Relevant bibliographies by topics / Intention Recognition

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Intention Recognition'

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Published: 4 June 2021
Last updated: 9 February 2022

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Journal articles on the topic "Intention Recognition"

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Mazzone, Marco. "Pragmatics and Cognition: Intentions and Pattern Recognition in Context." International Review of Pragmatics 1, no. 2 (2009): 321–47. http://dx.doi.org/10.1163/187730909x12535267111615.

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AbstractThe importance of intention reading for communication has already been emphasized many years ago by Paul Grice. More recently, the rich debate on “theory of mind” has convinced many that intention reading may in fact play a key role also in current, cognitively oriented theories of pragmatics: Relevance Theory is a case in point. On a close analysis, however, it is far from clear that RT may really accommodate the idea that intention reading drives comprehension. Here I examine RT's difficulties with that idea, and propose a framework where intention reading is actually assigned a significant role. This framework is compatible with RT's account of a unified, automatic mechanism of interpretation in lexical pragmatics, to the extent that the account shares many features of associative and constraint-based explanations of other linguistic phenomena. In fact, my suggestion is that our sensitivity to others' intentions depends crucially on the availability of specific patterns of intentional behaviour grounded in social regularities. In other words, intention reading would be just a case, though a very special one, of pattern recognition.
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Han, The Anh, Luís Moniz Pereira, and Francisco C. Santos. "Corpus-Based Intention Recognition in Cooperation Dilemmas." Artificial Life 18, no. 4 (October 2012): 365–83. http://dx.doi.org/10.1162/artl_a_00072.

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Intention recognition is ubiquitous in most social interactions among humans and other primates. Despite this, the role of intention recognition in the emergence of cooperative actions remains elusive. Resorting to the tools of evolutionary game theory, herein we describe a computational model showing how intention recognition coevolves with cooperation in populations of self-regarding individuals. By equipping some individuals with the capacity of assessing the intentions of others in the course of a prototypical dilemma of cooperation—the repeated prisoner's dilemma—we show how intention recognition is favored by natural selection, opening a window of opportunity for cooperation to thrive. We introduce a new strategy (IR) that is able to assign an intention to the actions of opponents, on the basis of an acquired corpus consisting of possible plans achieving that intention, as well as to then make decisions on the basis of such recognized intentions. The success of IR is grounded on the free exploitation of unconditional cooperators while remaining robust against unconditional defectors. In addition, we show how intention recognizers do indeed prevail against the best-known successful strategies of iterated dilemmas of cooperation, even in the presence of errors and reduction of fitness associated with a small cognitive cost for performing intention recognition.
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Peng, Minjing, Yanwei Qin, Chenxin Tang, and Xiangming Deng. "An E-Commerce Customer Service Robot Based on Intention Recognition Model." Journal of Electronic Commerce in Organizations 14, no. 1 (January 2016): 34–44. http://dx.doi.org/10.4018/jeco.2016010104.

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There are three defects for providing human-labor customer services in e-commerce operations: high costs of human labors, staff turnover, and lack of service quality assurance. Breakthroughs made in artificial intelligence, natural language processing and related fields make it possible to replace human labors with online artificial intelligent robots to provide the e-commerce customer service, which indicates the online robots are the future of e-commerce customer services. However, most of the current robots are designed to reply with knowledge matching the key words in question sentences from the database, rarely involving in research on customer intentions that are key factors influencing user experience and online sales. In this research, an intention recognizing model was proposed to obtain intentions of e-commerce consumers by computing the strengths of candidate intention nodes in the intention graph, which was used to describe relations between different goods that could be the intentional targets of e-commerce consumers. The proposed robot was constructed based on the intention recognizing model to identify intentions of consumers and use the located knowledge combined with the AIML based sentence composition template to produce the response sentences for consumers. At last, the proposed robot was evaluated using F3 and ROUGE metrics by comparing with a keyword matching robot. And the evaluation results proved the validity of the proposed robot.
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Ahmed, Abdulghani Ali. "Investigation Approach for Network Attack Intention Recognition." International Journal of Digital Crime and Forensics 9, no. 1 (January 2017): 17–38. http://dx.doi.org/10.4018/ijdcf.2017010102.

