| 1. |
- Bartlett, Madeleine E., et al.
(författare)
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Requirements for Robotic Interpretation of Social Signals “in the Wild” : Insights from Diagnostic Criteria of Autism Spectrum Disorder
- 2020
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Ingår i: Information. - : MDPI. - 2078-2489. ; 11:2
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Tidskriftsartikel (refereegranskat)abstract
- 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
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| 2. |
- Bartlett, Madeleine, et al.
(författare)
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What Can You See? : Identifying Cues on Internal States From the Movements of Natural Social Interactions
- 2019
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Ingår i: Frontiers in Robotics and AI. - : Frontiers Research Foundation. - 2296-9144. ; 6:49
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 3. |
- Billing, Erik, PhD, 1981-, et al.
(författare)
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The DREAM Dataset : Supporting a data-driven study of autism spectrum disorder and robot enhanced therapy
- 2020
-
Ingår i: PLOS ONE. - : Public Library of Science. - 1932-6203. ; 15:8
-
Tidskriftsartikel (refereegranskat)abstract
- We present a dataset of behavioral data recorded from 61 children diagnosed with Autism Spectrum Disorder (ASD). The data was collected during a large-scale evaluation of Robot Enhanced Therapy (RET). The dataset covers over 3000 therapy sessions and more than 300 hours of therapy. Half of the children interacted with the social robot NAO supervised by a therapist. The other half, constituting a control group, interacted directly with a therapist. Both groups followed the Applied Behavior Analysis (ABA) protocol. Each session was recorded with three RGB cameras and two RGBD (Kinect) cameras, providing detailed information of children’s behavior during therapy. This public release of the dataset comprises body motion, head position and orientation, and eye gaze variables, all specified as 3D data in a joint frame of reference. In addition, metadata including participant age, gender, and autism diagnosis (ADOS) variables are included. We release this data with the hope of supporting further data-driven studies towards improved therapy methods as well as a better understanding of ASD in general.
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| 4. |
- Cai, Haibin, et al.
(författare)
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Sensing-enhanced Therapy System for Assessing Children with Autism Spectrum Disorders : A Feasibility Study
- 2019
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Ingår i: IEEE Sensors Journal. - : Institute of Electrical and Electronics Engineers (IEEE). - 1530-437X .- 1558-1748. ; 19:4, s. 1508-1518
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Tidskriftsartikel (refereegranskat)abstract
- It is evident that recently reported robot-assisted therapy systems for assessment of children with autism spectrum disorder (ASD) lack autonomous interaction abilities and require significant human resources. This paper proposes a sensing system that automatically extracts and fuses sensory features such as body motion features, facial expressions, and gaze features, further assessing the children behaviours by mapping them to therapist-specified behavioural classes. Experimental results show that the developed system has a capability of interpreting characteristic data of children with ASD, thus has the potential to increase the autonomy of robots under the supervision of a therapist and enhance the quality of the digital description of children with ASD. The research outcomes pave the way to a feasible machine-assisted system for their behaviour assessment. IEEE
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| 5. |
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| 6. |
- Chersi, Fabian, et al.
(författare)
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Sentence processing : linking language to motor chains
- 2010
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Ingår i: Frontiers in Neurorobotics. - : Frontiers Media S.A.. - 1662-5218. ; 4
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Tidskriftsartikel (refereegranskat)abstract
- A growing body of evidence in cognitive science and neuroscience points towards the existence of a deep interconnection between cognition, perception and action. According to this embodied perspective language is grounded in the sensorimotor system and language understanding is based on a mental simulation process (Jeannerod, 2007; Gallese, 2008; Barsalou, 2009). This means that during action words and sentence comprehension the same perception, action, and emotion mechanisms implied during interaction with objects are recruited. Among the neural underpinnings of this simulation process an important role is played by a sensorimotor matching system known as the mirror neuron system (Rizzolatti and Craighero, 2004). Despite a growing number of studies, the precise dynamics underlying the relation between language and action are not yet well understood. In fact, experimental studies are not always coherent as some report that language processing interferes with action execution while others find facilitation. In this work we present a detailed neural network model capable of reproducing experimentally observed influences of the processing of action-related sentences on the execution of motor sequences. The proposed model is based on three main points. The first is that the processing of action-related sentences causes the resonance of motor and mirror neurons encoding the corresponding actions. The second is that there exists a varying degree of crosstalk between neuronal populations depending on whether they encode the same motor act, the same effector or the same action-goal. The third is the fact that neuronal populations’ internal dynamics, which results from the combination of multiple processes taking place at different time scales, can facilitate or interfere with successive activations of the same or of partially overlapping pools.
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| 7. |
- Da Lio, Mauro, et al.
(författare)
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Exploiting Dream-Like Simulation Mechanisms to Develop Safer Agents for Automated Driving The "Dreams4Cars" EU Research and Innovation Action
- 2017
-
Ingår i: 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC). - : IEEE. - 9781538615263 - 9781538615270
-
Konferensbidrag (refereegranskat)abstract
- Automated driving needs unprecedented levels of reliably and safety before marked deployment. The average human driver fatal accident rate is 1 every 100 million miles. Automated vehicles will have to provably best these figures. This paper introduces the notion of dream-like mechanisms as a simulation technology to produce a large number of hypothetical design and test scenarios - especially focusing on variations of more frequent dangerous and near miss events. Grounded in the simulation hypothesis of cognition, we show here some principles for effective simulation mechanisms and an artificial cognitive system architecture that can learn from the simulated situations.
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| 8. |
- Drejing, Karl, 1988-, et al.
