Academic literature on the topic 'Situated Intelligence'
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Journal articles on the topic "Situated Intelligence"
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.
Full textAndrade 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.
Full textDowning, 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.
Full textBandini, 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.
Full textBryan, 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.
Full textClancey, 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.
Full textBarsalou, 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.
Full textChai, 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.
Full textWEYNS, 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.
Full textWells, 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.
Full textDissertations / Theses on the topic "Situated Intelligence"
Mitchell, Matthew Winston 1968. "An architecture for situated learning agents." Monash University, School of Computer Science and Software Engineering, 2003. http://arrow.monash.edu.au/hdl/1959.1/5553.
Full textChu, Rita CM. "An apprenticeship in mask making: situated cognition, situated learning, and tool acquisition in the context of Chinese Dixi mask making." The Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=osu1158693508.
Full textRihawi, Omar. "Modelling and simulation of distributed large scale situated multi-agent systems." Thesis, Lille 1, 2014. http://www.theses.fr/2014LIL10148/document.
Full textThis thesis aims to design a distributed large scale MAS simulation. When the number of agents reaches several millions, it is necessary to distribute MAS simulation. However, this can raise some issues: agents allocation, interactions from different machines, time management, etc. When we distribute MAS simulation on different machines, agents must be separated between these machines and should still be able to produce their normal behaviours. Our distribution is able to cover all agents' perceptions during the simulation and allow all agents to interact normally. Moreover, with large-scale simulations the main observations are done on the macroscopic level. In this thesis, we study two main aspects to distribute large-scale simulations. The first aspect is the efficient strategy that can be used to distribute MAS concepts (agents and environment). We propose two efficient distribution approaches: agents distribution and environment distribution. The second aspect is the relaxation of synchronization constraints in order to speed up the execution of large-scale simulations. Relaxing this constraint can induce incoherent interactions, which do not exist in a synchronized context. But, in some applications that can not affect the macroscopic level. Our experiments on different categories of MAS applications show that some applications can be distributed efficiently in one distribution approach more than the other. In addition, we have studied the impact of incoherent iterations on the emerging behaviour of different applications, and we have evidenced situations in which unsynchronized simulations still produced the expected macroscopic behaviour
Afoutni, Zoubida. "Un modèle multi-agents pour la représentation de l'action située basé sur l'affordance et la stigmergie." Thesis, La Réunion, 2015. http://www.theses.fr/2015LARE0027/document.
Full textSimulation modelling of complex systems nowadays is an ideal solution to get a good understanding of these systems. In effect, compared with real experiments in the field of studies considered, virtual experiments allow one to quickly answer questions about these systems and provide solutions within a delay well adapted to their actual context. This thesis deals with the issue of human action representation, accounting with its temporal and spatial dimensions at individual and collective levels. This question has already been addressed in the field of Artificial intelligence in general and in the one of Agricultural systems in particular, the latter being the application domain of this thesis. The models proposed to date were mainly based upon the theory of planned action, explicitly accounting with the temporal dimension of action only. The main limits of these models lie in their complexity, because the ability to predict all future changes in actors' behaviors is far too difficult. This difficulty leads to the need of frequently re-planning the course of actions in order to get consistent results. The second drawback lies in the discrepancy that may arise between the results of simulated actions and actual observations. In effect, real actors do not realize systematically the actions they forecast according to the situations they actually encounter. In order to overcome the limits of planning models, we developed a model of human action based on the theory of situated action. Action is there viewed as a process endowed with a temporal thickness and emerging from the situations created by the interaction, through time and space, between the actor and its environment. Our model combines the concepts of affordance and stigmergy as well as the notion of emergence. Therefore we propose a multi-agents system within which space is explicitly represented and partitioned into a set of “places”. The control of each place is left to an abstract agent standing for an observer capable of detecting the affordances occurring on its place and trigger appropriate actions. Actors as well as passive objects are represented as “environmental entities”. These entities carry information about their capacity of performing or undergoing actions. This information allows the agents to detect affordances thanks to the meta-knowledge they hold. Once detected, these affordances are reified in the environment to be used to determine the action that will eventually be executed. Coordination of actions, at the collective level, is performed through stigmergy: the agents communicate implicitly between them using a set of marks as a metaphor of pheromons in ant colonies. To prove the relevance of the proposed model, a software prototype, applied to the domain of agricultural production systems, has been implemented with the simulation platform AnyLogic
Chao, Crystal. "Timing multimodal turn-taking in human-robot cooperative activity." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/54904.
Full textHoopes, Daniel Matthew. "The ContexTable: Building and Testing an Intelligent, Context-Aware Kitchen Table." BYU ScholarsArchive, 2004. https://scholarsarchive.byu.edu/etd/12.
Full textKarim, Samim M. R. "Acquiring plans within situated resource-bounded agents : a hybrid BDI-based approach /." Connect to thesis, 2009. http://repository.unimelb.edu.au/10187/4865.
