Relevant bibliographies by topics / Situated Intelligence

Academic literature on the topic 'Situated Intelligence'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Situated Intelligence"

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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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Situated Intelligence"

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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.

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Chu, 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.

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Rihawi, Omar. "Modelling and simulation of distributed large scale situated multi-agent systems." Thesis, Lille 1, 2014. http://www.theses.fr/2014LIL10148/document.

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Les systèmes multi-agents sont constitués d'entités autonomes qui interagissent avec leur environnement pour résoudre un objectif collectif. Si l'on souhaite modéliser des systèmes contenant des millions d'agents, une puissance de calcul et de stockage importante devient nécessaire. Pour atteindre de telles simulations large échelle, distribuer le simulateur sur un réseau de machines est nécessaire, mais il faut prendre en compte quelques aspects. Le premier aspect se concentre sur deux types de répartition de la charge de calcul : la première basée sur l'environnement, la deuxième basée sur les agents. Nous évaluons les performances de ces répartitions en les confrontant à des applications dont les dynamiques de déplacement sont très différentes, ce qui nous permet d'identifier plusieurs critères devant être pris en compte pour garantir des gains de performance lors de la distribution de simulations d'agents situés. Le second aspect de notre travail étudie la synchronisation. En effet, à notre connaissance, tous les simulateurs existants fonctionnent sur la base d'une synchronisation forte entre les machines, ce qui garantit la causalité temporelle et la cohérence des calculs. Dans cette thèse, nous remettons en cause cette hypothèse en étudiant la relaxation de la contrainte de synchronisation. Nous proposons deux politiques de synchronisation : la synchronisation forte classique et une forme de synchronisation reposant sur une fenêtre de temps bornée entre la machine la plus lente et la machine la plus rapide. Des applications de natures différentes sont exécutées avec ces différents mécanismes de synchronisation
This 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
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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.

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La modélisation et la simulation des systèmes complexes constitue une solution idéal pour comprendre ces systèmes. En effet, l'expérimentation virtuelle permet, par rapport à l'expérimentation réelle dans le champ d'étude considéré, d'apporter des réponses plus rapides aux questions posées sur ces systèmes, ce qui donne la possibilité de proposer des solutions en un temps adapté au contexte réel. Ce travail traite la question de la représentation de l'action humaine en prenant en compte sa dimension temporelle et spatiale aux échelles individuelle et collective. Cette question a déjà été traitée dans le domaine de l'intelligence artificielle, en général, et celui des systèmes agricoles, en particulier, qui constitue le domaine d'application de cette thèse. Les modèles proposés jusqu'à présent se basaient principalement sur la théorie de l'action planifiée en ne prenant en compte que la dimension temporelle de l'action. Les limites majeures de ces modèles résident dans leur complexité dans la mesure où il est difficile de pouvoir prédire l'ensemble des changements futurs dans l'environnement de l'acteur. Cela conduit à la nécessité de re-planifier fréquemment les actions afin d'obtenir des résultats cohérents. La deuxième limite réside dans l'écart qu'il peut y avoir entre les résultats des actions simulées et la réalité observée. En effet, un acteur ne réalise pas systématiquement les actions qu'il prévoit selon les situations réelles dans lesquelles il se trouve. Afin de pallier aux limites des modèles de l'action planifiée, nous avons développé un modèle de l'action humaine qui se base sur la théorie de l'action située. L'action est vue comme un processus doté d'une épaisseur temporelle émergent des situations créées par l'interaction entre l'acteur et son environnement dans le temps et dans l'espace. Notre modèle combine le concept d'affordance, le concept de stigmergie ainsi que la notion d'émergence. Nous proposons donc un système multi-agents dans lequel l'espace est explicitement représenté et partitionné en un ensemble de places. Le pilotage de chaque place est attribué à un agent abstrait. Celui-ci représente un observateur capable de détecter à tout instant les affordances émergentes sur sa place ainsi que de déclencher l'action appropriée. Les acteurs sont représentés comme des entités de l'environnement au même titre que les objets passifs. Ces entités de l'environnement portent un ensemble d'informations sur leurs capacités à exécuter ou subir des actions. Ces informations permettent aux agents, grâce aux méta-connaissances qu'ils détiennent de détecter les affordances. Celles-ci, une fois détectées, sont réifiées dans l'environnement et utilisées par les agents grâce à un mécanisme de sélection d'actions pour déterminer l'action qui sera finalement exécuter. La coordination des actions au niveau collectif se fait par stigmergie : les agents communiquent de façon implicite en utilisant un ensemble de marques qui sont une métaphore des phéromones des colonies de fourmis. Afin de montrer la pertinence du modèle proposé, un prototype appliqué au domaine des systèmes de production agricoles a été implémenté en utilisant la plateforme AnyLogic
Simulation 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
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Chao, Crystal. "Timing multimodal turn-taking in human-robot cooperative activity." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/54904.