(författare)
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Engagement: A traceable motivational concept in human-robot interaction
- 2015
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Ingår i: Affective Computing and Intelligent Interaction (ACII), 2015 International Conference on. - : IEEE Computer Society. - 9781479999538 ; , s. 956-961
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Konferensbidrag (refereegranskat)abstract
- Engagement is essential to meaningful social interaction between humans. Understanding the mechanisms by which we detect engagement of other humans can help us understand how we can build robots that interact socially with humans. However, there is currently a lack of measurable engagement constructs on which to build an artificial system that can reliably support social interaction between humans and robots. This paper proposes a definition, based on motivation theories, and outlines a framework to explore the idea that engagement can be seen as specific behaviors and their attached magnitude or intensity. This is done by the use of data from multiple sources such as observer ratings, kinematic data, audio and outcomes of interactions. We use the domain of human-robot interaction in order to illustrate the application of this approach. The framework further suggests a method to gather and aggregate this data. If certain behaviors and their attached intensities co-occur with various levels of judged engagement, then engagement could be assessed by this framework consequently making it accessible to a robotic platform. This framework could improve the social capabilities of interactive agents by adding the ability to notice when and why an agent becomes disengaged, thereby providing the interactive agent with an ability to reengage him or her. We illustrate and propose validation of our framework with an example from robot-assisted therapy for children with autism spectrum disorder. The framework also represents a general approach that can be applied to other social interactive settings between humans and robots, such as interactions with elderly people.
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| 9. |
- Durán, Boris, et al.
(författare)
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Modelling interaction in multi-modal affordance processing with neural dynamics
- 2012
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Ingår i: From Animals to Animats 12. - Berlin, Heidelberg : Springer Berlin/Heidelberg. - 9783642330926 - 9783642330933 ; , s. 75-84
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Konferensbidrag (refereegranskat)abstract
- Behavioral studies on the activation of affordances by understanding observation and action sentences on graspable objects show a direct relationship between the canonical orientation of graspable objects, their dimension and the kind of grip required by those objects to be grasped. The present work introduces the concepts of Dynamic Field Theory for modeling the results observed in the behavioral studies previously mentioned. The model was not only able to replicate qualitatively similar results regarding reaction times, but also the identification of same versus different object and the distinction between observable versus action sentences. The model shows the potential of dynamic field theory for the design and implementation of brain inspired cognitive systems. © 2012 Springer-Verlag.
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| 10. |
- Emruli, Blerim
(författare)
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Simple principles of cognitive computation with distributed representations
- 2012
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Brains and computers represent and process sensory information in different ways. Bridgingthat gap is essential for managing and exploiting the deluge of unprocessed andcomplex data in modern information systems. The development of brain-like computersthat learn from experience and process information in a non-numeric cognitive way willopen up new possibilities in the design and operation of both sensor and informationcommunication systems.This thesis presents a set of simple computational principles with cognitive qualities,which can enable computers to learn interesting relationships in large amounts of datastreaming from complex and changing real-world environments. More specifically, thiswork focuses on the construction of a computational model for analogical mapping andthe development of a method for semantic analysis with high-dimensional arrays.A key function of cognitive systems is the ability to make analogies. A computationalmodel of analogical mapping that learns to generalize from experience is presented in thisthesis. This model is based on high-dimensional random distributed representations anda sparse distributed associative memory. The model has a one-shot learning process andan ability to recall distinct mappings. After learning a few similar mapping examplesthe model generalizes and performs analogical mapping of novel inputs. As a majorimprovement over related models, the proposed model uses associative memory to learnmultiple analogical mappings in a coherent way.Random Indexing (RI) is a brain-inspired dimension reduction method that was developedfor natural language processing to identify semantic relationships in text. Ageneralized mathematical formulation of RI is presented, which enables N-way RandomIndexing (NRI) of multidimensional arrays. NRI is an approximate, incremental, scalable,and lightweight dimension reduction method for large non-sparse arrays. In addition, itprovides low and predictable storage requirements, and also enables the range of arrayindices to be further extended without modification of the data representation. Numericalsimulations of two-way and ordinary one-way RI are presented that illustrate whenthe approach is feasible. In conclusion, it is suggested that NRI can be used as a tool tomanage and exploit Big Data, for instance in data mining, information retrieval, socialnetwork analysis, and other machine learning applications.
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| 11. |
- Esteban, Pablo G., et al.
(författare)
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How to Build a Supervised Autonomous System for Robot-Enhanced Therapy for Children with Autism Spectrum Disorder
- 2017
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Ingår i: Paladyn - Journal of Behavioral Robotics. - : De Gruyter Open. - 2080-9778 .- 2081-4836. ; 8:1, s. 18-38
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Tidskriftsartikel (refereegranskat)abstract
- Robot-Assisted Therapy (RAT) has successfully been used to improve social skills in children with autism spectrum disorders (ASD) through remote control of the robot in so-called Wizard of Oz (WoZ) paradigms.However, there is a need to increase the autonomy of the robot both to lighten the burden on human therapists (who have to remain in control and, importantly, supervise the robot) and to provide a consistent therapeutic experience. This paper seeks to provide insight into increasing the autonomy level of social robots in therapy to move beyond WoZ. With the final aim of improved human-human social interaction for the children, this multidisciplinary research seeks to facilitate the use of social robots as tools in clinical situations by addressing the challenge of increasing robot autonomy.We introduce the clinical framework in which the developments are tested, alongside initial data obtained from patients in a first phase of the project using a WoZ set-up mimicking the targeted supervised-autonomy behaviour. We further describe the implemented system architecture capable of providing the robot with supervised autonomy.
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| 12. |
- Hemeren, Paul E., et al.