Full textMataric, Maja J. "Interaction and Intelligent Behavior." Thesis, Massachusetts Institute of Technology, 1994. http://hdl.handle.net/1721.1/7343.
Full textFrost, Elizabeth Marie. "Creating a Well-Situated Human-Autonomy Team: The Effects of Team Structure." Wright State University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=wright1578914702378707.
Full textFerreira, Emmanuel. "Apprentissage automatique en ligne pour un dialogue homme-machine situé." Thesis, Avignon, 2015. http://www.theses.fr/2015AVIG0206/document.
Full textA dialogue system should give the machine the ability to interactnaturally and efficiently with humans. In this thesis, we focus on theissue of the development of stochastic dialogue systems. Thus, we especiallyconsider the Partially Observable Markov Decision Process (POMDP)framework which yields state-of-the-art performance on goal-oriented dialoguemanagement tasks. This model enables the system to cope with thecommunication ambiguities due to noisy channel and also to optimize itsdialogue management strategy directly from data with Reinforcement Learning (RL)methods.Considering statistical approaches often requires the availability of alarge amount of training data to reach good performance. However, corpora of interest are seldom readily available and collectingsuch data is both time consuming and expensive. For instance, it mayrequire a working prototype to initiate preliminary experiments with thesupport of expert users or to consider other alternatives such as usersimulation techniques.Very few studies to date have considered learning a dialogue strategyfrom scratch by interacting with real users, yet this solution is ofgreat interest. Indeed, considering the learning process as part of thelife cycle of a system offers a principle framework to dynamically adaptthe system to new conditions in an online and seamless fashion.In this thesis, we endeavour to provide solutions to make possible thisdialogue system cold start (nearly from scratch) but also to improve its ability to adapt to new conditions in operation (domain extension, new user profile, etc.).First, we investigate the conditions under which initial expertknowledge (such as expert rules) can be used to accelerate the policyoptimization of a learning agent. Similarly, we study how polarized userappraisals gathered throughout the course of the interaction can beintegrated into a reinforcement learning-based dialogue manager. Morespecifically, we discuss how this information can be cast intosocially-inspired rewards to speed up the policy optimisation for bothefficient task completion and user adaptation in an online learning setting.The results obtained on a reference task demonstrate that a(quasi-)optimal policy can be learnt in just a few hundred dialogues,but also that the considered additional information is able tosignificantly accelerate the learning as well as improving the noise tolerance.Second, we focus on reducing the development cost of the spoken language understanding module. For this, we exploit recent word embedding models(projection of words in a continuous vector space representing syntacticand semantic properties) to generalize from a limited initial knowledgeabout the dialogue task to enable the machine to instantly understandthe user utterances. We also propose to dynamically enrich thisknowledge with both active learning techniques and state-of-the-artstatistical methods. Our experimental results show that state-of-the-artperformance can be obtained with a very limited amount of in-domain andin-context data. We also show that we are able to refine the proposedmodel by exploiting user returns about the system outputs as well as tooptimize our adaptive learning with an adversarial bandit algorithm tosuccessfully balance the trade-off between user effort and moduleperformance.Finally, we study how the physical embodiment of a dialogue system in a humanoid robot can help the interaction in a dedicated Human-Robotapplication where dialogue system learning and testing are carried outwith real users. Indeed, in this thesis we propose an extension of thepreviously considered decision-making techniques to be able to take intoaccount the robot's awareness of the users' belief (perspective taking)in a RL-based situated dialogue management optimisation procedure
Books on the topic "Situated Intelligence"
Stucky, Susan U. The situated processing of situated languages. Menlo Park, CA: CSLI/SRI International, 1987.
Find full textSituated cognition: On human knowledge and computer representations. Cambridge, U.K: Cambridge University Press, 1997.
Find full textLuc, Steels, and Brooks Rodney Allen, eds. The artificial life route to artificial intelligence: Building embodied, situated agents. Hillsdale, N.J: L. Erlbaum Associates, 1995.
Find full textCritiques of knowing: Situated textualities in science, computing, and the arts. London: Routledge, 1999.
Find full textCatching ourselves in the act: Situated activity, interactive emergence, evolution, and human thought. Cambridge, Mass: MIT Press, 1996.
Find full textScherpenisse, Wim (Willem Adriaan), 1958-, ed. RQ: Hoe risico-intelligentie zorgt voor betere beslissingen in onzekere situaties. Amsterdam: Maven Publishing, 2012.
Find full textSituated Self: Identity in a World of Ambient Intelligence. Wolf Legal Publishers, W.L.P., 2010.
Find full text(Editor), Luc Steels, and Rodney Brooks (Editor), eds. The Artificial Life Route To Artificial Intelligence: Building Embodied, Situated Agents. Lawrence Erlbaum, 1995.
Find full textSteels, Luc. The Artificial Life Route to Artificial Intelligence: Building Embodied, Situated Agents. Lawrence Erlbaum Assoc Inc, 1995.
