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Windridge, D., Svensson, H. & Thill, S. (2020). On the utility of dreaming: A general model for how learning in artificial agents can benefit from data hallucination. Adaptive Behavior, Article ID UNSP 1059712319896489.
Open this publication in new window or tab >>On the utility of dreaming: A general model for how learning in artificial agents can benefit from data hallucination
2020 (English)In: Adaptive Behavior, ISSN 1059-7123, E-ISSN 1741-2633, article id UNSP 1059712319896489Article in journal (Refereed) Epub ahead of print
Abstract [en]

We consider the benefits of dream mechanisms - that is, the ability to simulate new experiences based on past ones - in a machine learning context. Specifically, we are interested in learning for artificial agents that act in the world, and operationalize "dreaming" as a mechanism by which such an agent can use its own model of the learning environment to generate new hypotheses and training data. We first show that it is not necessarily a given that such a data-hallucination process is useful, since it can easily lead to a training set dominated by spurious imagined data until an ill-defined convergence point is reached. We then analyse a notably successful implementation of a machine learning-based dreaming mechanism by Ha and Schmidhuber (Ha, D., & Schmidhuber, J. (2018). World models. arXiv e-prints, arXiv:1803.10122). On that basis, we then develop a general framework by which an agent can generate simulated data to learn from in a manner that is beneficial to the agent. This, we argue, then forms a general method for an operationalized dream-like mechanism. We finish by demonstrating the general conditions under which such mechanisms can be useful in machine learning, wherein the implicit simulator inference and extrapolation involved in dreaming act without reinforcing inference error even when inference is incomplete.

Place, publisher, year, edition, pages
Sage Publications, 2020
Keywords
Artificial dream mechanisms, data simulation, machine learning, reinforcement learning
National Category
Computer Sciences
Research subject
Interaction Lab (ILAB)
Identifiers
urn:nbn:se:his:diva-18160 (URN)10.1177/1059712319896489 (DOI)000506780000001 ()2-s2.0-85077601986 (Scopus ID)
Available from: 2020-01-23 Created: 2020-01-23 Last updated: 2020-01-27Bibliographically approved
Bartlett, M. E., Costescu, C., Baxter, P. & Thill, S. (2020). Requirements for Robotic Interpretation of Social Signals “in the Wild”: Insights from Diagnostic Criteria of Autism Spectrum Disorder. Information, 11(2), Article ID 81.
Open this publication in new window or tab >>Requirements for Robotic Interpretation of Social Signals “in the Wild”: Insights from Diagnostic Criteria of Autism Spectrum Disorder
2020 (English)In: Information, E-ISSN 2078-2489, Information, E-ISSN 2078-2489, Vol. 11, no 2, article id 81Article in journal (Refereed) Published
Abstract [en]

The last few decades have seen widespread advances in technological means to characterise observable aspects of human behaviour such as gaze or posture. Among others, these developments have also led to significant advances in social robotics. At the same time, however, social robots are still largely evaluated in idealised or laboratory conditions, and it remains unclear whether the technological progress is sufficient to let such robots move “into the wild”. In this paper, we characterise the problems that a social robot in the real world may face, and review the technological state of the art in terms of addressing these. We do this by considering what it would entail to automate the diagnosis of Autism Spectrum Disorder (ASD). Just as for social robotics, ASD diagnosis fundamentally requires the ability to characterise human behaviour from observable aspects. However, therapists provide clear criteria regarding what to look for. As such, ASD diagnosis is a situation that is both relevant to real-world social robotics and comes with clear metrics. Overall, we demonstrate that even with relatively clear therapist-provided criteria and current technological progress, the need to interpret covert behaviour cannot yet be fully addressed. Our discussions have clear implications for ASD diagnosis, but also for social robotics more generally. For ASD diagnosis, we provide a classification of criteria based on whether or not they depend on covert information and highlight present-day possibilities for supporting therapists in diagnosis through technological means. For social robotics, we highlight the fundamental role of covert behaviour, show that the current state-of-the-art is unable to charact