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Sensitive information has critical risks when transmitted through computer networks. Existing protection systems still have limitations with treating network information with sufficient confidentiality, integrity, and availability. The rapid development of network technologies helps increase network attacks and hides their malicious intentions. Attack intention is the ultimate attack goal that the attacker attempts to achieve by executing various intrusion methods or techniques. Recognizing attack intentions helps security administrator develop effective protection systems that can detect network attacks that have similar intentions. This paper analyses attack types and classifies them according to their malicious intent. An investigation approach based on similarity metric is proposed to recognize attacker plans and predict their intentions. The obtained results demonstrate that the proposed approach is capable of investigating similarity of attack signatures and recognizing the intentions of Network attack.
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Cao, Guoxiang, Anlin Wang, and Donghuan Xu. "Wheel Loader Driving Intention Recognition with Gaussian Mixture - Hidden Markov Model." MATEC Web of Conferences 237 (2018): 03001. http://dx.doi.org/10.1051/matecconf/201823703001.

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Accurate recognition of driving intentions can delay upshifts under the intention of quick acceleration to maximize vehicle power performance; avoid frequent gear changes in automatic transmissions for rapid deceleration intention and make all power to flow to the bucket in the desire for fast motion of cylinders. However, due to the ambiguity of the human intentions and multiple meanings of depressing on the accelerator pedal in wheel loader, it is difficult to recognize driving intention. Nevertheless, the driver’s intentions are directly reflected in the accelerator pedal, brake pedal and hydraulic valve control handle. By detecting these observable signals such as the signals of acceleration pedal’s displacement and velocity, brake pedal’s displacement and velocity and valve status Gaussian Mixture – Hidden Markov Model(MGHMM) can recognize the unobservable driving intentions. The experiment is done in Simulink and the results show that MGHMM can recognize driving intentions as expected.
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Yue, Shi-guang, Peng Jiao, Ya-bing Zha, and Quan-jun Yin. "A Logical Hierarchical Hidden Semi-Markov Model for Team Intention Recognition." Discrete Dynamics in Nature and Society 2015 (2015): 1–19. http://dx.doi.org/10.1155/2015/975951.

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Intention recognition is significant in many applications. In this paper, we focus on team intention recognition, which identifies the intention of each team member and the team working mode. To model the team intention as well as the world state and observation, we propose a Logical Hierarchical Hidden Semi-Markov Model (LHHSMM), which has advantages of conducting statistical relational learning and can present a complex mission hierarchically. Additionally, the LHHSMM explicitly models the duration of team working mode, the intention termination, and relations between the world state and observation. A Logical Particle Filter (LPF) algorithm is also designed to infer team intentions modeled by the LHHSMM. In experiments, we simulate agents’ movements in a combat field and employ agents’ traces to evaluate performances of the LHHSMM and LPF. The results indicate that the team working mode and the target of each agent can be effectively recognized by our methods. When intentions are interrupted within a high probability, the LHHSMM outperforms a modified logical hierarchical hidden Markov model in terms of precision, recall, andF-measure. By comparing performances of LHHSMMs with different duration distributions, we prove that the explicit duration modeling of the working mode is effective in team intention recognition.
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Schlenoff, Craig, Zeid Kootbally, Anthony Pietromartire, Marek Franaszek, and Sebti Foufou. "Intention recognition in manufacturing applications." Robotics and Computer-Integrated Manufacturing 33 (June 2015): 29–41. http://dx.doi.org/10.1016/j.rcim.2014.06.007.

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Mghabghab, Serge, Imad H. Elhajj, and Daniel Asmar. "Personalized teleoperation via intention recognition." Advanced Robotics 32, no. 13 (April 23, 2018): 697–716. http://dx.doi.org/10.1080/01691864.2018.1460619.

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Gregoromichelaki, Eleni, Ruth Kempson, Matthew Purver, Gregory J. Mills, Ronnie Cann, Wilfried Meyer-Viol, and Patrick G. T. Healey. "Incrementality and intention-recognition in utterance processing." Dialogue & Discourse 2, no. 1 (May 3, 2011): 199–233. http://dx.doi.org/10.5087/dad.2011.109.

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Ever since dialogue modelling first developed relative to broadly Gricean assumptions about utter-ance interpretation (Clark, 1996), it has remained an open question whether the full complexity of higher-order intention computation is made use of in everyday conversation. In this paper we examine the phenomenon of split utterances, from the perspective of Dynamic Syntax, to further probe the necessity of full intention recognition/formation in communication: we do so by exploring the extent to which the interactive coordination of dialogue exchange can be seen as emergent from low-level mechanisms of language processing, without needing representation by interlocutors of each other’s mental states, or fully developed intentions as regards messages to be conveyed. We thus illustrate how many dialogue phenomena can be seen as direct consequences of the grammar architecture, as long as this is presented within an incremental, goal-directed/predictive model.
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de Clercq, P., J. van den Herik, A. Hasman, and A. Latoszek-Berendsen. "Intention-based Expressions in GASTINE." Methods of Information in Medicine 48, no. 04 (2009): 391–96. http://dx.doi.org/10.3414/me0591.

