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Journal articles on the topic 'Situated Intelligence'

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Published: 10 December 2022
Last updated: 28 January 2023

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1

Timpka, Toomas. "Situated clinical cognition." Artificial Intelligence in Medicine 7, no. 5 (October 1995): 387–94. http://dx.doi.org/10.1016/0933-3657(95)00011-t.

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2

Andrade Da Silva, Kézia, and Amarolinda Klein. "The Development of Cultural Intelligence in Situated Learning." Academy of Management Proceedings 2020, no. 1 (August 2020): 21535. http://dx.doi.org/10.5465/ambpp.2020.21535abstract.

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3

Downing, Keith L. "Neuroscientific implications for situated and embodied artificial intelligence." Connection Science 19, no. 1 (March 2007): 75–104. http://dx.doi.org/10.1080/09540090701192584.

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4

Bandini, S., S. Manzoni, and C. Simone. "Heterogeneous Agents Situated in Heterogeneous Spaces." Applied Artificial Intelligence 16, no. 9-10 (October 2002): 831–52. http://dx.doi.org/10.1080/08839510290030516.

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5

Bryan, Victoria M., and John D. Mayer. "Are People-Centered Intelligences Psychometrically Distinct from Thing-Centered Intelligences? A Meta-Analysis." Journal of Intelligence 9, no. 4 (September 30, 2021): 48. http://dx.doi.org/10.3390/jintelligence9040048.

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The Cattell–Horn–Carroll (CHC) or three-stratum model of intelligence envisions human intelligence as a hierarchy. General intelligence (g) is situated at the top, under which are a group of broad intelligences such as verbal, visuospatial processing, and quantitative knowledge that pertain to more specific areas of reasoning. Some broad intelligences are people-centered, including personal, emotional, and social intelligences; others concern reasoning about things more generally, such as visuospatial and quantitative knowledge. In the present research, we conducted a meta-analysis of 87 studies, including 2322 effect sizes, to examine the average correlation between people-to-people intelligences relative to the average correlation between people-to-thing-centered intelligences (and similar comparisons). Results clearly support the psychometric distinction between people-centered and thing-centered mental abilities. Coupled with evidence for incremental predictions from people-centered intelligences, our findings provide a secure foundation for continued research focused on people-centered mental abilities.
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6

Clancey, William J. "Situated Cognition: Stepping out of Representational Flatland." AI Communications 4, no. 2-3 (1991): 109–12. http://dx.doi.org/10.3233/aic-1991-42-309.

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7

Barsalou, Lawrence W. "Simulation, situated conceptualization, and prediction." Philosophical Transactions of the Royal Society B: Biological Sciences 364, no. 1521 (May 12, 2009): 1281–89. http://dx.doi.org/10.1098/rstb.2008.0319.

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Based on accumulating evidence, simulation appears to be a basic computational mechanism in the brain that supports a broad spectrum of processes from perception to social cognition. Further evidence suggests that simulation is typically situated, with the situated character of experience in the environment being reflected in the situated character of the representations that underlie simulation. A basic architecture is sketched of how the brain implements situated simulation. Within this framework, simulators implement the concepts that underlie knowledge, and situated conceptualizations capture patterns of multi-modal simulation associated with frequently experienced situations. A pattern completion inference mechanism uses current perception to activate situated conceptualizations that produce predictions via simulations on relevant modalities. Empirical findings from perception, action, working memory, conceptual processing, language and social cognition illustrate how this framework produces the extensive prediction that characterizes natural intelligence.
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8

Chai, Joyce Y., Rui Fang, Changsong Liu, and Lanbo She. "Collaborative Language Grounding Toward Situated Human-Robot Dialogue." AI Magazine 37, no. 4 (January 17, 2017): 32–45. http://dx.doi.org/10.1609/aimag.v37i4.2684.