(författare)
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Deriving motor primitives through action segmentation
- 2011
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Ingår i: Frontiers in Psychology. - : Frontiers Media S.A.. - 1664-1078. ; 1, s. 1-11
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Tidskriftsartikel (refereegranskat)abstract
- The purpose of the present experiment is to further understand the effect of levels of processing (top-down vs. bottom-up) on the perception of movement kinematics and primitives for grasping actions in order to gain insight into possible primitives used by the mirror system. In the present study, we investigated the potential of identifying such primitives using an action segmentation task. Specifically, we investigated whether or not segmentation was driven primarily by the kinematics of the action, as opposed to high-level top-down information about the action and the object used in the action. Participants in the experiment were shown 12 point-light movies of object-centered hand/arm actions that were either presented in their canonical orientation together with the object in question (top-down condition) or upside down (inverted) without information about the object (bottom-up condition). The results show that (1) despite impaired high-level action recognition for the inverted actions participants were able to reliably segment the actions according to lower-level kinematic variables, (2) segmentation behavior in both groups was significantly related to the kinematic variables of change in direction, velocity, and acceleration of the wrist (thumb and finger tips) for most of the included actions. This indicates that top-down activation of an action representation leads to similar segmentation behavior for hand/arm actions compared to bottom-up, or local, visual processing when performing a fairly unconstrained segmentation task. Motor primitives as parts of more complex actions may therefore be reliably derived through visual segmentation based on movement kinematics.
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| 13. |
- Hemeren, Paul, et al.
(författare)
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Kinematic-based classification of social gestures and grasping by humans and machine learning techniques
- 2021
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Ingår i: Frontiers in Robotics and AI. - : Frontiers Media S.A.. - 2296-9144. ; 8:308, s. 1-17
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Tidskriftsartikel (refereegranskat)abstract
- The affective motion of humans conveys messages that other humans perceive and understand without conventional linguistic processing. This ability to classify human movement into meaningful gestures or segments plays also a critical role in creating social interaction between humans and robots. In the research presented here, grasping and social gesture recognition by humans and four machine learning techniques (k-Nearest Neighbor, Locality-Sensitive Hashing Forest, Random Forest and Support Vector Machine) is assessed by using human classification data as a reference for evaluating the classification performance of machine learning techniques for thirty hand/arm gestures. The gestures are rated according to the extent of grasping motion on one task and the extent to which the same gestures are perceived as social according to another task. The results indicate that humans clearly rate differently according to the two different tasks. The machine learning techniques provide a similar classification of the actions according to grasping kinematics and social quality. Furthermore, there is a strong association between gesture kinematics and judgments of grasping and the social quality of the hand/arm gestures. Our results support previous research on intention-from-movement understanding that demonstrates the reliance on kinematic information for perceiving the social aspects and intentions in different grasping actions as well as communicative point-light actions.
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| 14. |
- Kleinhans, Ashley, et al.
(författare)
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Modelling primate control of grasping for robotics applications
- 2015. - 1
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Ingår i: Computer Vision - ECCV 2014 Workshops. - Cham : Springer International Publishing Switzerland. - 9783319161808 - 9783319161815 ; , s. 438-447
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Bokkapitel (refereegranskat)abstract
- The neural circuits that control grasping and perform related visual processing have been studied extensively in macaque monkeys. We are developing a computational model of this system, in order to better understand its function, and to explore applications to robotics. We recently modelled the neural representation of three-dimensional object shapes, and are currently extending the model to produce hand postures so that it can be tested on a robot. To train the extended model, we are developing a large database of object shapes and corresponding feasible grasps. Finally, further extensions are needed to account for the influence of higher-level goals on hand posture. This is essential because often the same object must be grasped in different ways for different purposes. The present paper focuses on a method of incorporating such higher-level goals. A proof-of-concept exhibits several important behaviours, such as choosing from multiple approaches to the same goal. Finally, we discuss a neural representation of objects that supports fast searching for analogous objects.
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| 15. |
- Lagerstedt, Erik, et al.
(författare)
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Agent Autonomy and Locus of Responsibility for Team Situation Awareness
- 2017
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Ingår i: HAI '17. - New York : Association for Computing Machinery (ACM). - 9781450351133 ; , s. 261-269
-
Konferensbidrag (refereegranskat)abstract
- Rapid technical advancements have led to dramatically improved abilities for artificial agents, and thus opened up for new ways of cooperation between humans and them, from disembodied agents such as Siris to virtual avatars, robot companions, and autonomous vehicles. It is therefore relevant to study not only how to maintain appropriate cooperation, but also where the responsibility for this resides and/or may be affected. While there are previous organisations and categorisations of agents and HAI research into taxonomies, situations with highly responsible artificial agents are rarely covered. Here, we propose a way to categorise agents in terms of such responsibility and agent autonomy, which covers the range of cooperation from humans getting help from agents to humans providing help for the agents. In the resulting diagram presented in this paper, it is possible to relate different kinds of agents with other taxonomies and typical properties. A particular advantage of this taxonomy is that it highlights under what conditions certain effects known to modulate the relationship between agents (such as the protégé effect or the "we"-feeling) arise.
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| 16. |
- Lagerstedt, Erik, et al.
(författare)
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Benchmarks for evaluating human-robot interaction : lessons learned from human-animal interactions
- 2020
-
Ingår i: Proceedings of the 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). - : IEEE. - 9781728160764 - 9781728160757 ; , s. 137-143
-
Konferensbidrag (refereegranskat)abstract
- Human-robot interaction (HRI) is fundamentally concerned with studying the interaction between humans and robots. While it is still a relatively young field, it can draw inspiration from other disciplines studying human interaction with other types of agents. Often, such inspiration is sought from the study of human-computer interaction (HCI) and the social sciences studying human-human interaction (HHI). More rarely, the field also turns to human-animal interaction (HAI).In this paper, we identify two distinct underlying motivations for making such comparisons: to form a target to recreate orto obtain a benchmark (or baseline) for evaluation. We further highlight relevant (existing) overlap between HRI and HAI, and identify specific themes that are of particular interest for further trans-disciplinary exploration. At the same time, since robots and animals are clearly not the same, we also discuss important differences between HRI and HAI, their complementarity notwithstanding. The overall purpose of this discussion is thus to create an awareness of the potential mutual benefit between the two disciplines and to describe opportunities that exist for future work, both in terms of new domains to explore, and existing results to learn from.