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Summary Objectives: 1) To evaluate the design of the framework for computerized intention-based clinical practice guidelines; 2) to implement runtime features such as plan recognition and backtracking. Method: To evaluate the design, we implemented the heart failure guideline into GASTINE, a tool for representing and executing intention-based clinical guidelines. Result: Description of the current implementation of intention-based expressions in GASTINE and analysis of some generic shortcomings. Explanation of how these shortcomings are addressed. Presentation of how plan recognition and backtracking work and how they improve the system. Conclusion: The improved guideline system is rather flexible, i.e., it allows deviations from the guideline as long as they are in the spirit of the guideline. The recognition of actions as intended by the users facilitates a flexible decision support system. The intentions are used to explain why certain actions were suggested. Therefore it is assumed that showing the intention behind suggested actions provides a better insight into why these actions are advised.
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Dissertations / Theses on the topic "Intention Recognition"

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Akridge, Cameron. "Intention Recognition in a Strategic Environment." Honors in the Major Thesis, University of Central Florida, 2005. http://digital.library.ucf.edu/cdm/ref/collection/ETH/id/736.

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This item is only available in print in the UCF Libraries. If this is your Honors Thesis, you can help us make it available online for use by researchers around the world by following the instructions on the distribution consent form at http://library.ucf
Bachelors
Engineering and Computer Science
Computer Engineering
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Aarno, Daniel. "Intention recognition in human machine collaborative systems." Licentiate thesis, KTH, Numerical Analysis and Computer Science, NADA, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-4303.

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Robotsystem har använts flitigt under de senaste årtiondena för att skapa automationslösningar i ett flertal områden. De flesta nuvarande automationslösningarna är begränsade av att uppgifterna de kan lösa måste vara repetitiva och förutsägbara. En av anledningarna till detta är att dagens robotsystem saknar förmåga att förstå och resonera om omvärlden. På grund av detta har forskare inom robotik och artificiell intelligens försökt att skapa intelligentare maskiner. Trots att stora framsteg har gjorts då det gäller att skapa robotar som kan fungera och interagera i en mänsklig miljö så finns det för nuvarande inget system som kommer i närheten av den mänskliga förmågan att resonera om omvärlden.

För att förenkla problemet har vissa forskare föreslagit en alternativ lösning till helt självständiga robotar som verkar i mänskliga miljöer. Alternativet är att kombinera människors och maskiners förmågor. Exempelvis så kan en person verka på en avlägsen plats, som kanske inte är tillgänglig för personen i fråga på grund av olika orsaker, genom att använda fjärrstyrning. Vid fjärrstyrning skickar operatören kommandon till en robot som verkar som en förlängning av operatörens egen kropp.

Segmentering och identifiering av rörelser skapade av en operatör kan användas för att tillhandahålla korrekt assistans vid fjärrstyrning eller samarbete mellan människa och maskin. Assistansen sker ofta inom ramen för virtuella fixturer där eftergivenheten hos fixturen kan justeras under exekveringen för att tillhandahålla ökad prestanda i form av ökad precision och minskad tid för att utföra uppgiften.

Den här avhandlingen fokuserar på två aspekter av samarbete mellan människa och maskin. Klassificering av en operatörs rörelser till ett på förhand specificerat tillstånd under en manipuleringsuppgift och assistans under manipuleringsuppgiften baserat på virtuella fixturer. Den specifika tillämpningen som behandlas är manipuleringsuppgifter där en mänsklig operatör styr en robotmanipulator i ett fjärrstyrt eller samarbetande system.

En metod för att följa förloppet av en uppgift medan den utförs genom att använda virtuella fixturer presenteras. Istället för att följa en på förhand specificerad plan så har operatören möjlighet att undvika oväntade hinder och avvika från modellen. För att möjliggöra detta estimeras kontinuerligt sannolikheten att operatören följer en viss trajektorie (deluppgift). Estimatet används sedan för att justera eftergivenheten hos den virtuella fixturen så att ett beslut om hur rörelsen ska fixeras kan tas medan uppgiften utförs.

