_REF][START_REF] Floyd | Using explanations to provide transparency during trust-guided behavior adaptation 1[END_REF] Layer that allows the agent to explain why it adapted its behaviours Trust, Adaptation 2017 Hayes and Shah[START_REF] Hayes | Improving Robot Controller Transparency Through Autonomous Policy Explanation[END_REF],[START_REF] Arnold | FactSheets: Increasing trust in AI services through supplier's declarations of conformity[END_REF] Autonomosly synthesize policy descriptions and respond to both general and target queries by human collaborators Human-Robot Collaboration, Control, Debug 2017 Roncone et al. [62] Transfer information to the human partner about its own [robot] internal state and intents Trust, Proficiency, Confidence, Uncertainty, Introspection 2017 Chakraborti et al. [16], [14] Robot's attempt to move the human's model to be in conformance with its own. al. [18, 19],[1, 2] 2018 Descriptive quality of an interface pertaining to its abilities to afford an operator's comprehension about an intelligent agent's intent, perfor-et al. [49] Establish a two-way collaborative dialogue on data attributions between human and machine and ex-Billard [39] Ensure the robot's behaviours complies with human intention, adapting generated motions (i.e., the desired velocity) to those intended by the human user Human-Robot Collaboration Manuscript submitted to ACM

Table 1 .

 1 Inclusion Criteria and Search String

	Topic	Description	Search Term
	Human Involvement Exclude papers without human involve-	("human-robot" OR "child-robot" OR
		ment, e.g., position papers or agent-agent	"human-machine")
		interaction
	Explainability		(transparen* OR interpretabl* OR explain-
			abl*)
	Explainability II		(obser* OR legib* OR visualiz* OR (com-
			mun* AND "non-verbal"))
	Autonomy	Exclude papers that are not using an au-	(learn* OR plan* OR reason* OR navigat*
		tonomous agent	OR adapt* OR personalis* OR decision-
			making OR autonomous)
	Social Cues	Exclude papers that do not have a social	(social OR interact* OR collab* OR shared
		interaction between human and agent	OR teamwork OR (model* AND (mental
			OR mutual)))
	Agent	Exclude papers that do not use an agent	(agent* OR robot* OR machine* OR sys-
			tem*)
	Recency	Only consider the last 10 years	( LIMIT-TO (PUBYEAR, 2019) OR ... OR
			LIMIT-TO (PUBYEAR, 2009) )
	Subject Area	Only consider papers from computer sci-	(LIMIT-TO (SUBJAREA, "COMP") OR
		ence, engineering, math, psychology, so-	LIMIT-TO (SUBJAREA, "ENGI") OR
		cial sciences, or neuroscience	LIMIT-TO (SUBJAREA, "MATH") OR
			LIMIT-TO (SUBJAREA, "SOCI") OR
			LIMIT-TO (SUBJAREA, "PSYC") OR
			LIMIT-TO (SUBJAREA, "NEUR"))
		TITLE-ABS-KEY ("human-robot" OR "child-robot" OR "human-machine") AND
		(transparen* OR interpretabl* OR explainabl*) AND (obser* OR legib* OR visualiz*
		OR (commun* AND "non-verbal")) AND (learn* OR plan* OR reason* OR navigat*
		OR adapt* OR personalis* OR decision-making OR autonomous) AND (social OR
		interact* OR collab* OR shared OR teamwork OR (model* AND (mental OR mutual)))
		AND (agent* OR robot* OR machine* OR system*) AND (LIMIT-TO (PUBYEAR,
	Full Search String	2019) OR LIMIT-TO (PUBYEAR, 2018) OR LIMIT-TO (PUBYEAR, 2017) OR LIMIT-TO
		(PUBYEAR, 2016) OR LIMIT-TO (PUBYEAR, 2015) OR LIMIT-TO (PUBYEAR, 2014)
		OR LIMIT-TO (PUBYEAR, 2013) OR LIMIT-TO (PUBYEAR, 2012) OR LIMIT-TO
		(PUBYEAR, 2011) OR LIMIT-TO (PUBYEAR, 2010) OR LIMIT-TO (PUBYEAR, 2009))

Table 2 .

