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Communication Models in Human–Robot Interaction: An Asymmetric MODel of ALterity in Human–Robot Interaction (AMODAL-HRI). Int J Soc Robot 2021. [DOI: 10.1007/s12369-021-00785-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
AbstractWe argue for an interdisciplinary approach that connects existing models and theories in Human–Robot Interaction (HRI) to traditions in communication theory. In this article, we review existing models of interpersonal communication and interaction models that have been applied and developed in the contexts of HRI and social robotics. We argue that often, symmetric models are proposed in which the human and robot agents are depicted as having similar ways of functioning (similar capabilities, components, processes). However, we argue that models of human–robot interaction or communication should be asymmetric instead. We propose an asymmetric interaction model called AMODAL-HRI (an Asymmetric MODel of ALterity in Human–Robot Interaction). This model is based on theory on joint action, common robot architectures and cognitive architectures, and Kincaid’s model of communication. On the basis of this model, we discuss key differences between humans and robots that influence human expectations regarding interacting with robots, and identify design implications.
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52
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Analysis of the human connectome data supports the notion of a "Common Model of Cognition" for human and human-like intelligence across domains. Neuroimage 2021; 235:118035. [PMID: 33838264 DOI: 10.1016/j.neuroimage.2021.118035] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 03/29/2021] [Accepted: 03/30/2021] [Indexed: 11/15/2022] Open
Abstract
The Common Model of Cognition (CMC) is a recently proposed, consensus architecture intended to capture decades of progress in cognitive science on modeling human and human-like intelligence. Because of the broad agreement around it and preliminary mappings of its components to specific brain areas, we hypothesized that the CMC could be a candidate model of the large-scale functional architecture of the human brain. To test this hypothesis, we analyzed functional MRI data from 200 participants and seven different tasks that cover a broad range of cognitive domains. The CMC components were identified with functionally homologous brain regions through canonical fMRI analysis, and their communication pathways were translated into predicted patterns of effective connectivity between regions. The resulting dynamic linear model was implemented and fitted using Dynamic Causal Modeling, and compared against six alternative brain architectures that had been previously proposed in the field of neuroscience (three hierarchical architectures and three hub-and-spoke architectures) using a Bayesian approach. The results show that, in all cases, the CMC vastly outperforms all other architectures, both within each domain and across all tasks. These findings suggest that a common set of architectural principles that could be used for artificial intelligence also underpins human brain function across multiple cognitive domains.
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53
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Ramirez-Pedraza R, Ramos F. Decision-making bioinspired model for target definition and “satisfactor” selection for physiological needs. COGN SYST RES 2021. [DOI: 10.1016/j.cogsys.2020.10.020] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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54
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55
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Stocco A, Prat CS, Graham LK. Individual Differences in Reward-Based Learning Predict Fluid Reasoning Abilities. Cogn Sci 2021; 45:e12941. [PMID: 33619738 DOI: 10.1111/cogs.12941] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2018] [Revised: 12/30/2020] [Accepted: 01/04/2021] [Indexed: 11/28/2022]
Abstract
The ability to reason and problem-solve in novel situations, as measured by the Raven's Advanced Progressive Matrices (RAPM), is highly predictive of both cognitive task performance and real-world outcomes. Here we provide evidence that RAPM performance depends on the ability to reallocate attention in response to self-generated feedback about progress. We propose that such an ability is underpinned by the basal ganglia nuclei, which are critically tied to both reward processing and cognitive control. This hypothesis was implemented in a neurocomputational model of the RAPM task, which was used to derive novel predictions at the behavioral and neural levels. These predictions were then verified in one neuroimaging and two behavioral experiments. Furthermore, an effective connectivity analysis of the neuroimaging data confirmed a role for the basal ganglia in modulating attention. Taken together, these results suggest that individual differences in a neural circuit related to reward processing underpin human fluid reasoning abilities.