En flerlagers dold Markovmodell (eng. layered hidden Markov model) används för att modellera mänskliga färdigheter. En gestemklassificerare som klassificerar en operatörs rörelser till olika grundläggande handlingsprimitiver, eller gestemer, evalueras. Gestemklassificerarna används sedan i en flerlagers dold Markovmodell för att modellera en simulerad fjärrstyrd manipuleringsuppgift. Klassificeringsprestandan utvärderas med avseende på brus, antalet gestemer, typen på den dolda Markovmodellen och antalet tillgängliga träningssekvenser. Den flerlagers dolda Markovmodellen tillämpas sedan på data från en trajektorieföljningsuppgift i 2D och 3D med en robotmanipulator för att ge både kvalitativa och kvantitativa resultat. Resultaten tyder på att den flerlagers dolda Markovmodellen är väl lämpad för att modellera trajektorieföljningsuppgifter och att den flerlagers dolda Markovmodellen är robust med avseende på felklassificeringar i de underliggande gestemklassificerarna.


Robot systems have been used extensively during the last decades to provide automation solutions in a number of areas. The majority of the currently deployed automation systems are limited in that the tasks they can solve are required to be repetitive and predicable. One reason for this is the inability of today’s robot systems to understand and reason about the world. Therefore the robotics and artificial intelligence research communities have made significant research efforts to produce more intelligent machines. Although significant progress has been made towards achieving robots that can interact in a human environment there is currently no system that comes close to achieving the reasoning capabilities of humans.

In order to reduce the complexity of the problem some researchers have proposed an alternative to creating fully autonomous robots capable of operating in human environments. The proposed alternative is to allow fusion of human and machine capabilities. For example, using teleoperation a human can operate at a remote site, which may not be accessible for the operator for a number of reasons, by issuing commands to a remote agent that will act as an extension of the operator’s body.

Segmentation and recognition of operator generated motions can be used to provide appropriate assistance during task execution in teleoperative and human-machine collaborative settings. The assistance is usually provided in a virtual fixture framework where the level of compliance can be altered online in order to improve the performance in terms of execution time and overall precision. Acquiring, representing and modeling human skills are key research areas in teleoperation, programming-by-demonstration and human-machine collaborative settings. One of the common approaches is to divide the task that the operator is executing into several sub-tasks in order to provide manageable modeling.

This thesis is focused on two aspects of human-machine collaborative systems. Classfication of an operator’s motion into a predefined state of a manipulation task and assistance during a manipulation task based on virtual fixtures. The particular applications considered consists of manipulation tasks where a human operator controls a robotic manipulator in a cooperative or teleoperative mode.

A method for online task tracking using adaptive virtual fixtures is presented. Rather than executing a predefined plan, the operator has the ability to avoid unforeseen obstacles and deviate from the model. To allow this, the probability of following a certain trajectory sub-task) is estimated and used to automatically adjusts the compliance of a virtual fixture, thus providing an online decision of how to fixture the movement.

A layered hidden Markov model is used to model human skills. A gestem classifier that classifies the operator’s motions into basic action-primitives, or gestemes, is evaluated. The gestem classifiers are then used in a layered hidden Markov model to model a simulated teleoperated task. The classification performance is evaluated with respect to noise, number of gestemes, type of the hidden Markov model and the available number of training sequences. The layered hidden Markov model is applied to data recorded during the execution of a trajectory-tracking task in 2D and 3D with a robotic manipulator in order to give qualitative as well as quantitative results for the proposed approach. The results indicate that the layered hidden Markov model is suitable for modeling teleoperative trajectory-tracking tasks and that the layered hidden Markov model is robust with respect to misclassifications in the underlying gestem classifiers.

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Aarno, Daniel K. E. "Intention recognition in human machine collaborative systems /." Stockholm : KTH, School of Computer Science and Communication, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-4303.

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Akridge, Cameron. "ON ADVANCED TEMPLATE-BASED INTERPRETATION AS APPLIED TO INTENTION RECOGNITION IN A STRATEGIC ENVIRONMENT." Master's thesis, University of Central Florida, 2007. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/4106.

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An area of study that has received much attention over the past few decades is simulations involving threat assessment in military scenarios. Recently, much research has emerged concerning the recognition of troop movements and formations in non-combat simulations. Additionally, there have been efforts towards the detection and assessment of various types of malicious intentions. One such work by Akridge addressed the issue of Strategic Intention Recognition, but fell short in the detection of tactics that it could not detect without somehow manipulating the environment. Therefore, the aim of this thesis is to address the problem of recognizing an opponent's intent in a strategic environment where the system can think ahead in time to see the agent's plan. To approach the problem, a structured form of knowledge called Template-Based Interpretation is borrowed from the work of others and enhanced to reason in a temporally dynamic simulation.
M.S.Cp.E.
School of Electrical Engineering and Computer Science
Engineering and Computer Science
Computer Engineering
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Cruz, Gabriel M. Eng Massachusetts Institute of Technology. "Solving Dec-MDPs with options and intention recognition." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/106028.