 2 Papers by Terminology Used in the Title

	Category	Paper
	Transparency	[1, 2, 10, 17, 18, 26, 27, 60-62] [28, 34]

Table 3 .

 3 Papers on Explainability Ordered by Social Cues

	Category	Paper
	Speech	[6, 26, 52, 60, 62]
	Text	[1, 2, 14, 18, 27, 28, 30, 34, 61, 75-77]
	Movement	[12, 14, 17, 36, 44, 45, 52, 60, 65, 83]
	Imagery	[10, 12, 49, 60, 62, 64]
	Other/Unspecified [8, 60]/ [16, 32, 39, 48]*
	around teamwork ("anticipate robot's behaviour"

Table 4 .

 4 Papers on Explainability by Measure

	Type	Outcome	Papers
	Robustness Positive	[7, 18, 32, 36, 44, 45, 60, 65, 76, 77]
	Robustness Negative
	Robustness Non-significant	[17]
	Robustness No statistical test [14, 49, 67]
	Trust	Positive	[7, 10, 17, 18, 47, 64, 75, 77, 83]
	Trust	Negative
	Trust	Non-significant	[2]
	Trust	No statistical test [1, 6, 16, 27, 28, 30, 44, 61]
	Efficiency	Positive	[2, 47, 76]
	Efficiency	Negative
	Efficiency	Non-significant	[17, 18, 60, 75]
	Efficiency	No statistical test [16, 62]
	other	any	[15, 34, 39, 52, 80]

Table 5 .

 5 Identified Categories by Paper

	Learning Paradigm
	Measurement
	Social Cues	n o n e M o v e m e
	Definition	t r a n s p a r e n e x p c l a y i n a b i e x p l i t r e y s s i v o t i t h e y r
	CitationKey

https://www.scopus.com/

A spreadsheet detailing the raw search results can be found in the supplementary files.Manuscript submitted to ACM

A spreadsheet detailing the reason for excluding a paper can be found in the supplementary files.Manuscript submitted to ACM

Manuscript submitted to ACM

https://www.merriam-webster.com Manuscript submitted to ACM

. Boyce et al. [10] X . . . . . . . X . X . . . . Brown and Laurier [12] . . . . X X . . X . . . . X . Chakraborti et al. [16] . X . . . . . . . X X . X . . Chao et al. [17] X . . . . X . . . . X X X . X Chen et al. [18] X . . . . . X . . . X X X . . Fischer et al. [26] X . . . . . X . . X . . . .

. . X X . X . . . . . X X Perlmutter et al. [60] X . . . . X . X X X . X X . X Poulsen et al. [61] X . . . . . X . . . X . . . . Roncone et al. [62] X . . . . . . X X . . . X . . Schaefer et al. [64] . . . X . . . . X . X . . . . Manuscript submitted to ACM

ACKNOWLEDGMENT

This work received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 765955 (ANIMATAS Innovative Training Network).


Maud Lekieffre
email: maud.lekieffre@chu-lyon.fr

Laure Gallay

Océane Landon-Cardinal

Arnaud Hot

Methods for scoping review

come

Methods

Protocol

This scoping review was conducted using the Joanna Briggs Institute framework (1) and the present report respects the PRISMA-ScR checklist [START_REF] Tricco | PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation[END_REF]. The review included the following six key phases: 1) identifying the research question, 2) identifying relevant studies, 3) study selection, 4) charting the data, 5) collating, summarising, and reporting the results.

Research question

This scoping review was guided by the following question: What is the spectrum of myositis associated with RA?