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To enable situated human-robot dialogue, techniques to support grounded language communication are essential. One particular challenge is to ground human language to robot internal representation of the physical world. Although copresent in a shared environment, humans and robots have mismatched capabilities in reasoning, perception, and action. Their representations of the shared environment and joint tasks are significantly misaligned. Humans and robots will need to make extra effort to bridge the gap and strive for a common ground of the shared world. Only then, is the robot able to engage in language communication and joint tasks. Thus computational models for language grounding will need to take collaboration into consideration. A robot not only needs to incorporate collaborative effort from human partners to better connect human language to its own representation, but also needs to make extra collaborative effort to communicate its representation in language that humans can understand. To address these issues, the Language and Interaction Research group (LAIR) at Michigan State University has investigated multiple aspects of collaborative language grounding. This article gives a brief introduction to this research effort and discusses several collaborative approaches to grounding language to perception and action.
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9

WEYNS, DANNY, ELKE STEEGMANS, and TOM HOLVOET. "TOWARDS ACTIVE PERCEPTION IN SITUATED MULTI-AGENT SYSTEMS." Applied Artificial Intelligence 18, no. 9-10 (October 2004): 867–83. http://dx.doi.org/10.1080/08839510490509063.

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10

Wells, Andrew. "Situated action, symbol systems and universal computation." Minds and Machines 6, no. 1 (February 1996): 33–46. http://dx.doi.org/10.1007/bf00388916.

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11

MacLennan, Alan. "The artificial life route to artificial intelligence: Building embodied, situated agents." Journal of the American Society for Information Science 47, no. 6 (June 1996): 482–83. http://dx.doi.org/10.1002/(sici)1097-4571(199606)47:6<482::aid-asi14>3.0.co;2-0.

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12

Vera, Alonso H., and Herbert A. Simon. "Situated Action: A Symbolic Interpretation." Cognitive Science 17, no. 1 (January 1993): 7–48. http://dx.doi.org/10.1207/s15516709cog1701_2.

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13

Vera, Alonso H., and Herbert A. Simon. "Situated Action: Reply to Reviewers." Cognitive Science 17, no. 1 (January 1993): 77–86. http://dx.doi.org/10.1207/s15516709cog1701_6.

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14

CLARK, ANDY. "EDITORIAL Representation, Development and Situated Connectionism." Connection Science 4, no. 3-4 (January 1992): 171–74. http://dx.doi.org/10.1080/09540099208946613.

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15

SAUNDERS, ROB, and JOHN S. GERO. "Curious agents and situated design evaluations." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 18, no. 2 (May 2004): 153–61. http://dx.doi.org/10.1017/s0890060404040119.

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This paper presents a possible future direction for agent-based simulation using complex agents that can learn from experience and report their individual evaluations. Adding learning to the agent model permits the simulation of potentially important agent behavior such as curiosity. The agents can then report evaluations of a design that are situated in their individual experience. The paper describes the architecture of curious agents used in the situated evaluation of designs. It then describes an example of the application of such curious agents in the evaluation of the curating of an exhibition in an art gallery.
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16

LIEW, PAK-SAN, and JOHN S. GERO. "Constructive memory for situated design agents." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 18, no. 2 (May 2004): 163–98. http://dx.doi.org/10.1017/s0890060404040120.

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Design is situated. “Situatedness” in designing entails the explicit consideration of the state of the environment, the knowledge and experiences of the designer, and the interactions between the designer and the environment during designing. Central to the notion of situatedness is the notion of design situation and constructive memory. A design situation models a particular state of interaction between a design agent and the environment at a particular point in time. Memory construction occurs whenever a design agent uses past experiences and knowledge within the current design environment in a situated manner. This paper is concerned with the development of an agent-based computational design tool that takes into consideration the notion of situatedness in designing. A key element of this tool is a constructive memory system that supports the dynamic nature of designing. Memories of past experiences are constructed as required by the current situation, and past experiences are refined for future utility according to the current interactions between the agent and the environment. This latter case of knowledge improvement is illustrated through a series of experiments that demonstrates the effect of grounding on the operating modes and responses of a constructive memory system.
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17

Gero, John S., and Udo Kannengiesser. "An ontology of situated design teams." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 21, no. 3 (August 2007): 295–308. http://dx.doi.org/10.1017/s0890060407000297.