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| 17. |
- Lagerstedt, Erik, et al.
(författare)
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Conceptual Tools for Exploring Perspectives of Different Kinds of Road-Users
- 2023
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Konferensbidrag (refereegranskat)abstract
- The traffic domain is increasingly inhabited by vehicles with driving support systems and automation to the degree where the idea of fully autonomous vehicles is gaining popularity as a credible prediction about the near future. As more aspects of driving become automated, the role of the driver, and the way they perceive their vehicle, surroundings, and fellow road users, change. To address some of the emerging kinds of interaction between different agents in the traffic environment, it is important to take social phenomena and abilities into account, even to the extent of considering highly automated vehicles to be social agents in their own right. To benefit from that, it is important to frame the perception of the traffic environment, as well as the road users in it, in an appropriate theoretical context. We propose that there are helpful concepts related to functional and subjective perception, derived from gestalt psychology and Umweltlehre, that can fill this theoretical need, and support better understanding of vehicles of various degrees of automation.
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| 18. |
- Lagerstedt, Erik, et al.
(författare)
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Interacting with Artificial Agents
- 2015
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Ingår i: Thirteenth Scandinavian Conference on Artificial Intelligence. - : IOS Press. - 9781614995890 - 9781614995883 ; , s. 184-185
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Konferensbidrag (refereegranskat)
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| 19. |
- Lagerstedt, Erik, et al.
(författare)
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Multiple Roles of Multimodality Among Interacting Agents
- 2023
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Ingår i: ACM Transactions on Human-Robot Interaction. - : ACM Digital Library. - 2573-9522. ; 12:2
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Tidskriftsartikel (refereegranskat)abstract
- The term ‘multimodality’ has come to take on several somewhat different meanings depending on the underlying theoretical paradigms and traditions, and the purpose and context of use. The term is closely related to embodiment, which in turn is also used in several different ways. In this paper, we elaborate on this connection and propose that a pragmatic and pluralistic stance is appropriate for multimodality. We further propose a distinction between first and second order effects of multimodality; what is achieved by multiple modalities in isolation and the opportunities that emerge when several modalities are entangled. This highlights questions regarding ways to cluster or interchange different modalities, for example through redundancy or degeneracy. Apart from discussing multimodality with respect to an individual agent, we further look to more distributed agents and situations where social aspects become relevant.In robotics, understanding the various uses and interpretations of these terms can prevent miscommunication when designing robots, as well as increase awareness of the underlying theoretical concepts. Given the complexity of the different ways in which multimodality is relevant in social robotics, this can provide the basis for negotiating appropriate meanings of the term at a case by case basis.
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| 20. |
- Lagerstedt, Erik
(författare)
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Perceiving agents : Pluralism, interaction, and existence
- 2024
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Perception is a vast subject to study. One way to approach and study it might therefore be to break down the concept into smaller pieces. Specific modes of sensation, mechanisms, phenomena, or contexts might be selected as the proxy or starting point for addressing perception as a whole. Another approach would be to widen the concept, and attempt to study perception through the larger context of which it is a part. I have, in this thesis, attempted the latter strategy, by emphasising an existential perspective, and examine the role and nature of perception through that lens.The larger perspective of broadening the scope does not specifically allow for better answers, but rather different kinds of answers, providing complementary ways of exploring what it means to be an artificial or natural agent, and what consequences that can have for the access to, as well as representation, processing, and communication of information. A broader stance can also facilitate exploration of questions regarding larger perspectives, such as the relation between individual agents, as well as their place in larger structures such as societies and cyber-physical systems.In this thesis I use existential phenomenology to frame the concept of perception, while drawing from theories in biology and psychology. My work has a particular focus on human-robot interaction, a field of study at a fascinating intersection of humans designing, using, and communicating with something human-made, partially human-like, yet distinctly non-human. The work is also applied to some aspects of the traffic domain which, given the increasing interest in self-driving vehicles, is partially another instance of complex and naturalistic human-robot interaction.Ultimately, I argue for a pluralistic and pragmatic approach to the understanding of perception, and its related concepts. To understand a system of agents as they interact, it is not only necessary to acknowledge their respective circumstances, but take serious the idea that none of the agents’ constructed worlds are more or less real, they might only be more or less relevant in relation to specific contexts, perspectives, or needs. Such an approach is particularly relevant when addressing the complexities of the increasingly urgent sustainability challenges.
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| 21. |
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| 22. |
- Mahmoud, Sara, 1988-
(författare)
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Cognitively inspired design : Rethink the wheel for self-driving cars
- 2023
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis examines Cognitively Inspired Design (CID), which is the process of transferring cognitive science frameworks and theories to intelligent systems in an application context. The thesis studies the relation between cognitive science and the traditional approach to developing systems. There are numerous differences and challenges between those two fields, making the transformation from cognitive science to designing a novel cognitive system a challenging process. To examine this process, the Guest and Martin (2021) multi-layer model has been utilized. The model proposes a sequence of six layers in which a researcher follows from a defined cognitive concept or framework to an empirical experiment of a computational model. This multi-layered model is a path function in which each layer is a function that takes the input from the previous layer and passes the output to the following layer.The thesis takes the application of self-driving cars as the context of study. Self-driving cars are considered one of the most important applications requiring a high level of intelligence and cognitive ability because they encounter real world scenarios and the risk of failure may cost lives. This thesis analyzes the transformation of CID in three main studies.The first study theoretically analyzes the applicability and compares the different cognitive paradigms and current AI techniques for self-driving cars. The thesis argues for exploring the emergent paradigm as a less explored paradigm in cognitive systems compared to its main opponent paradigm; the cognitivist. The emergent paradigm is claimed to describe the interactive nature of the human cognition. The analysis highlights the opportunities that the field of self-driving cars benefits from when considering the characteristics of the emergent paradigm.The second study considers the path function for a selected emergent paradigm theory. The study focuses on the aspect of how humans learn from hypothetical scenarios before encountering them in the real world, in particular, learning how to handle rare scenarios that are difficult to learn in the real world. The study addresses the mechanism for automatically generating these scenarios without being designed and created manually by a developer. The study considers curriculum learning as the candidate theory subject of study. The process of transferring this theory is studied using the path function multilayer model. The study conducts an experiment to address the relation between the importance of the theory in human learning and its equivalence in artificial cognitive systems.The third study focuses on more debatable theories in the emergent paradigm, in particular enactive and embodiment theories. These theories have gained much attention in research because of the high promise they may deliver for advancing the field of artificial cognitive systems. The applicability of the transition of these theories into artificial cognitive systems is examined in relation to the application of self-driving cars, using the path function multi-layer model. The study considers the aspects that support and hinder such transformation.The thesis concludes by discussing the current state of CID and the aspects the researchers and developers need to consider in this process before, during, and after the transformation. Overall, the thesis attempts to study cognitive theories mainly from an engineering perspective. In short, the thesis focuses on the transformation of CID, not the promise of delivering a novel cognitive system solution.