3 Screening for relevant studies: eligibility criteria As ACR/EULAR criteria for RA diagnosis include a wide range of polyarthritis (seronegative and seropositive, erosive and non-erosive), we chose to enlarge research to all polyarthritis unless diagnosed as other inflammatory rheumatism (psoriatic rheumatism, spondylarthropathy, Behçet's disease, Still's disease, sarcoidosis…); the objective was to limit misdiagnosis bias. Consequently, reports were included if they considered the association of myositis or IIM polyarthritis or RA. For practical reasons, reports published in a language other than English or French were excluded. Duplicates were manually excluded.

If a case was reported in more than report (for example in a literature review reporting previously published case reports), only the original report was included. Because RA affects adults, we focused on adult population; reports on paediatric populations (i.e. considering juvenile DM) were therefore excluded. As it was outside the scope of the review, reports about non-IIM myositis (i.e. infectious myositis, muscle dystrophies), paraneoplastic syndromes (including citations related to immune check point inhibitors), defined inflammatory rheumatic disease other than RA (psoriatic arthritis, sarcoidosis, spondylarthropathy, Still's disease, and Behçet's disease), and reports that did not present human data were excluded. When we could not distinguish polyarthritis from polyarthralgia, studies were excluded. In addition, because the present review concentrates on the diagnoses related to the association of myositis and RA or RA-like polyarthritis, reports on pathogenesis, fundamental research, and/or therapeutics were excluded.

Two electronic databases (PubMed ® and Web of Science ® ) were searched on 1 May 2021; the search query was: (myositis OR 'inflammatory idiopathic myopathies') AND (polyarthritis OR 'rheumatoid arthritis'), without limitation on date, language, subject, or type. The reference lists of 17 pre-selected relevant review articles were manually searched to identify any further studies not yet captured (listed in supplementary data).

Citation management

After relevance screening (based on inclusion and exclusion criteria) on titles and abstract, records were integrated into Zotero ® (version 5.0.96.2). Full-text reports of records deemed relevant after title and abstract screening were procured; those that could not be obtained through institutional subscriptions available to the primary author were sought through interlibrary loan.

Data charting process

Data from included papers were charted by one reviewer (ML). Extracted data included: first author, year of publication, publication type, main topic of the study, and data relevant to the specific objectives of this scoping review, i.e. 1) patient characteristics 2) RA characteristics, 3) myositis characteristics, 4) associated extra-muscular or extra-articular involvement, 5) autoimmunity biological markers [antinuclear antibodies (ANA), rheumatoid factor (RF), anti-citrullinated proteins antibodies (ACPA), anti-synthetase antibodies (ARS), etc.], and 6) final diagnosis.

Data synthesis and report

A list of suitable diagnoses for the association of myositis and RA or RA-like polyarthritis was created. When myositis occurred in a patient recently treated by anti-TNF therapy, it was classified as drug-induced myositis irrespectively of the conclusion of the respective authors.

Descriptive statistics were used to summarise the results.


Atharva Deshpande

Tushar Dongare

Pratik Chaudhari

Shubham Borse

V2G and G2V plug-in and wireless charging integration in Vehicle Parking

Keywords: EV, charging dock, risk, smart detection, V2G, G2V, wireless charging

Introduction

Vehicles are one of the most parts of today's human life. even though, it has become a basic need for humans after food clothing, and shelter. automobiles are in various forms like two-wheelers, four-wheelers, multi-wheels bus, trucks, etc. Currently, the vehicles are work with the help of fuels like petrol, diesel, etc. and the fuels are getting depleted rapidly. Also, these fossil fuels take time for their formation.