Find full textKray, C. Situated Interaction on Spatial Topics (Dissertations in Artificial Intelligence: Infix). Ios Pr Inc, 2003.
Find full textBook chapters on the topic "Situated Intelligence"
Loukanova, Roussanka. "Situation Theory, Situated Information, and Situated Agents." In Transactions on Computational Collective Intelligence XVII, 145–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-44994-3_8.
Full textLiefke, Kristina, and Mark Bowker. "Rich Situated Attitudes." In New Frontiers in Artificial Intelligence, 45–61. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61572-1_4.
Full textReffat, Rabee M., and John S. Gero. "Computational Situated Learning in Design." In Artificial Intelligence in Design ’00, 589–610. Dordrecht: Springer Netherlands, 2000. http://dx.doi.org/10.1007/978-94-011-4154-3_29.
Full textBoccignone, Giuseppe, Vittorio Caggiano, Gianluca Di Fiore, Angelo Marcelli, and Paolo Napoletano. "A Bayesian Approach to Situated Vision." In Brain, Vision, and Artificial Intelligence, 367–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11565123_35.
Full textGero, John S., and Udo Kannengiesser. "The Situated Function — Behaviour — Structure Framework." In Artificial Intelligence in Design ’02, 89–104. Dordrecht: Springer Netherlands, 2002. http://dx.doi.org/10.1007/978-94-017-0795-4_5.
Full textBölöni, Ladislau. "Autobiography Based Prediction in a Situated AGI Agent." In Artificial General Intelligence, 11–20. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09274-4_2.
Full textde Brito, Maiquel, Jomi F. Hübner, and Olivier Boissier. "Bringing Constitutive Dynamics to Situated Artificial Institutions." In Progress in Artificial Intelligence, 624–37. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23485-4_63.
Full textLison, Pierre, and Geert-Jan M. Kruijff. "Robust Processing of Situated Spoken Dialogue." In KI 2009: Advances in Artificial Intelligence, 241–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04617-9_31.
Full textHexmoor, Henry. "A Cognitive Model of Situated Autonomy." In Advances in Artificial Intelligence. PRICAI 2000 Workshop Reader, 325–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45408-x_33.
Full textGero, John S. "Conceptual designing as a sequence of situated acts." In Artificial Intelligence in Structural Engineering, 165–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0030450.
Full textConference papers on the topic "Situated Intelligence"
Nishida, Toyoaki. "Social Intelligence Design for Cultivating Shared Situated Intelligence." In 2010 IEEE International Conference on Granular Computing (GrC-2010). IEEE, 2010. http://dx.doi.org/10.1109/grc.2010.170.
Full textDjerroud, Halim, and Arab Cherif. "Environment Engine for Situated MAS." In 11th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2019. http://dx.doi.org/10.5220/0007310501290137.
Full textVizzari, Giuseppe, and Francesco Olivieri. "Towards Hybrid Situated Agents Based Virtual Environments." In 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology. IEEE, 2009. http://dx.doi.org/10.1109/wi-iat.2009.356.
Full textMavridis, Nikolaos, Thirimachos Bourlai, and Dimitri Ognibene. "The Human-Robot Cloud: Situated collective intelligence on demand." In 2012 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER). IEEE, 2012. http://dx.doi.org/10.1109/cyber.2012.6392580.
Full textDowning, Keith L. "The predictive basis of situated and embodied artificial intelligence." In the 2005 conference. New York, New York, USA: ACM Press, 2005. http://dx.doi.org/10.1145/1068009.1068016.
Full textAndrist, Sean, Dan Bohus, Ashley Feniello, and Nick Saw. "Developing Mixed Reality Applications with Platform for Situated Intelligence." In 2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW). IEEE, 2022. http://dx.doi.org/10.1109/vrw55335.2022.00018.
Full textKawsar, Fahim, Gerd Kortuem, and Bashar Altakrouri. "Designing Pervasive Interactions for Ambient Guidance with Situated Flows." In 2010 IEEE/ACM International Conference on Web Intelligence-Intelligent Agent Technology (WI-IAT). IEEE, 2010. http://dx.doi.org/10.1109/wi-iat.2010.119.
Full text"Effective Distribution of Large Scale Situated Agent-based Simulations." In International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and and Technology Publications, 2014. http://dx.doi.org/10.5220/0004756903120319.
Full textBalke, Tina, Marina De Vos, Julian Padget, and Dimitris Traskas. "Normative Run-Time Reasoning for Institutionally-Situated BDI Agents." In 2011 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT). IEEE, 2011. http://dx.doi.org/10.1109/wi-iat.2011.49.
Full text"HOW TO EASILY DESIGN REUSABLE BEHAVIOURS FOR SITUATED CHARACTERS?" In 1st International Conference on Agents and Artificial Intelligence. SciTePress - Science and and Technology Publications, 2009. http://dx.doi.org/10.5220/0001544601670172.
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