Personal Agents in the Rule Responder Architecture

Craig, Benjamin Larry; Boley, Harold

doi:10.1007/978-3-540-88808-6_17

Cited by 11 publications

(7 citation statements)

References 2 publications

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“…We try to follow message patterns similar to Web Service communication [8]. For example, there can be inonly, request-response, and request-response-acknowledge protocols, as well as entire workflow protocols [6]. Most of the instantiations of SymposiumPlanner primarily focuses on the request-response protocol.…”

Section: Agent Communication Protocolsmentioning

confidence: 99%

“…The new Rule Responder web interface allows users to issue formal queries via web forms and in controlled natural language. Meanwhile, there are large semantic knowledge repositories on the Internet, such as: DBpedia (Deutschland) 5 , Freebase 6 , YAGO 7 , Semantic Web Dog Food 8 . By reusing and integrating existing fact information on the Internet, we avoid redundancy in the knowledge bases of the SymposiumPlanner's agents.…”

Section: Introductionmentioning

confidence: 99%

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Principles of the SymposiumPlanner Instantiations of Rule Responder

Zhao

Paschke

Ali

et al. 2011

Rule-Based Modeling and Computing on the Semantic Web

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Section: Agent Communication Protocolsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Principles of the SymposiumPlanner Instantiations of Rule Responder

Zhao

Paschke

Ali

et al. 2011

Rule-Based Modeling and Computing on the Semantic Web

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“…OO jDREW is written in the Java programming language and available via Java Webstart and for LGPL download. The alerting rules described here benefitted from earlier implementations of NBBizKB, FindXpRT, and eTourPlan in OO jDREW as well as from the Rule Responder use case for symposium planning [24].…”

Section: Oo Jdrew Rule Enginementioning

confidence: 99%

Neurosymbolic Alerting Rules

Velik

Boley

2010

IEEE Trans. Ind. Electron.

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Abstract-Future building automation will require complex (human-like) perception and decision-making processes not being feasible with classical approaches. In this paper, we address both the perception and the decision-making process and present an alerting model that reacts to perceived situations in a building with decisions about possible alerts. Perception is based on the neuro-symbolic information processing model, which detects candidate alerts. Integrated with perception, decision-making is based on the rule model of RuleML, which computes alerts to relevant building occupants about current opportunities and risks. A general model of neuro-symbolic alerting rules is developed and exemplified with a use case of building alerts.

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“…For our purposes, the environment within a building is managed with three different types of agents, developing those in Rule Responder Craig & Boley (2008), namely personal agents, a building agent and a facilitator, as shown in Fig.2.…”

Section: The Building Automation Modelmentioning

confidence: 99%