These all factors lead to an ecological imbal-ance of the earth. Recently a rapid advancement has taken place in the automobile sector for the development of electric vehicles, plug-in hybrid vehicles, etc. They need batteries or batteryfuel combinations to operate. The charging of these vehicles is done with the help of charging outlets from the power grid. This electricity provided to the charging dock largely comes from the conventional sources of coal and other fossil fuels. They are also a contributor to the ecological imbalance due to limited availability and pollution. Hence, solutions need to find an alternative source of power. Renewable energy sources can be used in order to curb this problem. Apart from these vehicles also can be used as electricity-generating utilities. In this paper, we are presenting a working model representation of Grid-tied smart parking. This type of parking will help generate electricity through various renewable energy sources like solar, wind, piezo, etc. Also, the vehicle's batteries can act as an energy storage option for the load. This is known as Vehicle to Grid (V2G) or Grid to Vehicle (G2V). Furthermore, energy conservation measures are taken through human detection sensor installed at the parking lot. This sensor will also serve the purpose of security against the entry of humans into the parking lot. Also, the Radiofrequency tag system will serve against illegal entrants on the campus while opening the gate. The driverless car with a wireless charging facility is also worked out in this model project. The conventional charging system of wired connectors is also shown for easy comparison.

2 Vehicle to grid (V2G)

Vehicle to grid (V2G) technology can be defined as a system in which there is a capability to control, bi-directional flow of electric energy be-tween a vehicle and the electrical grid. The integration of electric vehicles into the power grid is called the vehicle-to-grid system. Any electricdrive vehicle has within them the energy source and power electronics combining making it capable to drive the power requirements of homes and offices. It has been calculated that 92% of the total vehicles remain parked even during peak hours. When a vehicle is not being operated, the onboard battery is connected to a nearby electrical grid via appropriate communication devices. The idea is to use the power from idle vehicles to provide load-shedding and peak shaving and many other functions. The vehicle batteries can be fully charged during low-demand hours and the flow can be reversed at any time according to the requirements. This can be fulfilled by utilizing the concept of 'smart grid' which is an electricity network capable of processing the information, manages the electricity flow to fulfill the end-users varying power demand, and is able to provide communication between generation sources and end-users. This concept works to balance the 'off-peak' and 'peak' demand. The Vehicle can get charged during off-peak hours and can sell it back to the grid during peak hours. Electrical Vehicles and PHEVs PHEVs can be operated as a load while charging, a distributed storage, including services like reactive regulation, motor starting, or a standalone energy source such as peak sharing. Although care has to be taken while discharging the onboard batteries as excessive discharge might affect the battery life and its expectancy. The PHEV fleet is large enough and if a particular portion of the vehicle's stored energy could be tapped while parked, it would provide the power grid with large amounts of energy at a given time of the day. This section concentrates on the benefits of using PHEVs as a distributed storage.

B. Peak sharing and other electrical benefits

The stored battery energy can be used to provide power to help balance loads by charging at night when the demand is low (valley filling) and providing power to the grid when the demand is high (peak sharing). Peak sharing also provides additional advantages such as the reduction of a variety of unwanted factors like line losses, delay transmission, transmission congestion, etc. It also helps in reducing the stressed operation of a power system, thus adding to its longevity in terms of lasting. This leads to avoiding heavy investments in installing peaking power plants. Power regulation authorities have a good ability to predicting the peak loads (mostly during summers due to the load of the AC's).

Using hybrid vehicles as a distributed storage acts as a robust alternative to expensive and capital-demanding 'peaking plant' generators. Generally, power-seeking bodies purchase electrical energy through long-term contracts with generation companies or from spot electricity markets in the short run. Peak load periods, see the highest electricity prices. Peak shaving applications of the PHEV fleet reduces the cost of electricity during peak periods. The money saved in the case can be used to further propel the utilities of PHEVs by investing in research facilities and expanding their horizon. The average benefits from the V2G participation of an EV are estimated to range from $392 to $561 annually per vehicle. Thus PHEVs offer the power system with a flexible, controllable load and could provide load leveling during off-peak periods.