Place, publisher, year, edition, pages
MDPI, 2020
Keywords
autism spectrum disorder, diagnosis, technology, behaviour
National Category
Human Computer Interaction
Research subject
Interaction Lab (ILAB)
Identifiers
urn:nbn:se:his:diva-18184 (URN)10.3390/info11020081 (DOI)
Note

This article belongs to the Special Issue Advances in Social Robots

Available from: 2020-02-03 Created: 2020-02-03 Last updated: 2020-02-04Bibliographically approved
Mahmoud, S., Svensson, H. & Thill, S. (2019). Cognitively-inspired episodic imagination for self-driving vehicles. In: : . Paper presented at Workshop TCV2019: Towards Cognitive Vehicles: perception, learning and decision making under real-world constraints. Is bio-inspiration helpful? held on November 8, 2019 in Macau, China, as part of the IROS 2019 conference, The 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems.
Open this publication in new window or tab >>Cognitively-inspired episodic imagination for self-driving vehicles
2019 (English)Conference paper, Poster (with or without abstract) (Refereed)
Abstract [en]

The controller of an autonomous vehicle needsthe ability to learn how to act in different driving scenariosthat it may face. A significant challenge is that it is difficult,dangerous, or even impossible to experience and explore variousactions in situations that might be encountered in the realworld. Autonomous vehicle control would therefore benefitfrom a mechanism that allows the safe exploration of actionpossibilities and their consequences, as well as the ability tolearn from experience thus gained to improve driving skills.In this paper we demonstrate a methodology that allows alearning agent to create simulations of possible situations. Thesesimulations can be chained together in a sequence that allowsthe progressive improvement of the agent’s performance suchthat the agent is able to appropriately deal with novel situationsat the end of training. This methodology takes inspirationfrom the human ability to imagine hypothetical situations usingepisodic simulation; we therefore refer to this methodology asepisodic imagination.An interesting question in this respect is what effect thestructuring of such a sequence of episodic imaginations hason performance. Here, we compare a random process to astructured one and initial results indicate  that a structuredsequence outperforms a random one.

National Category
Robotics
Identifiers
urn:nbn:se:his:diva-18175 (URN)
Conference
Workshop TCV2019: Towards Cognitive Vehicles: perception, learning and decision making under real-world constraints. Is bio-inspiration helpful? held on November 8, 2019 in Macau, China, as part of the IROS 2019 conference, The 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems
Funder
EU, Horizon 2020, 41365
Available from: 2020-01-28 Created: 2020-01-28 Last updated: 2020-02-05
Thill, S. & Riveiro, M. (2019). Memento hominibus: on the fundamental role of end users in real-world interactions with neuromorphic systems. In: Proceedings of the Workshop on Robust Artificial Intelligence for Neurorobotics (RAI-NR) 2019: . Paper presented at Workshop on Robust Artificial Intelligence for Neurorobotics (RAI-NR) 2019, 26 – 28 August, University of Edinburgh, United Kingdom.
Open this publication in new window or tab >>Memento hominibus: on the fundamental role of end users in real-world interactions with neuromorphic systems
2019 (English)In: Proceedings of the Workshop on Robust Artificial Intelligence for Neurorobotics (RAI-NR) 2019, 2019Conference paper, Published paper (Refereed)
National Category
Computer and Information Sciences
Research subject
Interaction Lab (ILAB)
Identifiers
urn:nbn:se:his:diva-17786 (URN)
Conference
Workshop on Robust Artificial Intelligence for Neurorobotics (RAI-NR) 2019, 26 – 28 August, University of Edinburgh, United Kingdom
Available from: 2019-10-11 Created: 2019-10-11 Last updated: 2019-12-17Bibliographically approved
Cao, H.-L., Esteban, P. G., Bartlett, M., Baxter, P. E., Belpaeme, T., Billing, E., . . . Ziemke, T. (2019). Robot-Enhanced Therapy: Development and Validation of a Supervised Autonomous Robotic System for Autism Spectrum Disorders Therapy. IEEE robotics & automation magazine, 26(2), 49-58
Open this publication in new window or tab >>Robot-Enhanced Therapy: Development and Validation of a Supervised Autonomous Robotic System for Autism Spectrum Disorders Therapy
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2019 (English)In: IEEE robotics & automation magazine, ISSN 1070-9932, E-ISSN 1558-223X, Vol. 26, no 2, p. 49-58Article in journal (Refereed) Published
Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2019
National Category
Human Computer Interaction
Research subject
Interaction Lab (ILAB)
Identifiers
urn:nbn:se:his:diva-16864 (URN)10.1109/MRA.2019.2904121 (DOI)000471680800008 ()2-s2.0-85064382580 (Scopus ID)
Projects
DREAM, FP7 grant #611391.
Available from: 2019-05-06 Created: 2019-05-06 Last updated: 2019-11-18Bibliographically approved
Mahmoud, S. (2019). The Kaizen Agent: A self-driving car continuously learns by imagination.
Open this publication in new window or tab >>The Kaizen Agent: A self-driving car continuously learns by imagination
2019 (English)Report (Other academic)
Abstract [en]