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AbstractThis paper presents an ontological framework for situated design teams in which the team is both the subject and the object of designing. Team designing is modeled using the set of processes provided by the situated function–behavior–structure framework. This is a formal basis for understanding the drivers for change in the product to be designed and in the design team. We specifically focus on changes in a team's structure that emerge from interactions among individual team members and subteams.
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18

Da Rold, Federico. "Information-theoretic decomposition of embodied and situated systems." Neural Networks 103 (July 2018): 94–107. http://dx.doi.org/10.1016/j.neunet.2018.03.011.

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19

Kelly, Nick, and John S. Gero. "Situated interpretation in computational creativity." Knowledge-Based Systems 80 (May 2015): 48–57. http://dx.doi.org/10.1016/j.knosys.2014.12.005.

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20

Qu, S., and J. Y. Chai. "Context-based Word Acquisition for Situated Dialogue in a Virtual World." Journal of Artificial Intelligence Research 37 (March 11, 2010): 247–77. http://dx.doi.org/10.1613/jair.2912.

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To tackle the vocabulary problem in conversational systems, previous work has applied unsupervised learning approaches on co-occurring speech and eye gaze during interaction to automatically acquire new words. Although these approaches have shown promise, several issues related to human language behavior and human-machine conversation have not been addressed. First, psycholinguistic studies have shown certain temporal regularities between human eye movement and language production. While these regularities can potentially guide the acquisition process, they have not been incorporated in the previous unsupervised approaches. Second, conversational systems generally have an existing knowledge base about the domain and vocabulary. While the existing knowledge can potentially help bootstrap and constrain the acquired new words, it has not been incorporated in the previous models. Third, eye gaze could serve different functions in human-machine conversation. Some gaze streams may not be closely coupled with speech stream, and thus are potentially detrimental to word acquisition. Automated recognition of closely-coupled speech-gaze streams based on conversation context is important. To address these issues, we developed new approaches that incorporate user language behavior, domain knowledge, and conversation context in word acquisition. We evaluated these approaches in the context of situated dialogue in a virtual world. Our experimental results have shown that incorporating the above three types of contextual information significantly improves word acquisition performance.
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21

Vera, Alonso H., and Herbert A. Simon. "Situated Action: Reply to William Clancey." Cognitive Science 17, no. 1 (January 1993): 117–33. http://dx.doi.org/10.1207/s15516709cog1701_8.

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22

Wortham, Stanton. "Interactionally situated cognition: a classroom example." Cognitive Science 25, no. 1 (January 2001): 37–66. http://dx.doi.org/10.1207/s15516709cog2501_3.

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23

Shirouzu, Hajime, Naomi Miyake, and Hiroyuki Masukawa. "Cognitively active externalization for situated reflection." Cognitive Science 26, no. 4 (July 2002): 469–501. http://dx.doi.org/10.1207/s15516709cog2604_3.

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24

Zuo, Xiang, Naoto Iwahashi, Kotaro Funakoshi, Mikio Nakano, Ryo Taguchi, Shigeki Matsuda, Komei Sugiura, and Natsuki Oka. "Detecting Robot-Directed Speech by Situated Understanding in Physical Interaction." Transactions of the Japanese Society for Artificial Intelligence 25 (2010): 670–82. http://dx.doi.org/10.1527/tjsai.25.670.

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25

Swann, Philip. "Clancey on Situated Cognition: Throw out the Baby, but Keep the Bathwater." AI Communications 4, no. 2-3 (1991): 107–9. http://dx.doi.org/10.3233/aic-1991-42-308.

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26

Koenig, Sven, and Xiaoxun Sun. "Comparing real-time and incremental heuristic search for real-time situated agents." Autonomous Agents and Multi-Agent Systems 18, no. 3 (September 5, 2008): 313–41. http://dx.doi.org/10.1007/s10458-008-9061-x.

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27

Penny, Simon. "Art and robotics: sixty years of situated machines." AI & SOCIETY 28, no. 2 (March 15, 2012): 147–56. http://dx.doi.org/10.1007/s00146-012-0404-4.

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28

Summers, Jamie. "Trust Through Intelligence?" Contemporary Challenges: The Global Crime, Justice and Security Journal 1 (September 14, 2020): 108–25. http://dx.doi.org/10.2218/ccj.v1.4946.