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Affiliation(s)
- Andrea Stocco
- Department of Psychology & Institute for Learning and Brain Sciences (I-LABS), University of Washington
| | - Chantel S Prat
- Department of Psychology & Institute for Learning and Brain Sciences (I-LABS), University of Washington
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56
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Tsotsos JK, Abid O, Kotseruba I, Solbach MD. On the control of attentional processes in vision. Cortex 2021; 137:305-329. [PMID: 33677138 DOI: 10.1016/j.cortex.2021.01.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 12/17/2020] [Accepted: 01/07/2021] [Indexed: 11/26/2022]
Abstract
The study of attentional processing in vision has a long and deep history. Recently, several papers have presented insightful perspectives into how the coordination of multiple attentional functions in the brain might occur. These begin with experimental observations and the authors propose structures, processes, and computations that might explain those observations. Here, we consider a perspective that past works have not, as a complementary approach to the experimentally-grounded ones. We approach the same problem as past authors but from the other end of the computational spectrum, from the problem nature, as Marr's Computational Level would prescribe. What problem must the brain solve when orchestrating attentional processes in order to successfully complete one of the myriad possible visuospatial tasks at which we as humans excel? The hope, of course, is for the approaches to eventually meet and thus form a complete theory, but this is likely not soon. We make the first steps towards this by addressing the necessity of attentional control, examining the breadth and computational difficulty of the visuospatial and attentional tasks seen in human behavior, and suggesting a sketch of how attentional control might arise in the brain. The key conclusions of this paper are that an executive controller is necessary for human attentional function in vision, and that there is a 'first principles' computational approach to its understanding that is complementary to the previous approaches that focus on modelling or learning from experimental observations directly.
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57
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Maniadakis M, Hourdakis E, Sigalas M, Piperakis S, Koskinopoulou M, Trahanias P. Time-Aware Multi-Agent Symbiosis. Front Robot AI 2021; 7:503452. [PMID: 33501296 PMCID: PMC7805830 DOI: 10.3389/frobt.2020.503452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Accepted: 09/29/2020] [Indexed: 11/13/2022] Open
Abstract
Contemporary research in human-machine symbiosis has mainly concentrated on enhancing relevant sensory, perceptual, and motor capacities, assuming short-term and nearly momentary interaction sessions. Still, human-machine confluence encompasses an inherent temporal dimension that is typically overlooked. The present work shifts the focus on the temporal and long-lasting aspects of symbiotic human-robot interaction (sHRI). We explore the integration of three time-aware modules, each one focusing on a diverse part of the sHRI timeline. Specifically, the Episodic Memory considers past experiences, the Generative Time Models estimate the progress of ongoing activities, and the Daisy Planner devices plans for the timely accomplishment of goals. The integrated system is employed to coordinate the activities of a multi-agent team. Accordingly, the proposed system (i) predicts human preferences based on past experience, (ii) estimates performance profile and task completion time, by monitoring human activity, and (iii) dynamically adapts multi-agent activity plans to changes in expectation and Human-Robot Interaction (HRI) performance. The system is deployed and extensively assessed in real-world and simulated environments. The obtained results suggest that building upon the unfolding and the temporal properties of team tasks can significantly enhance the fluency of sHRI.
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Affiliation(s)
- Michail Maniadakis
- Institute of Computer Science, Foundation for Research and Technology - Hellas (FORTH), Heraklion, Greece
| | - Emmanouil Hourdakis
- Institute of Computer Science, Foundation for Research and Technology - Hellas (FORTH), Heraklion, Greece
| | - Markos Sigalas
- Institute of Computer Science, Foundation for Research and Technology - Hellas (FORTH), Heraklion, Greece
| | - Stylianos Piperakis
- Institute of Computer Science, Foundation for Research and Technology - Hellas (FORTH), Heraklion, Greece
| | - Maria Koskinopoulou
- Institute of Computer Science, Foundation for Research and Technology - Hellas (FORTH), Heraklion, Greece
| | - Panos Trahanias
- Institute of Computer Science, Foundation for Research and Technology - Hellas (FORTH), Heraklion, Greece
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58
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Pouncy T, Tsividis P, Gershman SJ. What Is the Model in Model-Based Planning? Cogn Sci 2021; 45:e12928. [PMID: 33398907 DOI: 10.1111/cogs.12928] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2019] [Revised: 11/17/2020] [Accepted: 11/17/2020] [Indexed: 11/28/2022]
Abstract
Flexibility is one of the hallmarks of human problem-solving. In everyday life, people adapt to changes in common tasks with little to no additional training. Much of the existing work on flexibility in human problem-solving has focused on how people adapt to tasks in new domains by drawing on solutions from previously learned domains. In real-world tasks, however, humans must generalize across a wide range of within-domain variation. In this work we argue that representational abstraction plays an important role in such within-domain generalization. We then explore the nature of this representational abstraction in realistically complex tasks like video games by demonstrating how the same model-based planning framework produces distinct generalization behaviors under different classes of task representation. Finally, we compare the behavior of agents with these task representations to humans in a series of novel grid-based video game tasks. Our results provide evidence for the claim that within-domain flexibility in humans derives from task representations composed of propositional rules written in terms of objects and relational categories.