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Thesis: M. Eng. in Computer Science and Engineering, Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 31-32).
In this thesis, we designed and implemented an algorithm to find approximate solutions to multi-agent systems. We model the problems with a Decentralized Markov Decision Process, and we make use of options and intention recognition to solve the problem. Rather than directly solving the Dec-MDP, which is NEXP-Complete, we instead solve a set of single-agent MDPs, that we can solve in P-Complete, and combine these solutions during execution time. We tested our algorithm on several instances of the Bribed Package Retrieval Problem and we were able to handle problems as large as our MDP solver would allow, which is a big improvement over what optimal Dec-MDP solvers can handle.
by Gabriel Cruz.
M. Eng. in Computer Science and Engineering
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Duncan, Kester. "Scene-Dependent Human Intention Recognition for an Assistive Robotic System." Scholar Commons, 2014. https://scholarcommons.usf.edu/etd/5009.

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In order for assistive robots to collaborate effectively with humans for completing everyday tasks, they must be endowed with the ability to effectively perceive scenes and more importantly, recognize human intentions. As a result, we present in this dissertation a novel scene-dependent human-robot collaborative system capable of recognizing and learning human intentions based on scene objects, the actions that can be performed on them, and human interaction history. The aim of this system is to reduce the amount of human interactions necessary for communicating tasks to a robot. Accordingly, the system is partitioned into scene understanding and intention recognition modules. For scene understanding, the system is responsible for segmenting objects from captured RGB-D data, determining their positions and orientations in space, and acquiring their category labels. This information is fed into our intention recognition component where the most likely object and action pair that the user desires is determined. Our contributions to the state of the art are manifold. We propose an intention recognition framework that is appropriate for persons with limited physical capabilities, whereby we do not observe human physical actions for inferring intentions as is commonplace, but rather we only observe the scene. At the core of this framework is our novel probabilistic graphical model formulation entitled Object-Action Intention Networks. These networks are undirected graphical models where the nodes are comprised of object, action, and object feature variables, and the links between them indicate some form of direct probabilistic interaction. This setup, in tandem with a recursive Bayesian learning paradigm, enables our system to adapt to a user's preferences. We also propose an algorithm for the rapid estimation of position and orientation values of scene objects from single-view 3D point cloud data using a multi-scale superquadric fitting approach. Additionally, we leverage recent advances in computer vision for an RGB-D object categorization procedure that balances discrimination and generalization as well as a depth segmentation procedure that acquires candidate objects from tabletops. We demonstrate the feasibility of the collaborative system presented herein by conducting evaluations on multiple scenes comprised of objects from 11 categories, along with 7 possible actions, and 36 possible intentions. We achieve approximately 81% reduction in interactions overall after learning despite changes to scene structure.
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Elzer, Stephanie. "A probabilistic framework for the recognition of intention in information graphics." Access to citation, abstract and download form provided by ProQuest Information and Learning Company; downloadable PDF file 0.70 Mb., 223 p, 2006. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&res_dat=xri:pqdiss&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&rft_dat=xri:pqdiss:3200529.

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Han, The Anh. "Intention recognition, commitment and their roles in the evolution of cooperation." Doctoral thesis, Faculdade de Ciências e Tecnologia, 2012. http://hdl.handle.net/10362/8784.