Components and Construction

A. Copper coil

The Induction coil is an electrical device for producing an intermittent source of high voltage. An induction coil consists of a central cylindrical core of soft iron on which are wound two insulated coils: an inner or primary coil, having relatively few turns of copper wire, and a surrounding secondary coil, having a large number of turns of thinner copper wire. An interrupter is used for making and breaking the current in the primary coil automatically. This current magnetizes the iron core and produces a large magnetic field throughout the induction coil. 4 Designing an Inverter circuit and Buck-Boost converter

A. Inverter

In this project, we have developed a 60 W inverter circuit, which can be further increased to 100 W using a higher rating transformer and more robust heat management system. The inverter is a multivibrator IC-based MOSFET powered output for 50 Hz frequency through a battery-powered Dc supply of 12 volts. Since the Indian national grid recommends 50 Hz as the standard frequency for synchronization purposes, we have constructed the inverter to synchronize the T-box power generation with the national grid, to contribute to national power generation and meet the load demand. The voltage rating we will be getting out of this inverter is 230v AC, 50 Hz. Following is the circuit diagram for the inverter circuit.

A. Basic Square wave generator 6V DC input to the chopper. DC-DC conversion 12V DC output from the chopper circuit is a constant DC voltage and is then input to the inverter circuit After the RC modulation for pulse width, the CD4047 multivibrator IC will Both this output signal from Q and Q' is then amplified using a MOSFET circuit and is then connected to the center-tapped transformer at each end. The center connection of the transformer is then connected directly to the ground connection of the DC input. This square waveform is alternating in nature, ideal only for simple applications, but it will involve too much distortion noise content. The Frequency OUPUT will not be constant and can cause damage to the connected AC appliance.

So a capacitor is added to make the circuit smooth. The step-up transformer would then convert the 12 V AC voltage to 230 V AC. One more capacitor would filter the ripple content generated due to conversion and the circuit would give full AC pure 50 Hz waveform. We use 12-battery as a power supply. The output will be a square wave, which has three forms are: I would Get The 50% duty Cycle Pin 13 is the basic frequency obtained by the VR1 and C1 across pins 1,2 & 3. Pin 10 (Q) and Pin 11(Q) shows the half frequency of the original frequency at pin 13. Both frequencies at pins 10 and pin 11 are inverted to each other phases. The inverted Outputs are 180 degrees out of phase. Three outputs have the 50% duty cycle. See the waveform signal of three outputs. We cannot adjust the duty cycle of this circuit. It always is a duty cycle of 50% that symmetry exists. Then, we can set the frequency output by adjusting VR1 and charging C1. The value of VR1 is used in a range of 1K to 1M. To Adjust to the desired frequency. And, the C1 will determine the frequency. The value is used to determine the various range. You can see this from the table below. Note: You should pick a capacitor that has a low leakage. To reduce waveform distortion and mistake frequency. The value of C1 in the circuit as Figure 1 Thus, if we use a capacitor of 0.01 uF and set a target of 50 Hz frequency, we need to adjust our variable resistor to around 806 ohms.

A. Stepwise conversion from 12 v DC to 230 v AC 6V DC input to the chopper. DC-DC conversion 12V DC output from the chopper circuit is a constant DC voltage and is then input to the inverter circuit After the RC modulation for pulse width, the CD4047 multivibrator IC will give an output at its terminal.

Both this output signal from Q and Q' is then amplified using a MOSFET circuit and is then connected to the center-tapped transformer at each end. The center connection of the transformer is then connected directly to the ground connection of the DC input. This square waveform is alternating in nature, ideal only for simple applications, but it will involve too much distortion noise content. The Frequency OUPUT will not be constant and can cause damage to the connected AC appliance.

So a capacitor is added to make the circuit smooth. The step-up transformer would then convert the 12 V AC voltage to 230 V AC. One more capacitor would filter the ripple content generated due to conversion and the circuit would give full AC pure 50 Hz waveform.