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Affiliation(s)
- Thomas Pouncy
- Department of Psychology and Center for Brain Science, Harvard University
| | - Pedro Tsividis
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology
| | - Samuel J Gershman
- Department of Psychology and Center for Brain Science, Harvard University.,Center for Brains, Minds and Machines, Massachusetts Institute of Technology
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59
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Autonomous cognition development with lifelong learning: A self-organizing and reflecting cognitive network. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2020.09.027] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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60
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Guleva V, Shikov E, Bochenina K, Kovalchuk S, Alodjants A, Boukhanovsky A. Emerging Complexity in Distributed Intelligent Systems. ENTROPY (BASEL, SWITZERLAND) 2020; 22:E1437. [PMID: 33352754 PMCID: PMC7766450 DOI: 10.3390/e22121437] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 12/14/2020] [Accepted: 12/16/2020] [Indexed: 12/31/2022]
Abstract
Distributed intelligent systems (DIS) appear where natural intelligence agents (humans) and artificial intelligence agents (algorithms) interact, exchanging data and decisions and learning how to evolve toward a better quality of solutions. The networked dynamics of distributed natural and artificial intelligence agents leads to emerging complexity different from the ones observed before. In this study, we review and systematize different approaches in the distributed intelligence field, including the quantum domain. A definition and mathematical model of DIS (as a new class of systems) and its components, including a general model of DIS dynamics, are introduced. In particular, the suggested new model of DIS contains both natural (humans) and artificial (computer programs, chatbots, etc.) intelligence agents, which take into account their interactions and communications. We present the case study of domain-oriented DIS based on different agents' classes and show that DIS dynamics shows complexity effects observed in other well-studied complex systems. We examine our model by means of the platform of personal self-adaptive educational assistants (avatars), especially designed in our University. Avatars interact with each other and with their owners. Our experiment allows finding an answer to the vital question: How quickly will DIS adapt to owners' preferences so that they are satisfied? We introduce and examine in detail learning time as a function of network topology. We have shown that DIS has an intrinsic source of complexity that needs to be addressed while developing predictable and trustworthy systems of natural and artificial intelligence agents. Remarkably, our research and findings promoted the improvement of the educational process at our university in the presence of COVID-19 pandemic conditions.
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Affiliation(s)
| | | | | | - Sergey Kovalchuk
- National Center for Cognitive Research, ITMO University, 197101 Saint Petersburg, Russia; (V.G.); (E.S.); (K.B.); (A.A.); (A.B.)
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61
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Kelly MA, Arora N, West RL, Reitter D. Holographic Declarative Memory: Distributional Semantics as the Architecture of Memory. Cogn Sci 2020; 44:e12904. [PMID: 33140517 DOI: 10.1111/cogs.12904] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2019] [Revised: 03/30/2020] [Accepted: 08/31/2020] [Indexed: 11/29/2022]
Abstract
We demonstrate that the key components of cognitive architectures (declarative and procedural memory) and their key capabilities (learning, memory retrieval, probability judgment, and utility estimation) can be implemented as algebraic operations on vectors and tensors in a high-dimensional space using a distributional semantics model. High-dimensional vector spaces underlie the success of modern machine learning techniques based on deep learning. However, while neural networks have an impressive ability to process data to find patterns, they do not typically model high-level cognition, and it is often unclear how they work. Symbolic cognitive architectures can capture the complexities of high-level cognition and provide human-readable, explainable models, but scale poorly to naturalistic, non-symbolic, or big data. Vector-symbolic architectures, where symbols are represented as vectors, bridge the gap between the two approaches. We posit that cognitive architectures, if implemented in a vector-space model, represent a useful, explanatory model of the internal representations of otherwise opaque neural architectures. Our proposed model, Holographic Declarative Memory (HDM), is a vector-space model based on distributional semantics. HDM accounts for primacy and recency effects in free recall, the fan effect in recognition, probability judgments, and human performance on an iterated decision task. HDM provides a flexible, scalable alternative to symbolic cognitive architectures at a level of description that bridges symbolic, quantum, and neural models of cognition.