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It would be understandable to view community policing and counter-terrorism policing as two distinct concepts which are intrinsically situated at opposites poles in the world of policing. While one invokes cultures such as transparency, public engagement and visibility, the other is traditionally considered as a series of clandestine operations more akin to the intelligence-led policing model. This paper will argue that the two policing strategies are in fact compatible. With a shift in the nature of terrorism itself apparent, the contemporary lone wolf attacker is not only a deadly threat, but one which is incredibly difficult to detect using methods such as background checks and covert investigation. This is due to a lack of communication and/or physical ties between attackers, a high level of isolation stemming from affinity to extremist ideologies, combined with self-struggle and anger. In order to combat prospective attacks, effective preventative measures must be implemented in both geographical and social spaces. Such measures warrant the implementation of community policing philosophies which can help establish trust and promote co-operation, leading to accurate, reliable community intelligence, as well as reassurance and security for the members of the community itself.
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29

CODDINGTON, ALEXANDRA M., and MICHAEL LUCK. "A MOTIVATION-BASED PLANNING AND EXECUTION FRAMEWORK." International Journal on Artificial Intelligence Tools 13, no. 01 (March 2004): 5–25. http://dx.doi.org/10.1142/s0218213004001399.

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AI planning systems tend to be disembodied and are not situated within the environment for which plans are generated, thus losing information concerning the interaction between the system and its environment. This paper argues that such information may potentially be valuable in constraining plan formulation, and presents both an agent- and domain-independent architecture that extends the classical AI planning framework to take into account context, or the interaction between an autonomous situated planning agent and its environment. The paper describes how context constrains the goals an agent might generate, enables those goals to be prioritised, and constrains plan selection.
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30

Gorniak, Peter, and Deb Roy. "Situated Language Understanding as Filtering Perceived Affordances." Cognitive Science 31, no. 2 (March 4, 2007): 197–231. http://dx.doi.org/10.1080/15326900701221199.

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31

Bianchi, N., P. Bottoni, P. Mussio, C. Spinu, and C. Garbay. "Situated Image Understanding in a Multiagent Framework." International Journal of Pattern Recognition and Artificial Intelligence 12, no. 05 (August 1998): 595–624. http://dx.doi.org/10.1142/s021800149800035x.

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The paper addresses the problem of controlling situated image understanding processes. Two complementary control styles are considered and applied cooperatively, a deliberative one and a reactive one. The role of deliberative control is to account for the unpredictability of situations, by dynamically determining which strategies to pursue, based on the results obtained so far and more generally on the state of the understanding process. The role of reactive control is to account for the variability of local properties of the image by tuning operations to subimages, each one being homogeneous with respect to a given operation. A variable organization of agents is studied to face this variability. The two control modes are integrated into a unified formalism describing segmentation and interpretation activities. A feedback from high level interpretation tasks to low level segmentation tasks thus becomes possible and is exploited to recover wrong segmentations. Preliminary results in the field of liver biopsy image understanding are shown to demonstrate the potential of the approach.
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32

Marge, Matthew, and Alexander I. Rudnicky. "Miscommunication Detection and Recovery in Situated Human–Robot Dialogue." ACM Transactions on Interactive Intelligent Systems 9, no. 1 (March 2019): 1–40. http://dx.doi.org/10.1145/3237189.

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33

Bandini, Stefania, Mizar Luca Federici, and Giuseppe Vizzari. "SITUATED CELLULAR AGENTS APPROACH TO CROWD MODELING AND SIMULATION." Cybernetics and Systems 38, no. 7 (August 28, 2007): 729–53. http://dx.doi.org/10.1080/01969720701534141.

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34

Huynh, Brandon, Adam Ibrahim, Yun Suk Chang, Tobias Höllerer, and John O’Donovan. "User Perception of Situated Product Recommendations in Augmented Reality." International Journal of Semantic Computing 13, no. 03 (September 2019): 289–310. http://dx.doi.org/10.1142/s1793351x19400129.