For an agent to autonomously interact in a real world environment, it needs tolearn how to behave in the different scenarios that it may face. There are differentapproaches of modeling an artificial agent with interactive capabilities. Oneapproach is providing the agent with knowledge beforehand. Another approachis to let the agent learn from data and interaction. A well-known techniques ofthe former approach is supervised learning. In this approach, data is collected,labeled and provided to train the network as pre-defined input and correct outputas a training set. This requires data to be available beforehand. In a realworld environment however, it is difficult to determine all possible interactionsand provide the correct response to each. The agent thus needs to be able tolearn by itself from the environment to figure out the best reaction in each situation.To facilitate this, the agent needs to be able to sense the environment,make decisions and react back to the environment. The agent repeats this tryingdifferent decisions. To learn from these trials, the agent needs to accumulate oldexperiences, learn and adjust its knowledge and develop progressively after eachinteraction. However, in many applications, experiencing various actions in differentscenarios is difficult, dangerous or even impossible. The agent thereforeneeds an experimental environment where it can safely explore the possibilities,learn from experiences and develop new skills.This research aims to develop a methodology to build an interactive learningagent that can improve its learning performance progressively and perform wellin real world environments. The agent follows the Japanese concept Kaizenwhich refers to activities that continuously improve all functions. It meansstriving for continuous improvement and not radically changing processes. Thecontribution of this research is first to model and develop this agent so thatit can acquire new knowledge based on existing knowledge without negativelyaffecting the old knowledge and skills. Secondly, this research aims to developa novel method to systematically generate synthetic scenarios that contributesto its learning performance.This proposal consists of the background of artificial cognitive systems, acomparison of the theories and approaches in artificial cognitive systems fordeveloping a learning interactive system, and a review of the state of the artin reinforcement learning. Imagination-based learning is discussed and the purposesof imagination are defined. Imagination for creation is used as a scenariogenerator for practicing new skills without the necessity to try them all in thereal world. The research proposal results in the research questions and objectivesto be investigated as well as an outline of the methodology.

Publisher
p. 38
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Interaction Lab (ILAB)
Identifiers
urn:nbn:se:his:diva-18176 (URN)
Available from: 2020-01-28 Created: 2020-01-28 Last updated: 2020-02-05
Bartlett, M., Edmunds, C. E. .., Belpaeme, T., Thill, S. & Lemaignan, S. (2019). What Can You See?: Identifying Cues on Internal States From the Movements of Natural Social Interactions. Frontiers in Robotics and AI, 6(49)
Open this publication in new window or tab >>What Can You See?: Identifying Cues on Internal States From the Movements of Natural Social Interactions
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2019 (English)In: Frontiers in Robotics and AI, E-ISSN 2296-9144, Vol. 6, no 49Article in journal (Refereed) Published
Abstract [en]