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| 23. |
- Mahmoud, Sara, 1988-, et al.
(författare)
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Cognitively-inspired episodic imagination for self-driving vehicles
- 2019
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Ingår i: Towards Cognitive Vehicles: perception, learning and decision making under real-world constraints. Is bio-inspiration helpful?. ; , s. 28-31
-
Konferensbidrag (refereegranskat)abstract
- 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.
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| 24. |
- Mahmoud, Sara, 1988-, et al.
(författare)
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How to train a self-driving vehicle : On the added value (or lack thereof) of curriculum learning and replay buffers
- 2023
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Ingår i: Frontiers in Artificial Intelligence. - : Frontiers Media S.A.. - 2624-8212. ; 6
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Tidskriftsartikel (refereegranskat)abstract
- Learning from only real-world collected data can be unrealistic and time consuming in many scenario. One alternative is to use synthetic data as learning environments to learn rare situations and replay buffers to speed up the learning. In this work, we examine the hypothesis of how the creation of the environment affects the training of reinforcement learning agent through auto-generated environment mechanisms. We take the autonomous vehicle as an application. We compare the effect of two approaches to generate training data for artificial cognitive agents. We consider the added value of curriculum learning—just as in human learning—as a way to structure novel training data that the agent has not seen before as well as that of using a replay buffer to train further on data the agent has seen before. In other words, the focus of this paper is on characteristics of the training data rather than on learning algorithms. We therefore use two tasks that are commonly trained early on in autonomous vehicle research: lane keeping and pedestrian avoidance. Our main results show that curriculum learning indeed offers an additional benefit over a vanilla reinforcement learning approach (using Deep-Q Learning), but the replay buffer actually has a detrimental effect in most (but not all) combinations of data generation approaches we considered here. The benefit of curriculum learning does depend on the existence of a well-defined difficulty metric with which various training scenarios can be ordered. In the lane-keeping task, we can define it as a function of the curvature of the road, in which the steeper and more occurring curves on the road, the more difficult it gets. Defining such a difficulty metric in other scenarios is not always trivial. In general, the results of this paper emphasize both the importance of considering data characterization, such as curriculum learning, and the importance of defining an appropriate metric for the task.
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| 25. |
- Mahmoud, Sara, 1988-, et al.
(författare)
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Where to from here? : On the future development of autonomous vehicles from a cognitive systems perspective
- 2022
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Ingår i: Cognitive Systems Research. - : Elsevier. - 2214-4366 .- 1389-0417. ; 76, s. 63-77
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Tidskriftsartikel (refereegranskat)abstract
- Self-driving cars not only solve the problem of navigating safely from location A to location B; they also have to deal with an abundance of (sometimes unpredictable) factors, such as traffic rules, weather conditions, and interactions with humans. Over the last decades, different approaches have been proposed to design intelligent driving systems for self-driving cars that can deal with an uncontrolled environment. Some of them are derived from computationalist paradigms, formulating mathematical models that define the driving agent, while other approaches take inspiration from biological cognition. However, despite the extensive work in the field of self-driving cars, many open questions remain. Here, we discuss the different approaches for implementing driving systems for self-driving cars, as well as the computational paradigms from which they originate. In doing so, we highlight two key messages: First, further progress in the field might depend on adapting new paradigms as opposed to pushing technical innovations in those currently used. Specifically, we discuss how paradigms from cognitive systems research can be a source of inspiration for further development in modeling driving systems, highlighting emergent approaches as a possible starting point. Second, self-driving cars can themselves be considered cognitive systems in a meaningful sense, and are therefore a relevant, yet underutilised resource in the study of cognitive mechanisms. Overall, we argue for a stronger synergy between the fields of cognitive systems and self-driving vehicles.
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| 26. |
- Moore, Roger K., et al.
(författare)
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Vocal interactivity in-and-between humans, animals and robots
- 2016
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Ingår i: Frontiers in Robotics and AI. - : Frontiers Research Foundation. - 2296-9144. ; 3
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Forskningsöversikt (refereegranskat)abstract
- Almost all animals exploit vocal signals for a range of ecologically-motivated purposes: detecting predators/prey and marking territory, expressing emotions, establishing social relations and sharing information. Whether it is a bird raising an alarm, a whale calling to potential partners, a dog responding to human commands, a parent reading a story with a child, or a business-person accessing stock prices using \emph{Siri}, vocalisation provides a valuable communication channel through which behaviour may be coordinated and controlled, and information may be distributed and acquired. Indeed, the ubiquity of vocal interaction has led to research across an extremely diverse array of fields, from assessing animal welfare, to understanding the precursors of human language, to developing voice-based human-machine interaction. Opportunities for cross-fertilisation between these fields abound; for example, using artificial cognitive agents to investigate contemporary theories of language grounding, using machine learning to analyse different habitats or adding vocal expressivity to the next generation of language-enabled autonomous social agents. However, much of the research is conducted within well-defined disciplinary boundaries, and many fundamental issues remain. This paper attempts to redress the balance by presenting a comparative review of vocal interaction within-and-between humans, animals and artificial agents (such as robots), and it identifies a rich set of open research questions that may benefit from an inter-disciplinary analysis.