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Augmented reality (AR) interfaces increasingly utilize artificial intelligence systems to tailor content and experiences to the user. We explore the effects of one such system — a recommender system for online shopping — which allows customers to view personalized product recommendations in the physical spaces where they might be used. We describe results of a [Formula: see text] condition exploratory study in which recommendation quality was varied across three user interface types. Our results highlight potential differences in user perception of the recommended objects in an AR environment. Specifically, users rate product recommendations significantly higher in AR and in a 3D browser interface, and show a significant increase in trust in the recommender system, compared to a web interface with 2D product images. Through semi-structured interviews, we gather participant feedback which suggests AR interfaces perform better due to their ability to view products within the physical context where they will be used.
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35

Huffman, S. B., and J. E. Laird. "Flexibly Instructable Agents." Journal of Artificial Intelligence Research 3 (November 1, 1995): 271–324. http://dx.doi.org/10.1613/jair.150.

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This paper presents an approach to learning from situated, interactive tutorial instruction within an ongoing agent. Tutorial instruction is a flexible (and thus powerful) paradigm for teaching tasks because it allows an instructor to communicate whatever types of knowledge an agent might need in whatever situations might arise. To support this flexibility, however, the agent must be able to learn multiple kinds of knowledge from a broad range of instructional interactions. Our approach, called situated explanation, achieves such learning through a combination of analytic and inductive techniques. It combines a form of explanation-based learning that is situated for each instruction with a full suite of contextually guided responses to incomplete explanations. The approach is implemented in an agent called Instructo-Soar that learns hierarchies of new tasks and other domain knowledge from interactive natural language instructions. Instructo-Soar meets three key requirements of flexible instructability that distinguish it from previous systems: (1) it can take known or unknown commands at any instruction point; (2) it can handle instructions that apply to either its current situation or to a hypothetical situation specified in language (as in, for instance, conditional instructions); and (3) it can learn, from instructions, each class of knowledge it uses to perform tasks.
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36

RASHID, ASMA, MASUD AKHTAR, and MUHAMMAD NAVEED RIAZ. "Relationship between Emotional Intelligence, Emotion Regulation and Emotional Expressivity in Employees." International Review of Management and Business Research 10, no. 1 (March 7, 2021): 361–66. http://dx.doi.org/10.30543/10-1(2021)-31.

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The study examined the relationship between emotional intelligence, emotional regulation and emotional expressivity in employees. Present study was descriptive research based on survey research design conducted in various offices situated in Sargodha. Data from A purposive sample of employees (N =200) was collected using three scales. Results revealed significant relationship between emotional intelligence, emotional regulation and emotional expressivity in employees.: The finding has theoretical and applied significant in term of relationship between three interrelated constructs based on management of emotions at workplace. Keywords: Emotional Expressivity, Emotional Regulation, Emotional Intelligence, Employees.
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37

Santos, Bruno, Xabier Barandiaran, Philip Husbands, Miguel Aguilera, and Manuel Bedia. "Sensorimotor coordination and metastability in a situated HKB model." Connection Science 24, no. 4 (December 2012): 143–61. http://dx.doi.org/10.1080/09540091.2013.770821.

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38

Katai, Osamu, Toyoaki Nishida, and Renate Fruchter. "Situated and embodied interactions for symbiotic and inclusive societies." AI & SOCIETY 26, no. 3 (October 21, 2010): 193–96. http://dx.doi.org/10.1007/s00146-010-0302-6.

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39

Reboul, Anne. "Decoupling, situated cognition and immersion in art." Cognitive Processing 16, S1 (July 31, 2015): 355–58. http://dx.doi.org/10.1007/s10339-015-0721-x.

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40

Jaszczolt, K. M. "Situated temporal reference: A case for compositional pragmatics?" Journal of Pragmatics 42, no. 11 (November 2010): 2898–909. http://dx.doi.org/10.1016/j.pragma.2010.06.007.

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41

DETHLEFS, NINA, and HERIBERTO CUAYÁHUITL. "Hierarchical reinforcement learning for situated natural language generation." Natural Language Engineering 21, no. 3 (January 10, 2014): 391–435. http://dx.doi.org/10.1017/s1351324913000375.