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Affiliation(s)
- Mary Alexandria Kelly
- Department of Computer Science, Bucknell University
- College of Information Sciences and Computing, The Pennsylvania State University
| | - Nipun Arora
- Department of Cognitive Science, Carleton University
| | - Robert L West
- Department of Cognitive Science, Carleton University
| | - David Reitter
- College of Information Sciences and Computing, The Pennsylvania State University
- Google Research
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62
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Built Environment Evaluation in Virtual Reality Environments—A Cognitive Neuroscience Approach. URBAN SCIENCE 2020. [DOI: 10.3390/urbansci4040048] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
To date, the predominant tools for the evaluation of built environment quality and impact have been surveys, scorecards, or verbal comments—approaches that rely upon user-reported responses. The goal of this research project is to develop, test, and validate a data-driven approach for built environment quality evaluation/validation based upon measurement of real-time emotional responses to simulated environments. This paper presents an experiment that was conducted by combining an immersive virtual environment (virtual reality) and electroencephalogram (EEG) as a tool to evaluate Pre and Post Purple Line development. More precisely, the objective was to (a) develop a data-driven approach for built environment quality evaluation and (b) understand the correlation between the built environment characters and emotional state. The preliminary validation of the proposed evaluation method identified discrepancies between traditional evaluation results and emotion response indications through EEG signals. The validation and findings have laid a foundation for further investigation of relations between people’s general cognitive and emotional responses in evaluating built environment quality and characters.
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63
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Morita J, Miwa K, Maehigashi A, Terai H, Kojima K, Ritter FE. Cognitive Modeling of Automation Adaptation in a Time Critical Task. Front Psychol 2020; 11:2149. [PMID: 33123033 PMCID: PMC7566173 DOI: 10.3389/fpsyg.2020.02149] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2020] [Accepted: 07/31/2020] [Indexed: 11/13/2022] Open
Abstract
This paper presents a cognitive model that simulates an adaptation process to automation in a time-critical task. The paper uses a simple tracking task (which represents vehicle operation) to reveal how the reliance on automation changes as the success probabilities of the automatic and manual mode vary. The model was developed by using a cognitive architecture, ACT-R (Adaptive Control of Thought-Rational). We also introduce two methods of reinforcement learning: the summation of rewards over time and a gating mechanism. The model performs this task through productions that manage perception and motor control. The utility values of these productions are updated based on rewards in every perception-action cycle. A run of this model simulated the overall trends of the behavioral data such as the performance (tracking accuracy), the auto use ratio, and the number of switches between the two modes, suggesting some validity of the assumptions made in our model. This work shows how combining different paradigms of cognitive modeling can lead to practical representations and solutions to automation and trust in automation.
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Affiliation(s)
- Junya Morita
- Department of Behavior Informatics, Faculty of Informatics, Shizuoka University, Hamamatsu, Japan
| | - Kazuhisa Miwa
- Department of Cognitive and Psychological Sciences, Graduate School of Informatics, Nagoya University, Nagoya, Japan
| | - Akihiro Maehigashi
- Center for Research and Development in Admissions, Shizuoka University, Shizuoka, Japan
| | - Hitoshi Terai
- Department of Information and Computer Sciences, Faculty of Humanity-Oriented Science and Engineering, Kinki University, Fukuoka, Japan
| | - Kazuaki Kojima
- Learning Technology Laboratory, Teikyo University, Tochigi, Japan
| | - Frank E. Ritter
- College of Information Sciences and Technology, Pennsylvania State University, University Park, PA, United States
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64
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Banerjee R, Pal SK. Z
*-Numbers, Data Structures, and Thinking in Machine-Mind Architecture. IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2020. [DOI: 10.1109/tetci.2019.2935539] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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65
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Modeling human thinking about similarities by neuromatrices in the perspective of fuzzy logic. Neural Comput Appl 2020. [DOI: 10.1007/s00521-020-05363-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
AbstractIn this work, we propose a new method for modeling human reasoning about objects’ similarities. We assume that similarity depends on perceived intensities of objects’ attributes expressed by natural language expressions such as low, medium, and high. We show how to find the underlying structure of the matrix with intensities of objects’ similarities in the factor-analysis-like manner. The demonstrated approach is based on fuzzy logic and set theory principles, and it uses only maximum and minimum operators. Similarly to classic eigenvector decomposition, we aim at representing the initial linguistic ordinal-scale (LOS) matrix as a max–min product of other LOS matrix and its transpose. We call this reconstructing matrix a neuromatrix because we assume that such a process takes place at the neural level in our brain. We show and discuss on simple, illustrative examples, how the presented way of modeling grasps natural way of reasoning about similarities. The unique characteristics of our approach are treating smaller attribute intensities as less important in making decisions about similarities. This feature is consistent with how the human brain is functioning at a biological level. A neuron fires and passes information further only if input signals are strong enough. The proposal of the heuristic algorithm for finding the decomposition in practice is also introduced and applied to exemplary data from classic psychological studies on perceived similarities between colors and between nations. Finally, we perform a series of simulation experiments showing the effectiveness of the proposed heuristic.