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| 27. |
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| 28. |
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| 29. |
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| 30. |
- Riveiro, Maria, 1978-, et al.
(författare)
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“That's (not) the output I expected!” On the role of end user expectations in creating explanations of AI systems
- 2021
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Ingår i: Artificial Intelligence. - : Elsevier. - 0004-3702 .- 1872-7921. ; 298
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Tidskriftsartikel (refereegranskat)abstract
- Research in the social sciences has shown that expectations are an important factor in explanations as used between humans: rather than explaining the cause of an event per se, the explainer will often address another event that did not occur but that the explainee might have expected. For AI-powered systems, this finding suggests that explanation-generating systems may need to identify such end user expectations. In general, this is a challenging task, not the least because users often keep them implicit; there is thus a need to investigate the importance of such an ability.In this paper, we report an empirical study with 181 participants who were shown outputs from a text classifier system along with an explanation of why the system chose a particular class for each text. Explanations were both factual, explaining why the system produced a certain output or counterfactual, explaining why the system produced one output instead of another. Our main hypothesis was explanations should align with end user expectations; that is, a factual explanation should be given when the system's output is in line with end user expectations, and a counterfactual explanation when it is not.We find that factual explanations are indeed appropriate when expectations and output match. When they do not, neither factual nor counterfactual explanations appear appropriate, although we do find indications that our counterfactual explanations contained at least some necessary elements. Overall, this suggests that it is important for systems that create explanations of AI systems to infer what outputs the end user expected so that factual explanations can be generated at the appropriate moments. At the same time, this information is, by itself, not sufficient to also create appropriate explanations when the output and user expectations do not match. This is somewhat surprising given investigations of explanations in the social sciences, and will need more scrutiny in future studies.
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| 31. |
- Riveiro, Maria, 1978-, et al.
(författare)
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The challenges of providing explanations of AI systems when they do not behave like users expect
- 2022
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Ingår i: UMAP '22: Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization. - New York : Association for Computing Machinery (ACM). - 9781450392075 ; , s. 110-120
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Konferensbidrag (refereegranskat)abstract
- Explanations in artificial intelligence (AI) ensure that users of complex AI systems understand why the system behaves as it does. Expectations that users may have about the system behaviour play a role since they co-determine appropriate content of the explanations. In this paper, we investigate user-desired content of explanations when the system behaves in unexpected ways. Specifically, we presented participants with various scenarios involving an automated text classifier and then asked them to indicate their preferred explanation in each scenario. One group of participants chose the type of explanation from a multiple-choice questionnaire, the other had to answer using free text.Participants show a pretty clear agreement regarding the preferred type of explanation when the output matches expectations: most do not require an explanation at all, while those that do would like one that explains what features of the input led to the output (a factual explanation). When the output does not match expectations, users also prefer different explanations. Interestingly, there is less of an agreement in the multiple-choice questionnaire. However, the free text responses indicate slightly favour an explanation that describes how the AI system's internal workings led to the observed output (i.e., a mechanistic explanation).Overall, we demonstrate that user expectations are a significant variable in determining the most suitable content of explanations (including whether an explanation is needed at all). We also find different results, especially when the output does not match expectations, depending on whether participants answered via multiple-choice or free text. This shows a sensitivity to precise experimental setups that may explain some of the variety in the literature.
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| 32. |
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| 33. |
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| 34. |
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| 35. |
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| 36. |
- Svensson, Henrik, et al.
(författare)
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Dreaming of electric sheep? : Exploring the functions of dream-like mechanisms in the development of mental imagery simulations
- 2013
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Ingår i: Adaptive Behavior. - : Sage Publications. - 1059-7123 .- 1741-2633. ; 21:4, s. 222-238
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Tidskriftsartikel (refereegranskat)abstract
- According to the simulation hypothesis, mental imagery can be explained in terms of predictive chains of simulated perceptions and actions, i.e., perceptions and actions are reactivated internally by our nervous system to be used in mental imagery and other cognitive phenomena. Our previous research shows that it is possible but not trivial to develop simulations in robots based on the simulation hypothesis. While there are several previous approaches to modelling mental imagery and related cognitive abilities, the origin of such internal simulations has hardly been addressed. The inception of simulation (InSim) hypothesis suggests that dreaming has a function in the development of simulations by forming associations between experienced, non-experienced but realistic, and even unrealistic perceptions. Here, we therefore develop an experimental set-up based on a simple simulated robot to test whether such dream-like mechanisms can be used to instruct research into the development of simulations and mental imagery-like abilities. Specifically, the hypothesis is that dreams' informing the construction of simulations lead to faster development of good simulations during waking behaviour. The paper presents initial results in favour of the hypothesis.
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| 37. |
- Svensson, Henrik, et al.
(författare)
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Should robots dream of electric sheep?
