Leolani: A Reference Machine with a Theory of Mind for Social Communication

Vossen, Piek; Báez, Selene; Bajčetić, Lenka; Kraaijeveld, Bram

doi:10.1007/978-3-030-00794-2_2

Cited by 7 publications

(6 citation statements)

References 21 publications

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“…Based on BDI, the actions of rational agents are a consequence of their beliefs, desires, and intentions (without imposing specific conditions, e.g., rationality) [25]. ToM and BDI can complement one another [29,27]: In fact, the concept of intentions introduced in BDI can be included in a dynamic, mathematical framework of ToM, in order to account for the interlinks within a sequence of actions of the agent, for achieving a particular intention [26].…”

Section: Related Work and Open Challengesmentioning

confidence: 99%

Dynamic Mathematical Models of Theory of Mind for Socially Assistive Robots

Patrício,

Jamshidnejad

2023

IEEE Access

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Interactive machines should establish and maintain meaningful social interactions with humans. Thus, they need to understand and predict the mental states and actions of humans. Based on Theory of Mind (ToM), in order to understand and interact with each other, humans develop cognitive models of one another. Our main goal is to provide a mathematical framework based on ToM to improve the understanding of interactive machines regarding the perception, cognition, and decision-making of humans. Most stateof-the-art models of behavioral theories based on machine learning are focused on input-output black-box representations. Thus, they lack transparency and generalizability, and exhaustive training procedures are needed to personalize them for various humans. Moreover, these models lack dynamics, i.e., they do not mathematically describe the evolution of the mental states and actions of humans in time. Following a systems-and-control-theoretic point-of-view, we represent for the first time the perception, cognition, and decision-making of humans via a dynamic, mathematical framework by introducing a novel formalization and an extension to Fuzzy Cognitive Maps (FCMs). The resulting models are given in a general state-space representation, which can be used by interactive machines within known model-based state estimation and control methods. In a case study, the resulting models were identified and validated for 21 participants, in scenarios where predicting the intentions and behavior of the participants required understanding the dynamics of their mental procedures. The results of these experiments show that our model is capable of incorporating the dynamics to estimate the intentions and predict the behavior of the participants, with an accuracy of, respectively, 81.55% and 66.06%. Moreover, we compared our model with a state-of-the-art formalization of human cognition, which was made dynamic using our introduced FCM framework. Our model, which in addition to the elements of the state-of-the-art model included emotions, personality traits, and biases (thus providing a more transparent insight about the mental procedures of the participants) showed 6.25% and 2.45% more accuracy in, respectively, estimating the intentions and predicting the behavior of the participants.INDEX TERMS Dynamic mathematical models, long-term interactions of rational agents, state-space models of cognition, theory of mind.

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Section: Related Work and Open Challengesmentioning

confidence: 99%

Dynamic Mathematical Models of Theory of Mind for Socially Assistive Robots

Patrício,

Jamshidnejad

2023

IEEE Access

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“…Vossen et al [24] developed a system presenting a robot's mind based on experiences and what other people told the robot. Specifically, they built a memory function to store and retrieve the knowledge obtained during human-robot interactions.…”

Section: Using Scripting Techniquesmentioning

confidence: 99%

“…For content to be presented by experience-based dialogue, we treated the robot's experience of interaction with a person. Telling one's experience is an effective way to evoke the listener's empathy because it implies that the robot has social relationships, and such relationships always exist for listeners [24,26,27]. Thus, experience-based dialogue should affect a person's information cognition.…”

Section: Experience-based Dialoguementioning

confidence: 99%

“…Fletcher et al [24] reported that listening to stories of the speaker can induce brain activity in the listener, which helps the speaker connect with the audience and make it much more likely that they can imagine the speaker's point of view. Therefore, the experience message is an effective method in interpersonal communication to make the listener feel the message content and engage the listener more in the conversation.…”

Section: Knowledge Message = Knowledgementioning

confidence: 99%

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Sharing Experiences to Help a Robot Present Its Mind and Sociability

Yoshikawa

Iio

et al. 2020

Int J of Soc Robotics

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Many social robots have emerged in public places to serve people. For these services, the robots are assumed to be able to present internal aspects (i.e., mind, sociability) to engage and interact with people over the long term. In this paper, we propose a novel dialogue structure called experience-based dialogue to help a robot present and maintain a good interaction over the long term. This dialogue structure contains a piece of knowledge and a story about how the robot gained this knowledge, which are used to compose the robot's experience-related utterances for sharing experiences of interacting with previous users other than just the current user and help it present its internal aspects. We conducted an experiment to test the effects of our proposed dialogue structure and measure them with some published subjective scales. The results showed that experience-based dialogue can help a robot obtain better evaluations in terms of perceived intelligence, sociability, mind, anthropomorphism, animacy, likability, level of acceptance, and positive user reaction.

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“…In the context of conversational agents, these are usually experiences shared by the user throughout previous interactions with the agent (also referred to as shared memories). For example, [18] and [19] extract and store the contents of shared memories in knowledge graphs, specifically, when the context triggers a similar pattern in the conversation (actor, place and time triples in [18] and the H5W "who", "what", "where", "when", "why" and "how" structure in [19]). Although showing promising results, the models in these studies don't address the user experience with the affective aspects in the conversation.…”

Section: Introductionmentioning

confidence: 99%