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AbstractNatural Language Generation systems in interactive settings often face a multitude of choices, given that the communicative effect of each utterance they generate depends crucially on the interplay between its physical circumstances, addressee and interaction history. This is particularly true in interactive and situated settings. In this paper we present a novel approach forsituated Natural Language Generationin dialogue that is based onhierarchical reinforcement learningand learns the best utterance for a context by optimisation through trial and error. The model is trained from human–human corpus data and learns particularly to balance the trade-off betweenefficiencyanddetailin giving instructions: the user needs to be given sufficient information to execute their task, but without exceeding their cognitive load. We present results from simulation and a task-based human evaluation study comparing two different versions of hierarchical reinforcement learning: One operates using a hierarchy of policies with a large state space and local knowledge, and the other additionally shares knowledge across generation subtasks to enhance performance. Results show that sharing knowledge across subtasks achieves better performance than learning in isolation, leading to smoother and more successful interactions that are better perceived by human users.
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42

Patel, Nandish V. "Deferred system's design: Situated system requirements gathering with Hyper-Tmodeller." AI & Society 15, no. 4 (December 2001): 316–43. http://dx.doi.org/10.1007/bf01206113.

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43

P, Sreeja, and Nalinilatha M. "A STUDY ON RELATIONSHIP BETWEEN SOCIAL INTELLIGENCE AND ACADEMIC ACHIEVEMENT OF HIGHER SECONDARY STUDENTS." International Journal of Research -GRANTHAALAYAH 5, no. 6 (June 30, 2017): 476–88. http://dx.doi.org/10.29121/granthaalayah.v5.i6.2017.2059.

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The study aimed to identify the relationship between social intelligence and academic achievement of higher secondary students. The investigator adopted survey method to study the social intelligence between students from selected government, private and aided school. For this study a sample of 300 school students from eight various schools which are situated in Palakkad district selected by the investigator using simple random sampling technique. The findings revealed that there is no significant relationship between social intelligence and academic achievement. This study shows that students do not find healthy environment in schools for developing their social intelligence. The schools fail to provide a proper environment to develop better relationship, positive behavior, social skills positive attitudes and good mental health in students.
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44

Albertson, Dan, and Charles Meadows. "Situated topic complexity in interactive video retrieval." Journal of the American Society for Information Science and Technology 62, no. 9 (May 27, 2011): 1676–95. http://dx.doi.org/10.1002/asi.21573.

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45

Arrabales Moreno, Raúl, and Araceli Sanchis de Miguel. "Applying machine consciousness models in autonomous situated agents." Pattern Recognition Letters 29, no. 8 (June 2008): 1033–38. http://dx.doi.org/10.1016/j.patrec.2007.09.002.

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46

Urdiales, Cristina, and Ulises Cortés. "Situated robotics: from learning to teaching by imitation." Cognitive Processing 6, no. 3 (July 21, 2005): 196–201. http://dx.doi.org/10.1007/s10339-005-0004-z.

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47

Costa, Fernando Almeida e., and Luis Mateus Rocha. "Introduction to the Special Issue: Embodied and Situated Cognition." Artificial Life 11, no. 1-2 (January 2005): 5–11. http://dx.doi.org/10.1162/1064546053279035.

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48

Thornborrow, Joanna. "Narrative, opinion and situated argument in talk show discourse." Journal of Pragmatics 39, no. 8 (August 2007): 1436–53. http://dx.doi.org/10.1016/j.pragma.2007.04.001.

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49

Hearn, Jake. "‘A Library is a Growing Organism’: Redefining Artificial Intelligence and the Role of the Information Professional in the Corporate Legal World." Legal Information Management 22, no. 2 (June 2022): 81–87. http://dx.doi.org/10.1017/s1472669622000159.

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AbstractThis article, written by Jake Hearn, seeks to define and reevaluate artificial intelligence (AI) in the context of the corporate legal world. The article explores some of the opportunities on offer to information professionals to ensure that the profession continues to grow within the economic, cultural and professional context it is situated.
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50

Dinka, David, James M. Nyce, and Toomas Timpka. "Situated cognition in clinical visualization: The role of transparency in GammaKnife neurosurgery planning." Artificial Intelligence in Medicine 46, no. 2 (June 2009): 111–18. http://dx.doi.org/10.1016/j.artmed.2008.11.003.

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