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Turn-taking is a fundamental process that governs social interaction. When humans interact, they naturally take initiative and relinquish control to each other using verbal and nonverbal behavior in a coordinated manner. In contrast, existing approaches for controlling a robot's social behavior do not explicitly model turn-taking, resulting in interaction breakdowns that confuse or frustrate the human and detract from the dyad's cooperative goals. They also lack generality, relying on scripted behavior control that must be designed for each new domain. This thesis seeks to enable robots to cooperate fluently with humans by automatically controlling the timing of multimodal turn-taking. Based on our empirical studies of interaction phenomena, we develop a computational turn-taking model that accounts for multimodal information flow and resource usage in interaction. This model is implemented within a novel behavior generation architecture called CADENCE, the Control Architecture for the Dynamics of Embodied Natural Coordination and Engagement, that controls a robot's speech, gesture, gaze, and manipulation. CADENCE controls turn-taking using a timed Petri net (TPN) representation that integrates resource exchange, interruptible modality execution, and modeling of the human user. We demonstrate progressive developments of CADENCE through multiple domains of autonomous interaction encompassing situated dialogue and collaborative manipulation. We also iteratively evaluate improvements in the system using quantitative metrics of task success, fluency, and balance of control.
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Hoopes, Daniel Matthew. "The ContexTable: Building and Testing an Intelligent, Context-Aware Kitchen Table." BYU ScholarsArchive, 2004. https://scholarsarchive.byu.edu/etd/12.

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The purpose of this thesis was to design and evaluate The ContexTable, a context-aware system built into a kitchen table. After establishing the current status of the field of context-aware systems and the hurdles and problems being faced, a functioning prototype system was designed and built. The prototype makes it possible to explore established, untested theory and novel solutions to problems faced in the field.
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Karim, 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.

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Mataric, Maja J. "Interaction and Intelligent Behavior." Thesis, Massachusetts Institute of Technology, 1994. http://hdl.handle.net/1721.1/7343.

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We introduce basic behaviors as primitives for control and learning in situated, embodied agents interacting in complex domains. We propose methods for selecting, formally specifying, algorithmically implementing, empirically evaluating, and combining behaviors from a basic set. We also introduce a general methodology for automatically constructing higher--level behaviors by learning to select from this set. Based on a formulation of reinforcement learning using conditions, behaviors, and shaped reinforcement, out approach makes behavior selection learnable in noisy, uncertain environments with stochastic dynamics. All described ideas are validated with groups of up to 20 mobile robots performing safe--wandering, following, aggregation, dispersion, homing, flocking, foraging, and learning to forage.
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Frost, 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.

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Ferreira, Emmanuel. "Apprentissage automatique en ligne pour un dialogue homme-machine situé." Thesis, Avignon, 2015. http://www.theses.fr/2015AVIG0206/document.

