Tractable probabilistic models for intention recognition based on expert knowledge

Schrempf, Oliver C.; Albrecht, David W.; Hanebeck, Uwe D.

doi:10.1109/iros.2007.4399226

Cited by 34 publications

(16 citation statements)

References 12 publications

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“…11,13,14 Research about human intention inference occurs mainly in two categories: action-specific intention inference and context-specific intention inference. Actionspecific intention inference uses human activity/action patterns to infer human intentions including situation-assisted human intention inference, 15,16 online human utility-based human intention inference, 17,18 and hierarchical action modeling human intention inference 19,20 Context-specific intention inference involves context to assist human intention inference including environment-based most-desired intention identification, 21,22 context-assisted potential intention generation, 23,24 and spatial constraint intention inference 25,26 Context-specific intention inference…”

Section: Robotic Elderly Caregivingmentioning

confidence: 99%

Fuzzy context-specific intention inference for robotic caregiving

Liu

Zhang

2016

International Journal of Advanced Robotic Systems

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To provide timely and appropriate assistance, robots must have the capability of proactively understanding a user's personal needs, the so-called human intention inference. In human-human interaction, humans have a natural and implicit way to infer others' intentions by selecting correlated context features and interpreting these features based on their life experience. However, robots do not have this capability and it is not realistic to build an explicit formula to associate human intentions with context. In this article, a novel fuzzy context-specific intention inference method is developed for human-like implicit human intention inference. With a fuzzy manner, context features are converted into discrete context statuses, which are similar to human subjective feelings. An intention-centered common sense database is developed consisting of correlated fuzzy context statuses, object affordances, and their relationship with human intentions. With this database, a Fuzzy Naïve Bayesian Network algorithm is adopted for implicit intention inference. Home scenario results validated the fuzzy context-specific intention inference methods reliability and lab scenario results validated the fuzzy context-specific intention inference methods effectiveness and robustness. This work is expected to develop intuitive and effective human-robot interaction, consequently enhancing the adoption of assistive technologies and improving the independence of the disabled and elderly in activities of daily living.

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Section: Robotic Elderly Caregivingmentioning

confidence: 99%

Fuzzy context-specific intention inference for robotic caregiving

Liu

Zhang

2016

International Journal of Advanced Robotic Systems

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“…Reflecting this trend, there are a number of studies in the past few years for recognizing user intentions from a robot perspective [1], [11]- [13].…”

Section: Related Workmentioning

confidence: 99%

Personal Event Management among Multiple Devices Based on User Intention Recognition Using Dynamic Bayesian Networks

Jeon

Kim

Choi

2011

IEICE Trans. Inf. & Syst.

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SUMMARYThis paper proposes a proactive management system for the events that occur across multiple personal user devices, including desktop PCs, laptops, and smart phones. We implemented the Personal Event Management Service using Dynamic Bayesian Networks (PEMS-DBN) system that proactively executes appropriate tasks across multiple devices without explicit user requests by recognizing the user's device reuse intention, based on the observed actions of the user for specific devices. The client module of PEMS-DBN installed on each device monitors the user actions and recognizes user intention by using dynamic Bayesian networks. The server provides data sharing and maintenance for the clients. A series of experiments were performed to evaluate user satisfaction and system accuracy, and also the amounts of resource consumption during intention recognition and proactive execution are measured to ensure the system efficiency. The experimental results showed that the PEMS-DBN system can proactively provide appropriate, personalized services with a high degree of satisfaction to the user in an effective and efficient manner. key words: personal event management service (PEMS), user intention recognition, proactive task execution, dynamic Bayesian networks (DBN)

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“…In this context, a test environment for human-robot cooperation based on a extended range telepresence system [2]- [4] has been developed. In this test environment, the user can cooperate with a virtual humanoid robot in a virtual household environment.…”

Section: Applicationmentioning

confidence: 99%

“…Furthermore, possible applications and evaluation scenarios for the system are discussed. For this purpose, an evaluation platform is introduced, which employs techniques from extended range telepresence [2]- [4]. This platform allows a human to act and cooperate with a humanoid robot in a virtual environment in a realistic manner.…”

Section: Introductionmentioning

confidence: 99%

Three pillar information management system for modeling the environment of autonomous systems

Gheta

Baum

Belkin

et al. 2010

2010 IEEE International Conference on Virtual Environments, Human-Computer Interfaces and Measurement Systems

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Abstract-This contribution is about an information management and storage system for modeling the environment of autonomous systems. The three pillars of the system consist of prior knowledge, environment model and sensory information. The main pillar is the environment model, which supplies the autonomous system with relevant information about its current environment. For this purpose, an abstract representation of the real world is created, where instances with attributes and relations serve as virtual substitutes of entities (persons and objects) of the real world. The environment model is created based on sensory information about the real world. The gathered sensory information is typically uncertain in a stochastic sense and is represented in the environment model by means of Degreeof-Belief (DoB) distributions. The prior knowledge contains all relevant background knowledge (e. g., concepts organized in ontologies) for creating and maintaining the environment model. The concept of the three pillar information system has previously been published. Therefore this contribution focuses on further central properties of the system. Furthermore, the development status and possible applications as well as evaluation scenarios are discussed.

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Tractable probabilistic models for intention recognition based on expert knowledge

Cited by 34 publications

References 12 publications

Fuzzy context-specific intention inference for robotic caregiving

Fuzzy context-specific intention inference for robotic caregiving

Personal Event Management among Multiple Devices Based on User Intention Recognition Using Dynamic Bayesian Networks

Three pillar information management system for modeling the environment of autonomous systems

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