B. Buck-Boost converter In this project, a representation of a parking system for 3 vehicles is shown. The first two vehicles would be conventional electric vehicles, which have a plug-in charging system. These vehicles would have a certain voltage across the batteries. We have considered a reference voltage of around 6 volts. So, depending on the usage the vehicles could be either over the 6v range or under it. A boost converter and buck converter would be employed for that purpose respectively. The indicator would be placed at the dock to indicate the activated mode between V2G or G2V. A bidirectional relay would be employed for that purpose. The third vehicle would be a working representation for G2V mode, but with a wireless charging technique. Here, the user would not need to get outside of the vehicle for charging and plug-in purpose. The vehicle would just

Conclusions

In this paper, research has been done on V2G and G2V plug-in and wireless charging integration in vehicle parking. We have successfully researched and developed a working prototype model for the integration of Vehicle Parking space into the National Electricity grid with the help of the V2G and G2V concept along with the future wireless charging concept for EV. The following points were noted while developing the prototype-A.EV is not widely adopted due to poor charging infrastructure.

B.The conventional charging system is not reliable C.The service providers for EV charging face expensive land costs and initial investment in comparison to Return on Investment.

However, with our project following points were observed -A.EV parking space will no longer be a burden due to V2G technology.

B.The ROI period would be drastically reduced due to the monetization of charging in parking space.

C.Wireless charging technology ensured less wear and tear of the charging equipment.

D.Wireless charging also ensured the safety of passengers from external weather and privacy concerns.

Figure 1 :

 1 Figure 1: V2G system.A. Advantages and Applications of V2G.Electrical Vehicles and PHEVs PHEVs can be operated as a load while charging, a distributed storage, including services like reactive regulation, motor starting, or a standalone energy source such as peak sharing. Although care has to be taken while discharging the onboard batteries as excessive discharge might affect the battery life and its expectancy. The PHEV fleet is large enough and if a particular portion of the vehicle's stored energy could be tapped while parked, it would provide the power grid with large amounts of energy at a given time of the day. This section concentrates on the benefits of using PHEVs as a distributed storage.B. Peak sharing and other electrical benefitsThe stored battery energy can be used to provide power to help balance loads by charging at

  B. Atmega-328 Microcontroller The high-performance Microchip pi-coPower® 8-bit AVR® RISC-based microcontroller combines 32 KB ISP Flash memory with read-while-write capabilities, 1024B EEPROM, 2 KB SRAM, 23 general purpose I/O lines, 32 general purpose working registers, three flexible timer/counters with compare modes, internal

Figure 2 :

 2 Figure 2: Construction

Figure 3 :

 3 Figure 3: Copper Coil.

Figure 4 :Figure 5 :

 45 Figure 4: Square wave generator

  to determine the frequency use. 1uF = 1-10Hz 0.1uF = 10Hz-1kHz 0.01uF = 100Hz-10kHz And it use VR1 potentiometer is 250K. Another simple practical approach for frequency generation Frequncy = 1 / time 50 = 1/ R*C 50 = 1/ (R * C) 50 = 1/ (R * 0.01 * 10 -6) 50 * R * 10-8 = 1 R = 1/ (0.0012394) R = 806 ohms.

Figure 6 :

 6 Figure 6: Inverter circuit

Figure 7 :

 7 Figure 7: Buck-Boost converter

Figure 8 :

 8 Figure 8: Buck-Boost converter

Figure 9 :

 9 Figure 9: Grid to Vehicle

Figure 10 :

 10 Figure 10: Block Diagram

Figure 11 :

 11 Figure 11: Wireless Power Transfer Circuit

Table 1 :

 1 Vehicle-Grid B. Wireless Charging

Table 2 :

 2 Wireless power transfer

Acknowledgements

With profound and respect, we express our sincere appreciation to our mentor, Mr.Pritesh Wani sir whose systematic method of functioning has been our inspiration and we would also like to thank him for his valuable suggestions and guidance given in carrying out this work which will be very helpful for us in future.