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Dissertação para obtenção do Grau de Doutor em Informática
The goal of this thesis is twofold. First, intention recognition is studied from an Arti cial Intelligence (AI) modeling perspective. We present a novel and e cient intention recognition method that possesses several important features: (i) The method is context-dependent and incremental, enabled by incrementally constructing a three-layer Bayesian network model as more actions are observed, and in a context-dependent manner, relying on a logic programming knowledge base concerning the context; (ii) The Bayesian network is composed from a knowledge base of readily speci ed and readily maintained Bayesian network fragments with simple structures, enabling an e cient acquisition of the corresponding knowledge base (either from domain experts or else automatically from a plan corpus); and, (iii) The method addresses the issue of intention change and abandonment, and can appropriately resolve the issue of multiple intentions recognition. Several aspects of the method are evaluated experimentally, achieving some de nite success. Furthermore, on top of the intention recognition method, a novel framework for intention-based decision making is provided, illustrating several ways in which an ability to recognize intentions of others can enhance a decision making process. A second subgoal of the thesis concerns that, whereas intention recognition has been extensively studied in small scale interactive settings, there is a major shortage of modeling research with respect to large scale social contexts, namely evolutionary roles and aspects of intention recognition. Employing our intention recognition method and the tools of evolutionary game theory, this thesis explicitly addresses the roles played by intention recognition in the nal outcome of cooperation in large populations of self-regarding individuals. By equipping individuals with the capacity for assessing intentions of others in the course of social dilemmas, we show how intention recognition is selected by natural selection, opening a window of opportunity for cooperation to thrive, even in hard cooperation prone games like the Prisoner's Dilemma. In addition, there are cases where it is di cult, if not impossible, to recognize the intentions of another agent. In such cases, the strategy of proposing commitment, or of intention manifestation, can help to impose or clarify the intentions of others. Again using the tools of evolutionary game theory, we show that a simple form of commitment can lead to the emergence of cooperation; furthermore, the combination of commitment with intention recognition leads to a strategy better than either one by itself. How the thesis should be read? We recommend that the thesis be read sequentially, chapter by chapter [1-2-3-4-5-6-7-8]. However, for those more interested in intention recognition from the AI modeling perspective, i.e. the rst subgoal of the thesis, Chapters 6 and 7 can be omitted and Chapters 4 and 5 are optional [1-2-3-(4)-(5)-8]. In addition, for those more keen on the problem of the evolution of cooperation, i.e. the second subgoal of thesis, Chapter 3 and even Chapter 2, can be omitted [1-(2)-4-5-6-7-8].
Fundação para a Ciência e Tecnologia - PhD grant (ref. SFRH/BD/62373/2009)
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Awais, Muhammad [Verfasser], and Dominik [Akademischer Betreuer] Henrich. "Intuitive Human-Robot Interaction by Intention Recognition / Muhammad Awais. Betreuer: Dominik Henrich." Bayreuth : Universität Bayreuth, 2013. http://d-nb.info/1059353644/34.

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Khokar, Karan Hariharan. "Human Intention Recognition Based Assisted Telerobotic Grasping of Objects in an Unstructured Environment." Scholar Commons, 2013. http://scholarcommons.usf.edu/etd/4909.

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In this dissertation work, a methodology is proposed to enable a robot to identify an object to be grasped and its intended grasp configuration while a human is teleoperating a robot towards the desired object. Based on the detected object and grasp configuration, the human is assisted in the teleoperation task. The environment is unstructured and consists of a number of objects, each with various possible grasp configurations. The identification of the object and the grasp configuration is carried out in real time, by recognizing the intention of the human motion. Simultaneously, the human user is assisted to preshape over the desired grasp configuration. This is done by scaling the components of the remote arm end-effector motion that lead to the desired grasp configuration and simultaneously attenuating the components that are in perpendicular directions. The complete process occurs while manipulating the master device and without having to interact with another interface. Intention recognition from motion is carried out by using Hidden Markov Model (HMM) theory. First, the objects are classified based on their shapes. Then, the grasp configurations are preselected for each object class. The selection of grasp configurations is based on the human knowledge of robust grasps for the various shapes. Next, an HMM for each object class is trained by having a skilled teleoperator perform repeated preshape trials over each grasp configuration of the object class in consideration. The grasp configurations are modeled as the states of each HMM whereas the projections of translation and orientation vectors, over each reference vector, are modeled as observations. The reference vectors are the ideal translation and rotation trajectories that lead the remote arm end-effector towards a grasp configuration. During an actual grasping task performed by a novice or a skilled user, the trained model is used to detect their intention. The output probability of the HMM associated with each object in the environment is computed as the user is teleoperating towards the desired object. The object that is associated with the HMM which has the highest output probability, is taken as the desired object. The most likely Viterbi state sequence of the selected HMM gives the desired grasp configuration. Since an HMM is associated with every object, objects can be shuffled around, added or removed from the environment without the need to retrain the models. In other words, the HMM for each object class needs to be trained only once by a skilled teleoperator. The intention recognition algorithm was validated by having novice users, as well as the skilled teleoperator, grasp objects with different grasp configurations from a dishwasher rack. Each object had various possible grasp configurations. The proposed algorithm was able to successfully detect the operator's intention and identify the object and the grasp configuration of interest. This methodology of grasping was also compared with unassisted mode and maximum-projection mode. In the unassisted mode, the operator teleoperated the arm without any assistance or intention recognition. In the maximum-projection mode, the maximum projection of the motion vectors was used to determine the intended object and the grasp configuration of interest. Six healthy and one wheelchair-bound individuals, each executed twelve pick-and-place trials in intention-based assisted mode and unassisted mode. In these trials, they picked up utensils from the dishwasher and laid them on a table located next to it. The relative positions and orientations of the utensils were changed at the end of every third trial. It was observed that the subjects were able to pick-and-place the objects 51% faster and with less number of movements, using the proposed method compared to the unassisted method. They found it much easier to execute the task using the proposed method and experienced less mental and overall workloads. Two able-bodied subjects also executed three preshape trials over three objects in intention-based assisted and maximum projection mode. For one of the subjects, the objects were shuffled at the end of the six trials and she was asked to carry out three more preshape trials in the two modes. This time, however, the subject was made to change their intention when she was about to preshape to the grasp configurations. It was observed that intention recognition was consistently accurate through the trajectory in the intention-based assisted method except at a few points. However, in the maximum-projection method the intention recognition was consistently inaccurate and fluctuated. This often caused to subject to be assisted in the wring directions and led to extreme frustration. The intention-based assisted method was faster and had less hand movements. The accuracy of the intention based method did not change when the objects were shuffled. It was also shown that the model for intention recognition can be trained by a skilled teleoperator and be used by a novice user to efficiently execute a grasping task in teleoperation.
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Books on the topic "Intention Recognition"