In recent years, the field of Human-Robot Interaction (HRI) has seen an increasingdemand for technologies that can recognize and adapt to human behaviors and internalstates (e.g., emotions and intentions). Psychological research suggests that humanmovements are important for inferring internal states. There is, however, a need to betterunderstand what kind of information can be extracted from movement data, particularlyin unconstrained, natural interactions. The present study examines which internal statesand social constructs humans identify from movement in naturalistic social interactions.Participants either viewed clips of the full scene or processed versions of it displaying2D positional data. Then, they were asked to fill out questionnaires assessing their socialperception of the viewed material. We analyzed whether the full scene clips were moreinformative than the 2D positional data clips. First, we calculated the inter-rater agreementbetween participants in both conditions. Then, we employed machine learning classifiersto predict the internal states of the individuals in the videos based on the ratingsobtained. Although we found a higher inter-rater agreement for full scenes comparedto positional data, the level of agreement in the latter case was still above chance,thus demonstrating that the internal states and social constructs under study wereidentifiable in both conditions. A factor analysis run on participants’ responses showedthat participants identified the constructs interaction imbalance, interaction valence andengagement regardless of video condition. The machine learning classifiers achieveda similar performance in both conditions, again supporting the idea that movementalone carries relevant information. Overall, our results suggest it is reasonable to expecta machine learning algorithm, and consequently a robot, to successfully decode andclassify a range of internal states and social constructs using low-dimensional data (suchas the movements and poses of observed individuals) as input.

Place, publisher, year, edition, pages
Frontiers Research Foundation, 2019
Keywords
social psychology, human-robot interaction, machine learning, social interaction, recognition
National Category
Human Computer Interaction
Research subject
Interaction Lab (ILAB)
Identifiers
urn:nbn:se:his:diva-17301 (URN)10.3389/frobt.2019.00049 (DOI)000473169300001 ()2-s2.0-85068522657 (Scopus ID)
Available from: 2019-06-26 Created: 2019-06-26 Last updated: 2019-12-02Bibliographically approved
Thill, S. (2019). What we need from an embodied cognitive architecture. In: Maria Isabel Aldinhas Ferreira, João Silva Sequeira, Rodrigo Ventura (Ed.), Cognitive Architectures: (pp. 43-57). Cham: Springer
Open this publication in new window or tab >>What we need from an embodied cognitive architecture
2019 (English)In: Cognitive Architectures / [ed] Maria Isabel Aldinhas Ferreira, João Silva Sequeira, Rodrigo Ventura, Cham: Springer, 2019, p. 43-57Chapter in book (Refereed)
Abstract [en]

Given that original purpose of cognitive architectures was to lead to a unified theory of cognition, this chapter considers the possible contributions that cognitive architectures can make to embodied theories of cognition in particular. This is not a trivial question since the field remains very much divided about what embodied cognition actually means, and we will see some example positions in this chapter. It is then argued that a useful embodied cognitive architecture would be one that can demonstrate (a) what precisely the role of the body in cognition actually is, and (b) whether a body is constitutively needed at all for some (or all) cognitive processes. It is proposed that such questions can be investigated if the cognitive architecture is designed so that consequences of varying the precise embodiment on higher cognitive mechanisms can be explored. This is in contrast with, for example, those cognitive architectures in robotics that are designed for specific bodies first; or architectures in cognitive science that implement embodiment as an add-on to an existing framework (because then, that framework is by definition not constitutively shaped by the embodiment). The chapter concludes that the so-called semantic pointer architecture by Eliasmith and colleagues may be one framework that satisfies our desiderata and may be well-suited for studying theories of embodied cognition further.