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66
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Rossi S, Rossi A, Dautenhahn K. The Secret Life of Robots: Perspectives and Challenges for Robot’s Behaviours During Non-interactive Tasks. Int J Soc Robot 2020. [DOI: 10.1007/s12369-020-00650-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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67
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Kattepur A, Purushotaman B. RoboPlanner
: a pragmatic task planning framework for autonomous robots. COGNITIVE COMPUTATION AND SYSTEMS 2020. [DOI: 10.1049/ccs.2019.0025] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
Affiliation(s)
- Ajay Kattepur
- Embedded Systems and Robotics, TCS Research & InnovationIndia
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68
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A bio-inspired model of behavior considering decision-making and planning, spatial attention and basic motor commands processes. COGN SYST RES 2020. [DOI: 10.1016/j.cogsys.2019.10.006] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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69
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70
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Miyazawa K, Horii T, Aoki T, Nagai T. Integrated Cognitive Architecture for Robot Learning of Action and Language. Front Robot AI 2019; 6:131. [PMID: 33501146 PMCID: PMC7805838 DOI: 10.3389/frobt.2019.00131] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2019] [Accepted: 11/13/2019] [Indexed: 11/13/2022] Open
Abstract
The manner in which humans learn, plan, and decide actions is a very compelling subject. Moreover, the mechanism behind high-level cognitive functions, such as action planning, language understanding, and logical thinking, has not yet been fully implemented in robotics. In this paper, we propose a framework for the simultaneously comprehension of concepts, actions, and language as a first step toward this goal. This can be achieved by integrating various cognitive modules and leveraging mainly multimodal categorization by using multilayered multimodal latent Dirichlet allocation (mMLDA). The integration of reinforcement learning and mMLDA enables actions based on understanding. Furthermore, the mMLDA, in conjunction with grammar learning and based on the Bayesian hidden Markov model (BHMM), allows the robot to verbalize its own actions and understand user utterances. We verify the potential of the proposed architecture through experiments using a real robot.
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Affiliation(s)
- Kazuki Miyazawa
- Graduate School of Engineering Science, Osaka University, Osaka, Japan
| | - Takato Horii
- Graduate School of Engineering Science, Osaka University, Osaka, Japan
| | - Tatsuya Aoki
- Graduate School of Engineering Science, Osaka University, Osaka, Japan.,Graduate School of Informatics and Engineering, The University of Electro-Communications, Tokyo, Japan
| | - Takayuki Nagai
- Graduate School of Engineering Science, Osaka University, Osaka, Japan.,Artificial Intelligence Exploration Research Center, The University of Electro-Communications, Tokyo, Japan
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71
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Zhukova NA. General and Specific Problems of Multilevel Synthesis of Models of Monitoring Objects. AUTOMATIC DOCUMENTATION AND MATHEMATICAL LINGUISTICS 2019. [DOI: 10.3103/s0005105519060049] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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72
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Sotnik G. The Doubly-Bounded Rationality of an Artificial Agent and its Ability to Represent the Bounded Rationality of a Human Decision-Maker in Policy-Relevant Situations. J EXP THEOR ARTIF IN 2019. [DOI: 10.1080/0952813x.2019.1672797] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Affiliation(s)
- Garry Sotnik
- Systems Science Program, Portland State University, Portland, OR, USA
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73
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Rosenbloom PS, Forbus KD. Expanding and Repositioning Cognitive Science. Top Cogn Sci 2019; 11:918-927. [DOI: 10.1111/tops.12468] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2019] [Revised: 09/16/2019] [Accepted: 09/16/2019] [Indexed: 01/31/2023]
Affiliation(s)
- Paul S. Rosenbloom
- Department of Computer Science & Institute for Creative Technologies University of Southern California
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