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Kiefer, Peter. Mobile Intention Recognition. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-1854-2.

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Kiefer, Peter. Mobile intention recognition. New York: Springer, 2012.

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Han, The Anh. Intention Recognition, Commitment and Their Roles in the Evolution of Cooperation. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37512-5.

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Han, The Anh. Intention Recognition, Commitment and Their Roles in the Evolution of Cooperation: From Artificial Intelligence Techniques to Evolutionary Game Theory Models. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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Boffo, Vanna, Sabina Falconi, and Tamara Zappaterra, eds. Per una formazione al lavoro. Florence: Firenze University Press, 2012. http://dx.doi.org/10.36253/978-88-6655-304-5.

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The volume is a collection of the papers from a study seminar held at the University of Florence Faculty of Education and Training Sciences in March 2012 entitled Formazione e orientamento al lavoro. Le sfide della disabilità adulta. The aim of the initiative was to highlight a topic/problem which has little or no resonance in civil society, or in study and research contexts, namely, training and career guidance for disabled adults. The volume also recounts a course of studies carried out by Le Rose, a cooperative from the municipality of Florence, involving empirical research on the relationship between disability and job placement. As well as proposing an interdisciplinary and multifaceted reflection on a definitely innovative topic, the intention is to emphasize the central place of work in the lives of all people and the role that suitable education and training plays in constructing the adult identity. Care for the place where the job training is carried out, as well as attention to the relationships and actions pursued by the workers undertaking to develop job placement programmes, are central dimensions for the construction of a renewed culture of inclusion, citizenship and social and personal recognition.
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Kiefer, Peter. Mobile Intention Recognition. Springer, 2014.

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Kiefer, Peter. Mobile Intention Recognition. Springer, 2011.

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Plan, Activity, and Intent Recognition: Theory and Practice. Elsevier Science & Technology Books, 2014.

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Sukthankar, Gita, Hung Bui, Christopher W. Geib, and David V. Pynadath. Plan, Activity, and Intent Recognition: Papers from the AAAI Workshop. AAAI Press, 2011.

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Sukthankar, Gita, Christopher Geib, Hung Bui, and David Pynadath. Plan, Activity, and Intent Recognition: Papers from the AAAI Workshop. AAAI Press, 2010.

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Book chapters on the topic "Intention Recognition"

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te Vrugt, Jürgen, and Thomas Portele. "Intention Recognition." In SmartKom: Foundations of Multimodal Dialogue Systems, 285–99. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/3-540-36678-4_19.

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Kiefer, Peter. "Mobile Intention Recognition." In Mobile Intention Recognition, 11–53. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-1854-2_2.

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Kiefer, Peter. "Introduction." In Mobile Intention Recognition, 1–9. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-1854-2_1.

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Kiefer, Peter. "Related Approaches in Plan Recognition." In Mobile Intention Recognition, 55–78. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-1854-2_3.

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Kiefer, Peter. "Mobile Intention Recognition with Spatially Constrained Grammars." In Mobile Intention Recognition, 79–126. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-1854-2_4.

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Kiefer, Peter. "Evaluation and Discussion." In Mobile Intention Recognition, 127–44. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-1854-2_5.

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Kiefer, Peter. "Conclusion and Outlook." In Mobile Intention Recognition, 145–54. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-1854-2_6.

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Han, The Anh. "Incremental Intention Recognition." In Studies in Applied Philosophy, Epistemology and Rational Ethics, 17–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37512-5_2.