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Un système de dialogue permet de doter la Machine de la capacité d'interagir de façon naturelle et efficace avec l'Homme. Dans cette thèse nous nous intéressons au développement d'un système de dialogue reposant sur des approches statistiques, et en particulier du cadre formel des Processus Décisionnel de Markov Partiellement Observable, en anglais Partially Observable Markov Decision Process (POMDP), qui à ce jour fait office de référence dans la littérature en ce qui concerne la gestion statistique du dialogue. Ce modèle permet à la fois une prise en compte améliorée de l'incertitude inhérente au traitement des données en provenance de l'utilisateur (notamment la parole) et aussi l'optimisation automatique de la politique d'interaction à partir de données grâce à l'apprentissage par renforcement, en anglais Reinforcement Learning (RL). Cependant, une des problématiques liées aux approches statistiques est qu'elles nécessitent le recours à une grande quantité de données d'apprentissage pour atteindre des niveaux de performances acceptables. Or, la collecte de telles données est un processus long et coûteux qui nécessite généralement, pour le cas du dialogue, la réalisation de prototypes fonctionnels avec l'intervention d'experts et/ou le développement de solution alternative comme le recours à la simulation d'utilisateurs. En effet, très peu de travaux considèrent à ce jour la possibilité d'un apprentissage de la stratégie de la Machine de part sa mise en situation de zéro (sans apprentissage préalable) face à de vrais utilisateurs. Pourtant cette solution présente un grand intérêt, elle permet par exemple d'inscrire le processus d'apprentissage comme une partie intégrante du cycle de vie d'un système lui offrant la capacité de s'adapter à de nouvelles conditions de façon dynamique et continue. Dans cette thèse, nous nous attacherons donc à apporter des solutions visant à rendre possible ce démarrage à froid du système mais aussi, à améliorer sa capacité à s'adapter à de nouvelles conditions (extension de domaine, changement d'utilisateur,...). Pour ce faire, nous envisagerons dans un premier temps l'utilisation de l'expertise du domaine (règles expertes) pour guider l'apprentissage initial de la politique d'interaction du système. De même, nous étudierons l'impact de la prise en compte de jugements subjectifs émis par l'utilisateur au fil de l'interaction dans l'apprentissage, notamment dans un contexte de changement de profil d'utilisateur où la politique préalablement apprise doit alors pouvoir s'adapter à de nouvelles conditions. Les résultats obtenus sur une tâche de référence montrent la possibilité d'apprendre une politique (quasi-)optimale en quelques centaines d'interactions, mais aussi que les informations supplémentaires considérées dans nos propositions sont à même d'accélérer significativement l'apprentissage et d'améliorer la tolérance aux bruits dans la chaîne de traitement. Dans un second temps nous nous intéresserons à réduire les coûts de développement d'un module de compréhension de la parole utilisé dans l'étiquetage sémantique d'un tour de dialogue. Pour cela, nous exploiterons les récentes avancées dans les techniques de projection des mots dans des espaces vectoriels continus conservant les propriétés syntactiques et sémantiques, pour généraliser à partir des connaissances initiales limitées de la tâche pour comprendre l'utilisateur. Nous nous attacherons aussi à proposer des solutions afin d'enrichir dynamiquement cette connaissance et étudier le rapport de cette technique avec les méthodes statistiques état de l'art. Là encore nos résultats expérimentaux montrent qu'il est possible d'atteindre des performances état de l'art avec très peu de données et de raffiner ces modèles ensuite avec des retours utilisateurs dont le coût peut lui-même être optimisé
A 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
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Books on the topic "Situated Intelligence"

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Stucky, Susan U. The situated processing of situated languages. Menlo Park, CA: CSLI/SRI International, 1987.

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Situated cognition: On human knowledge and computer representations. Cambridge, U.K: Cambridge University Press, 1997.

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Luc, Steels, and Brooks Rodney Allen, eds. The artificial life route to artificial intelligence: Building embodied, situated agents. Hillsdale, N.J: L. Erlbaum Associates, 1995.

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Critiques of knowing: Situated textualities in science, computing, and the arts. London: Routledge, 1999.

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Catching ourselves in the act: Situated activity, interactive emergence, evolution, and human thought. Cambridge, Mass: MIT Press, 1996.

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Scherpenisse, Wim (Willem Adriaan), 1958-, ed. RQ: Hoe risico-intelligentie zorgt voor betere beslissingen in onzekere situaties. Amsterdam: Maven Publishing, 2012.

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Situated Self: Identity in a World of Ambient Intelligence. Wolf Legal Publishers, W.L.P., 2010.

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(Editor), Luc Steels, and Rodney Brooks (Editor), eds. The Artificial Life Route To Artificial Intelligence: Building Embodied, Situated Agents. Lawrence Erlbaum, 1995.

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Steels, Luc. The Artificial Life Route to Artificial Intelligence: Building Embodied, Situated Agents. Lawrence Erlbaum Assoc Inc, 1995.

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Kray, C. Situated Interaction on Spatial Topics (Dissertations in Artificial Intelligence: Infix). Ios Pr Inc, 2003.

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Book chapters on the topic "Situated Intelligence"

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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.

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Liefke, 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.

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Reffat, 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.

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Boccignone, 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.

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Gero, 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.

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Bölöni, 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.

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de 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.

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Lison, 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.

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Hexmoor, 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.

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Gero, 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.

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Conference papers on the topic "Situated Intelligence"

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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.

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Djerroud, 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.

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Vizzari, 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.

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Mavridis, 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.

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Downing, 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.

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Andrist, 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.

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Kawsar, 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.

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"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.

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Balke, 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.

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"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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