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Formal Approaches to Agent-Based Systems

Overview of attention for book
Cover of 'Formal Approaches to Agent-Based Systems'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 Ecology Based Decentralized Agent Management System
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    Chapter 2 From Abstract to Concrete Norms in Agent Institutions
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    Chapter 3 Meeting the Deadline: Why, When and How
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    Chapter 4 Multi-agent Systems Reliability, Fuzziness, and Deterrence
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    Chapter 5 Formalism Challenges of the Cougaar Model Driven Architecture
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    Chapter 6 Facilitating the Specification Capture and Transformation Process in the Development of Multi-agent Systems
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    Chapter 7 Using Ontologies to Formalize Services Specifications in Multi-agent Systems
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    Chapter 8 Two Formal Gas Models for Multi-agent Sweeping and Obstacle Avoidance
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    Chapter 9 A Formal Analysis of Potential Energy in a Multi-agent System
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    Chapter 10 Agent-Based Chemical Plume Tracing Using Fluid Dynamics
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    Chapter 11 Towards Timed Automata and Multi-agent Systems
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    Chapter 12 An Approach to V&V of Embedded Adaptive Systems
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    Chapter 13 Verifying Multi-agent Systems via Unbounded Model Checking
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    Chapter 14 Towards Symbolic Model Checking for Multi-agent Systems via obdd ’s
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    Chapter 15 Formal Consistency Verification of Deliberative Agents with Respect to Communication Protocols
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    Chapter 16 F-OWL: An Inference Engine for Semantic Web
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    Chapter 17 Model-Driven Architecture for Agent-Based Systems
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    Chapter 18 Apoptosis and Self-Destruct: A Contribution to Autonomic Agents?
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    Chapter 19 Petri Nets as Modeling Tool for Emergent Agents
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    Chapter 20 Massive Multi-agent systems control
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    Chapter 21 Fuzzy Hybrid Deliberative/Reactive Paradigm (FHDRP)
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    Chapter 22 Interaction and Communication of Agents in Networks and Language Complexity Estimates
Attention for Chapter 16: F-OWL: An Inference Engine for Semantic Web
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Chapter title
F-OWL: An Inference Engine for Semantic Web
Chapter number 16
Book title
Formal Approaches to Agent-Based Systems
Published in
Lecture notes in computer science, April 2004
DOI 10.1007/978-3-540-30960-4_16
Book ISBNs
978-3-54-024422-6, 978-3-54-030960-4
Authors

Youyong Zou, Tim Finin, Harry Chen

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 48 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Malaysia 1 2%
Turkey 1 2%
United States 1 2%
Colombia 1 2%
Unknown 44 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 19 40%
Student > Master 8 17%
Researcher 6 13%
Professor > Associate Professor 4 8%
Student > Postgraduate 3 6%
Other 4 8%
Unknown 4 8%
Readers by discipline Count As %
Computer Science 36 75%
Engineering 2 4%
Agricultural and Biological Sciences 1 2%
Arts and Humanities 1 2%
Social Sciences 1 2%
Other 1 2%
Unknown 6 13%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 27 October 2014.
All research outputs
#12,905,203
of 22,768,097 outputs
Outputs from Lecture notes in computer science
#3,775
of 8,125 outputs
Outputs of similar age
#51,159
of 58,284 outputs
Outputs of similar age from Lecture notes in computer science
#11
of 16 outputs
Altmetric has tracked 22,768,097 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,125 research outputs from this source. They receive a mean Attention Score of 5.0. This one has gotten more attention than average, scoring higher than 51% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 58,284 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 11th percentile – i.e., 11% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 16 others from the same source and published within six weeks on either side of this one. This one is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.