- 2012
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Ingår i: Proceedings of Workshop on Artificial Mental Imagery in Cognitive Systems and Robotics. - Plymouth : University of Plymouth Press. - 9781841023250 ; , s. 11-14
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Konferensbidrag (refereegranskat)
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| 38. |
- Svensson, Henrik
(författare)
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Simulations
- 2013
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis is concerned with explanations of embodied cognition as internal simulation. The hypothesis is that several cognitive processes can be explained in terms of predictive chains of simulated perceptions and actions.In other words, perceptions and actions are reactivated internally by the nervous system to be used in cognitive phenomena such as mental imagery.This thesis contributes by advancing the theoretical foundations of simulations and the empirical grounds on which they are based, including a review of the empiricial evidence for the existence of simulated perceptions and actions in cognition, a clarification of the representational function of simulations in cognition, as well as identifying implicit, bodily and environmental anticipation as key mechanisms underlying such simulations. The thesis also develops the ³inception of simulation² hypothesis, which suggests that dreaming has a function in the development of simulations by forming associations between experienced, non-experienced but realistic, and even unrealistic perceptions during early childhood. The thesis further investigates some aspects of simulations and the ³inception of simulation² hypothesis by using simulated robot models based on echo state networks. These experiments suggest that it is possible for a simple robot to develop internal simulations by associating simulated perceptions and actions, and that dream-like experiences can be beneficial for the development of such simulations.
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| 39. |
- Thill, Serge, et al.
(författare)
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A computational model of cognitive interference without neural inhibitory mechanisms
- 2010
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Ingår i: Cognition in Flux. - Austin : Cognitive Science Society, Inc.. - 9781617388903 - 1617388904 ; , s. 1607-1612
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Konferensbidrag (refereegranskat)abstract
- Interference between one cognitive behavior or sensory stimulus and subsequent behaviors is a commonly observed effect in the study of human cognition and Psychology. Traditional connectionist approaches explain this phenomenon by mutually inhibiting neural populations underlying those behaviors. Here, we present an alternative model, relying on a more detailed use of synaptic dynamics, in which populations of purely excitatory neurons can nonetheless interfere with each other, causing inhibition of activation for a varying amount of time. The fundamental, biologically motivated, mechanism in the model relies on current “spilling over” from an active neural population into another one, thereby depleting the latter population’s synaptic resources. The principles underlying the model may find applications even in the design of problemsolving artificial neural networks.
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| 40. |
- Thill, Serge
(författare)
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Considerations for a Neuroscience-Inspired Approach to the Design of Artificial Intelligent Systems
- 2011
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Ingår i: Artificial General Intelligence. - Berlin, Heidelberg : Springer. - 9783642228865 - 9783642228872 ; , s. 247-254
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Konferensbidrag (refereegranskat)abstract
- When designing artificial intelligent systems, one could do worse, at first glance, than take inspiration from the system whose performance one tries to match: the human brain. The continuing failure to produce such an inspired system is usually blamed on the lack of computational power and/or a lack of understanding of the neuroscience itself. This does not, however, affect the fundamental interest in neuroscience as studying the only known mechanism to date to have produced an intelligent system.This paper adds another consideration (to the well-established observation that our knowledge of how the brain works is sketchy at best) which needs to be taken into account when taking inspiration from neuroscience: the human brain has evolved specifically to serve the human body under constraints imposed by both the body and biological limitations. This does not necessarily imply that it is futile to consider neuroscience in such endeavours; however, this paper argues that one has to view results of neuroscience from a somewhat different perspective to maximise their utility in the creation of artificial intelligent systems and proposes an explicit separation of neural processes into three categories.
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| 41. |
- Thill, Serge, et al.
(författare)
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Driver adherence to recommendations from support systems improves if the systems explain why they are given : A simulator study
- 2018
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Ingår i: Transportation Research Part F. - : Elsevier BV. - 1369-8478 .- 1873-5517. ; 56, s. 420-435
-
Tidskriftsartikel (refereegranskat)abstract
- This paper presents a large-scale simulator study on driver adherence to recommendations given 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 experimental condition, participants were either given no eco-driving recommendations, or a system whose provided support was either basic (recommendations were given in the form 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 navigation system that likewise provided either basic or informative support, depending on the condition, was also provided. Effects are measured in terms of estimated simulated fuel savings as well as engine braking/coasting behaviour and gear change efficiency. Results indicate improvements in all variables. In particular, participants who had the support of an eco-driving system spent a significantly higher proportion of the time coasting. Participants also changed gears at lower engine RPM when using an eco-driving support system, and significantly more so when the system provided justifications. Overall, the results support the notion that providing reasons why a support system puts forward a certain recommendation improves adherence to it over mere presentation of the recommendation. Finally, results indicate that participants’ driving style was less eco-friendly if the navigation system provided justifications but the eco-system did not. This may be due to participants considering the two systems as one whole rather than separate entities with individual merits. This has implications for how to design and evaluate a given driver support system since its effectiveness may depend on the performance of other systems in the vehicle.
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| 42. |
- Thill, Serge
(författare)
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Embodied neuro-cognitive integration
- 2015
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Ingår i: Proceedings of the Workshop on “Neural-Cognitive Integration” (NCI @KI 2015). - Osnabrück : Institute of Cognitive Science.
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Konferensbidrag (refereegranskat)
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| 43. |
- Thill, Serge, et al.
(författare)
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Flexible sequence learning in a SOM model of the mirror system
- 2012
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Ingår i: Building Bridges Across Cognitive Sciences Around the World. - Austin, TX : Cognitive Science Society, Inc.. - 9780976831884 ; , s. 2423-2428
-
Konferensbidrag (refereegranskat)abstract
- We present initial work on a biologically and cognitively inspired model that may allow embodied agents to autonomously learn sequences of action primitives (forming an overall behaviour). Specifically, we combine a flexible model of sequence generation with a model of parietal mirror neuron activity. The main purpose is to illustrate that the approach is viable. Although further work is needed to improve the results sketched out here, the concept is sound and relevant both to efforts in modelling mirror neuron activity and enabling artificial embodied agents to autonomously learn sequences of action primitives.
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| 44. |
- Thill, Serge, et al.
