Context-mediated behavior for intelligent agents

Turner, Roy M.

doi:10.1006/ijhc.1997.0173

Cited by 86 publications

(68 citation statements)

References 23 publications

(15 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Turner [38] proposes a plan selection approach which clearly separates context and plan representation. Contexts are represented as contextual schemas (c-schemas), a frame-like knowledge structure.…”

Section: Behaviorsmentioning

confidence: 99%

“…However, in many cases ( [38,11] are of course exceptions), the use of contextual knowledge is not supported by a shared representation and a systematic approach to design.…”

Section: Context-based Design Of Robotic Systemsmentioning

confidence: 99%

“…In order to show the approach we will make use of a rule-based reasoning system and of a simple PROLOG implementation. Other proposals developed a specific formalism to represent contextual knowledge and reason about it on mobile robots [38,15,11], which could be adopted as well.…”

Section: Contextual Reasoningmentioning

confidence: 99%

“…On the one side, the design and implementation of experimental systems that are focussed on cognition; on the other side, the need to improve the performance and the scope of applicability of robotic systems by providing them with high-level knowledge and capabilities. Turner [38] specifically addresses contextual knowledge in robotic applications. He characterizes context as: "any identifiable configuration of environmental, mission-related, and agent-related features".…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Context-based design of robotic systems

Calisi

Iocchi

Nardi

et al. 2008

Robotics and Autonomous Systems

View full text Add to dashboard Cite

The need for improving the robustness, as well as the ability to adapt to different operational conditions, is a key requirement for a wider deployment of robots in many application domains. In this paper, we present an approach to the design of robotic systems, that is based on the explicit representation of knowledge about context. The goal of the approach is to improve the system performance, by dynamically tailoring the functionalities of the robot to the specific features of the situation at hand. While the idea of using contextual knowledge is not new, the proposed approach generalizes previous work and its advantages are discussed through a case study including several experiments. In particular, we identify many attempts to use contextual knowledge in several basic functionalities of a mobile robot such as: behaviors, navigation, exploration, localization, mapping and perception. We then show how re-designing our mobile platform with a common representation of contextual knowledge, leads to interesting improvements in many of the above mentioned components, thus achieving greater flexibility and robustness in the face of different situations. Moreover, a clear separation of contextual knowledge leads to a design methodology, which supports the design of small specialized system components instead of complex self-contained subsystems.

show abstract

Section: Behaviorsmentioning

confidence: 99%

“…However, in many cases ( [38,11] are of course exceptions), the use of contextual knowledge is not supported by a shared representation and a systematic approach to design.…”

Section: Context-based Design Of Robotic Systemsmentioning

confidence: 99%

Section: Contextual Reasoningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Context-based design of robotic systems

Calisi

Iocchi

Nardi

et al. 2008

Robotics and Autonomous Systems

View full text Add to dashboard Cite

show abstract

“…Turner [22] developed a system--an adaptive reasoner--to make context explicit for autonomous underwater vehicles to tackle unanticipated events in complex environments. Contextual knowledge is represented as a set of contextual schemas (cschemas), then retrieving the most appropriate of those and using them to help the reasoner behave appropriately for its current context.…”

Section: Related Workmentioning

confidence: 99%

Context Dynamic and Explanation in Contextual Graphs

Brézillon

Modeling and Using Context

View full text Add to dashboard Cite

Abstract. This paper discusses the dynamic of context through the use of a context-based formalism called contextual graphs that has been initially developed in the SART application for the development of a support system in incident solving on a subway line. First, we present the formalism of contextual graphs through its new implementation. Second, we discuss the dynamic of context in contextual graphs. Third, we present two characteristics of contextual graphs as they relate to the dynamic of context, the incremental knowledge acquisition and the explanation generation. We conclude by a discussion of the key properties and the potential of contextual graphs for other applications.

show abstract

Pragmatic Research on Context Modeling and Use

Brézillon¹,

Turner²

2022

Modeling and Use of Context in Action

View full text Add to dashboard Cite

Pragmatic approaches to solving problems in artificial intelligence arise due to fast technological evolution as well as the complexity of real-world tasks. These operational, bottom-up approaches are based on representing how to behave in specific contexts. In this chapter, pragmatic approaches are discussed from the perspective of agents (human and AI) using data, information, and knowledge in context. Information is what is interpreted by an agent from data about the world, and what is exchanged with other agents. Information is highly context-dependent and under the control of the knowledge that is in the agent's "mind". Context plays a pivotal role in the development of information and knowledge. Setting data, information and knowledge in context allows new approaches to modeling (human and AI) reasoning. Based on its contextual knowledge (the part of its knowledge concerning the current focus), a reasoner interprets and uses information and produces new information and knowledge. Concretely, the pragmatic approach is shown in three examples of context-based research addressing applications in the real world. Operational knowledge in these approaches leads to contextualization of reasoning and opens the door to context-based AI systems.

show abstract

Context-mediated behavior for intelligent agents

Cited by 86 publications

References 23 publications

Context-based design of robotic systems

Context-based design of robotic systems

Context Dynamic and Explanation in Contextual Graphs

Pragmatic Research on Context Modeling and Use

Contact Info

Product

Resources

About