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Han, The Anh. "Context-Dependent Intention Recognition." In Studies in Applied Philosophy, Epistemology and Rational Ethics, 35–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37512-5_3.

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Tanenhaus, Michael K., Chigusa Kurumada, and Meredith Brown. "Prosody and Intention Recognition." In Studies in Theoretical Psycholinguistics, 99–118. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-12961-7_6.

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Conference papers on the topic "Intention Recognition"

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Berg-Cross, Gary, and Christopher Crick. "Intentions and intention recognition in intelligent agents." In the 10th Performance Metrics for Intelligent Systems Workshop. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/2377576.2377623.

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Anwar, Suzan, Mariofanna Milanova, Andrea Bigazzi, Leonardo Bocchi, and Andrea Guazzini. "Real time intention recognition." In IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2016. http://dx.doi.org/10.1109/iecon.2016.7794016.

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Wang, Shoujin, Liang Hu, Yan Wang, Quan Z. Sheng, Mehmet Orgun, and Longbing Cao. "Intention2Basket: A Neural Intention-driven Approach for Dynamic Next-basket Planning." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/323.

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User purchase behaviours are complex and dynamic, which are usually observed as multiple choice actions across a sequence of shopping baskets. Most of the existing next-basket prediction approaches model user actions as homogeneous sequence data without considering complex and heterogeneous user intentions, impeding deep under-standing of user behaviours from the perspective of human inside drivers and thus reducing the prediction performance. Psychological theories have indicated that user actions are essentially driven by certain underlying intentions (e.g., diet and entertainment). Moreover, different intentions may influence each other while different choices usually have different utilities to accomplish an intention. Inspired by such psychological insights, we formalize the next-basket prediction as an Intention Recognition, Modelling and Accomplishing problem and further design the Intention2Basket (Int2Ba in short) model. In Int2Ba, an Intention Recognizer, a Coupled Intention Chain Net, and a Dynamic Basket Planner are specifically designed to respectively recognize, model and accomplish the heterogeneous intentions behind a sequence of baskets to better plan the next-basket. Extensive experiments on real-world datasets show the superiority of Int2Ba over the state-of-the-art approaches.
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Liu, Xuan, Meijing Zhao, Song Dai, Qiyue Yin, and Wancheng Ni. "Tactical Intention Recognition in Wargame." In 2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS). IEEE, 2021. http://dx.doi.org/10.1109/icccs52626.2021.9449256.

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Wang, Yiwei, Yixuan Sheng, Ji Wang, and Wenlong Zhang. "Human Intention Estimation With Tactile Sensors in Human-Robot Collaboration." In ASME 2017 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/dscc2017-5291.

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In this paper, machine learning methods are proposed for human intention estimation based on the change of force distribution on the interaction surface during human-robot collaboration (HRC). The force distribution under different human intentions are examined when the human and robot are jointly carrying the same piece of object. A pair of Robotiq tactile sensors is applied to monitor the change of force distribution on the interaction surface. Three machine learning algorithms are tested on recognition of human intentions based on the force distribution patterns on the contact surface of grippers for the manipulator. The K-nearest Neighbor model is selected to build a real-time framework, which includes human intention estimation and cooperative motion planning for the robot manipulator. A real-time experiment is conducted to validate the method, which suggests the human intention estimation approach can help enhance the efficiency of HRC.
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Aarno, Daniel, and Danica Kragic. "Layered HMM for Motion Intention Recognition." In 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, 2006. http://dx.doi.org/10.1109/iros.2006.282606.

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Sadri, Fariba. "Intention Recognition with Event Calculus Graphs." In 2010 IEEE/ACM International Conference on Web Intelligence-Intelligent Agent Technology (WI-IAT). IEEE, 2010. http://dx.doi.org/10.1109/wi-iat.2010.83.

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Thill, Serge, Alberto Montebelli, and Tom Ziemke. "Workshop on intention recognition in HRI." In 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, 2016. http://dx.doi.org/10.1109/hri.2016.7451868.

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Nilsson, Julia, Jonas Fredriksson, and Erik Coelingh. "Rule-Based Highway Maneuver Intention Recognition." In 2015 IEEE 18th International Conference on Intelligent Transportation Systems - (ITSC 2015). IEEE, 2015. http://dx.doi.org/10.1109/itsc.2015.159.

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Kim, Sangwook, Zhibin Yu, Jonghong Kim, Amitash Ojha, and Minho Lee. "Human-Robot Interaction using Intention Recognition." In HAI 2015: The Third International Conference on Human-Agent Interaction. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2814940.2815002.

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