(författare)
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How to Design Emergent Models of Cognition for Application-Driven Artificial Agents
- 2016
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Ingår i: Neurocomputational Models of Cognitive Development and Processing. - Singapore : World Scientific. - 9789814699358 - 9789814699334 ; , s. 115-129
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Konferensbidrag (refereegranskat)abstract
- Emergent models of cognition are attractive for artificial cognitive agents because they overcome the brittleness of systems that are fully specified in axiomatic terms at design time, increasing, for example, the ability to deal with uncertainty and unforeseen events. When the agent is created to fulfil specific requirements defined by a given application, there is an apparent conflict between the emergent (i.e. self-defining) nature of the agent's behaviour and the pre-specified (i.e. axiomatically-defined) nature of the requirements.Here, we develop a framework for the design of emergent models of cognition whose behaviour can be shaped to fulfil application requirements while retaining the desired characteristics of emergence. We achieve this by viewing the artificial agent as forming an eco-system with the environment in which it is deployed. Consequently, the objective function that determines the agent's behaviour is cast in terms that factor in interaction with the environment (while not being controlled by it) and therefore implicitly includes the application requirements.This framework is particularly relevant to application driven research where artificial agents are designed to interact with humans in a certain manner. We illustrate this with the example of robot-enhanced therapy for children with autism spectrum disorder
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| 45. |
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| 46. |
- Thill, Serge, et al.
(författare)
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Learning New Motion Primitives in the Mirror Neuron System : A Self-organising Computational Model
- 2010
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Ingår i: From Animals to Animats 11. - Berlin, Heidelberg : Springer Berlin/Heidelberg. - 9783642151927 - 9783642151934 ; , s. 413-423
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Konferensbidrag (refereegranskat)abstract
- Computational models of the mirror (neuron) system are attractive in robotics as they may inspire novel approaches to implemente.g. action understanding. Here, we present a simple self-organising map which forms the first part of larger ongoing work in building such amodel. We show that minor modifications to the standard implementation of such a map allows it to continuously learn new motor concepts.We find that this learning is facilitated by an initial motor babbling phase, which is in line with an embodied view of cognition. Interestingly,we also find that the map is capable of reproducing neurophysiologicaldata on goal-encoding mirror neurons. Overall, our model thus fulfils the crucial requirement of being able to learn new information throughout its lifetime. Further, although conceptually simple, its behaviour has interesting parallels to both cognitive and neuroscientific evidence.
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| 47. |
- Thill, Serge, et al.
(författare)
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Memento hominibus : on the fundamental role of end users in real-world interactions with neuromorphic systems
- 2019
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Ingår i: Proceedings of the Workshop on Robust Artificial Intelligence for Neurorobotics (RAI-NR) 2019.
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Konferensbidrag (refereegranskat)abstract
- In this contribution, we briefly examine the role of end users in the evaluation and characterisation of sophisticated AI-based systems, such as autonomous vehicles or near-future robots. Indeed, when trying to ensure the safety of learning, perception and control in real world settings, one aspect that needs consideration is that human end users are often part of such settings.We argue that current approaches for considering end users in this respect are insufficient, not the least from a safety perspective, and that this insufficiency will become more acute when transitioning to neuromorphic and/or strongly cognitively inspired solutions. We demonstrate this by borrowing examples from the field of enactivism, which demonstrate that human end users might change the system dynamics of advanced neuromorphic systems when interacting with them, which needs to be taken into consideration. Enactivism might also provide clues as to how to design future evaluation metrics for human-machine teams.
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| 48. |
- Thill, Serge, et al.
(författare)
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Modeling the Development of Goal-Specificity in Mirror Neurons
- 2011
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Ingår i: Cognitive Computation. - : Springer. - 1866-9956 .- 1866-9964. ; 3:4, s. 525-538
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Tidskriftsartikel (refereegranskat)abstract
- Neurophysiological studies have shown that parietal mirror neurons encode not only actions but also the goal of these actions. Although some mirror neurons will fire whenever a certain action is perceived (goal-independently), most will only fire if the motion is perceived as part of an action with a specific goal. This result is important for the action-understanding hypothesis as it provides a potential neurological basis for such a cognitive ability. It is also relevant for the design of artificial cognitive systems, in particular robotic systems that rely on computational models of the mirror system in their interaction with other agents. Yet, to date, no computational model has explicitly addressed the mechanisms that give rise to both goal-specific and goal-independent parietal mirror neurons. In the present paper, we present a computational model based on a self-organizing map, which receives artificial inputs representing information about both the observed or executed actions and the context in which they were executed. We show that the map develops a biologically plausible organization in which goal-specific mirror neurons emerge. We further show that the fundamental cause for both the appearance and the number of goal-specific neurons can be found in geometric relationships between the different inputs to the map. The results are important to the action-understanding hypothesis as they provide a mechanism for the emergence of goal-specific parietal mirror neurons and lead to a number of predictions: (1) Learning of new goals may mostly reassign existing goal-specific neurons rather than recruit new ones; (2) input differences between executed and observed actions can explain observed corresponding differences in the number of goal-specific neurons; and (3) the percentage of goal-specific neurons may differ between motion primitives.
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| 49. |
- Thill, Serge, et al.
(författare)
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On the functional contributions of emotion mechanisms to (artificial) cognition and intelligence
- 2012
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Ingår i: Artificial General Intelligence. - Berlin, Heidelberg : Springer Berlin/Heidelberg. - 9783642355059 - 9783642355066 ; , s. 322-331
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Konferensbidrag (refereegranskat)abstract
- We argue that emotions play a central role in human cognition. It is therefore of interest to researchers with an aim to create artificial systems with human-level intelligence (or indeed beyond) to consider the functions of emotions in the human cognition whose complexity they aim to recreate. To this end, we review here several functional roles of emotions in human cognition at different levels, for instance in behavioural regulation and reinforcement learning. We discuss some of the neuroscientific and bodily underpinnings of emotions and conclude with a discussion of possible approaches, including existing efforts, to endow artificial systems with mechanisms providing some of the functions of human emotions. © 2012 Springer-Verlag.
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| 50. |
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