Place, publisher, year, edition, pages
Cham: Springer, 2019
Series
Intelligent Systems, Control and Automation: Science and Engineering, ISSN 2213-8986, E-ISSN 2213-8994 ; 94
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Interaction Lab (ILAB); INF302 Autonomous Intelligent Systems
Identifiers
urn:nbn:se:his:diva-16167 (URN)10.1007/978-3-319-97550-4_4 (DOI)000465469800005 ()2-s2.0-85051464325 (Scopus ID)978-3-319-97549-8 (ISBN)978-3-319-97550-4 (ISBN)
Available from: 2018-09-07 Created: 2018-09-07 Last updated: 2019-06-11Bibliographically approved
Thill, S., Riveiro, M., Lagerstedt, E., Lebram, M., Hemeren, P., Habibovic, A. & Klingegård, M. (2018). Driver adherence to recommendations from support systems improves if the systems explain why they are given: A simulator study. Transportation Research Part F: Traffic Psychology and Behaviour, 56, 420-435
Open this publication in new window or tab >>Driver adherence to recommendations from support systems improves if the systems explain why they are given: A simulator study
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2018 (English)In: Transportation Research Part F: Traffic Psychology and Behaviour, ISSN 1369-8478, E-ISSN 1873-5517, Vol. 56, p. 420-435Article in journal (Refereed) Published
Abstract [en]

This paper presents a large-scale simulator study on driver adherence to recommendationsgiven by driver support systems, specifically eco-driving support and navigation support.123 participants took part in this study, and drove a vehicle simulator through a pre-defined environment for a duration of approximately 10 min. Depending on the experi-mental condition, participants were either given no eco-driving recommendations, or asystem whose provided support was either basic (recommendations were given in theform of an icon displayed in a manner that simulates a heads-up display) or informative(the system additionally displayed a line of text justifying its recommendations). A naviga-tion system that likewise provided either basic or informative support, depending on thecondition, was also provided.

Effects are measured in terms of estimated simulated fuel savings as well as engine brak-ing/coasting behaviour and gear change efficiency. Results indicate improvements in allvariables. In particular, participants who had the support of an eco-driving system spenta significantly higher proportion of the time coasting. Participants also changed gears atlower engine RPM when using an eco-driving support system, and significantly more sowhen the system provided justifications. Overall, the results support the notion that pro-viding reasons why a support system puts forward a certain recommendation improvesadherence to it over mere presentation of the recommendation.

Finally, results indicate that participants’ driving style was less eco-friendly if the navi-gation system provided justifications but the eco-system did not. This may be due to par-ticipants considering the two systems as one whole rather than separate entities withindividual merits. This has implications for how to design and evaluate a given driver sup-port system since its effectiveness may depend on the performance of other systems in thevehicle.

Keywords
Driver behaviour, System awareness, Eco-friendly behaviour, Driver recommendation systems
National Category
Psychology Human Computer Interaction Information Systems
Research subject
Interaction Lab (ILAB); Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science; INF302 Autonomous Intelligent Systems
Identifiers
urn:nbn:se:his:diva-15279 (URN)10.1016/j.trf.2018.05.009 (DOI)000437997700037 ()2-s2.0-85048505654 (Scopus ID)
Projects
TIEB
Funder
Swedish Energy Agency
Available from: 2018-06-04 Created: 2018-06-04 Last updated: 2019-11-19Bibliographically approved
Lagerstedt, E. & Thill, S. (2018). Perception of Agent Properties in Humans and Machines. In: : . Paper presented at 41st European Conference on Visual Perception ECVP 2018, 26–30 August 2018, Trieste, Italy (pp. 124-124). , 48
Open this publication in new window or tab >>Perception of Agent Properties in Humans and Machines
2018 (English)Conference paper, Poster (with or without abstract) (Refereed)
Series
PERCEPTION, ISSN 0301-0066, E-ISSN 1468-4233
National Category
Psychology (excluding Applied Psychology)
Research subject
Interaction Lab (ILAB); INF302 Autonomous Intelligent Systems
Identifiers
urn:nbn:se:his:diva-16131 (URN)000468288300466 ()
Conference
41st European Conference on Visual Perception ECVP 2018, 26–30 August 2018, Trieste, Italy
Projects
Dreams4Cars
Funder
EU, Horizon 2020, 731593
Available from: 2018-09-03 Created: 2018-09-03 Last updated: 2019-06-07Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-1177-4119

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