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[1]
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M. Agar.
Agents in living color: Towards emic agent-based models.
Journal of Artificial Societies and Social Simulation, 8(1),
January 2005.
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Makes the case for using models that emphasize insider/emic
information as well as the usual outsider/etic information
Keywords: Agent-Based Models, Ethnography, Substance Use, Emic/Etc,
Validity, Netlogo
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[2]
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S. Blower and P. Volberding.
What can modeling tell us about the threat of antiviral drug
resistance?
Current Opinion in Infectious Diseases, 15:609-614, 2002.
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Discussion of the spread of HIV, Herpes, and other viruses.
Models are used to make predictions of how specific treatments
will prevent spreading of certain strains but will expedite
the spread of other, drug-resistant strains.
Keywords: Antiviral, Drug Resistance, Mathematical Model
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[3]
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A. Brogi.
Probabilistic behaviours of reactive agents.
Electronic Notes in Theoretical Computer Science, 48:1-26,
2001.
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A formalisation built on logic of the probabilistic actions of
reactive agents. Proofs are included of invariants in agent
behavior while in specific domains.
Keywords: None
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[4]
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C. Castelfranchi.
Through the minds of the agents.
Journal of Artificial Societies and Social Simulation, 1(1),
January 1998.
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Poses that rationality in agents does not necessarily equate
selfishness or a lack of cooperation with other agents
Keywords: Cooperation, Altruism, Rationality, Cognition
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[5]
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R. Conte.
Emergent (info)institutions.
Journal of Cognitive Systems Research, 2:97-110, 2001.
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Argues that evolutionary and designed algorithms could benefit
from the inclusion of guilt and responsibility factors in the
agents
Keywords: Infosocieties, Social Order, Reputation, Reciprocal Altruism,
Cognitive Agents
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[6]
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R. Conte and M. Paolucci.
Responsibility for societies of agents.
Journal of Artificial Societies and Social Simulation, 7(4),
October 2004.
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In-depth analysis on the nature of responsibility and how to
utilize the idea in multi-agent systems
Keywords: Responsibility, Agents, Cognitive Modeling, E-Governance,
Organization Theory
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[7]
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J. Delgado.
Emergence of social conventions in complex networks.
Artificial Intelligence, 141:171-185, 2001.
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Discussion of complex networks. The paper includes experiments
comparing the amount of time required for various kinds of
networks to reach agreement on social conventions.
Keywords: Conventions, Emergent Behavior, Coordination, Multi-agent
Systems
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[8]
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G. Fortino, W. Russo, and E. Zimeo.
A statecharts-based software development process for mobile agents.
Information and Software Technology, 46:907-921, 2004.
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A visual modeling procedure for designing lightweight mobile
agents. The process uses a Statecharts variant which can be
translated into Java code using the Mobile Active Object (MAO)
framework.
Keywords: Statecharts, Mobile Agents, Design Patterns, Frameworks, Java
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[9]
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E. Fredkin and T. Toffoli.
Conservative logic.
International Journal of Theoretical Physics, 21:219-253,
1982.
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An introduction to a mathematical model of computation that
puts ideas in natural physics to use in software design.
Keywords: None
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[10]
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K. Garfield.
Emergent behavior, genetic algorithms, and synthetic social
relationships.
International Journal of Theoretical Physics, May 2004.
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Looks like it was a short presentation. But nice overview of
ideas.
Keywords: None
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[11]
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C. Gershenson.
Philosophical ideas on the simulation of social behaviour.
Journal of Artificial Societies and Social Simulation, 5(3),
June 2002.
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Open-ended points concerning artificial societies, modeling,
and the nature of social interaction. Differences between
conclusions arrived at through modelling and those discovered
through other scientific experiments are examined.
Keywords: Complex Sysems, Modeling, Social Behavior, Synthetic Method
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[12]
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H. Iba.
Evolutionary learning of communicating agents.
Journal of Information Sciences, 108:181-205, 1998.
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Experiments in two domains shows agents evolving different
roles through Genetic Programming. Communication between
agents greatly increases the efficiency of achieving the agents'
goals
Keywords: Genetic Programming, Multi-agent System, Distributed Artificial
Intelligence
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[13]
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Nicholas R. Jennings, Katia Sycara, and Michael Wooldridge.
A roadmap of agent research and development.
Autonomous Agents and Multi-Agent Systems, 1:275-306, 1998.
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In-depth discussion of the theory behind agent-based software
and the research done in the field. Includes examples of current
applications
Keywords: autonomous agents, multi-agent systems, history
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[14]
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M. Keeling.
The mathematics of diseases.
plus magazine, 14, March 2001.
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Quick background on SIR (Susceptible, Infected, Recovered)
model
Keywords: None
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[15]
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A. Lavenu, A. Valleron, and F. Carrat.
Exploring cross-protection between influenza strains by an
epidemiological model.
Virus Research, 103:101-105, 2004.
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Uses computer models to show how various strains of influenza
often only produce one peak of infection
Keywords: Influenza, Model, Cross-protection, Illness Peak
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[16]
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J. Lefèvre.
A didactic presentation of elementary bond graphs for non-engineering
students.
Journal of Franklin Institute, 328(5):547-563, 1991.
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A process for explaining Bond Graphs that relies chiefly on
Kirchhoff's laws.
Keywords: None
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[17]
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J. Liu, H. Jing, and Y. Y. Tang.
Multi-agent oriented constraint satisfaction.
Artificial Intelligence, 136:101-144, 2002.
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Explanation of ERA (Environment, Reactive rules, and Agents),
a method for using multi-agent systems to solve constraint
satisfaction problems such as n-queens and coloring. ERA has
the advantage of being able to generate approximate solutions
if computation time is limited.
Keywords: Constraint Satisfaction, Reactive Moving Behaviors, Behavior
Prioritization, Behavior Selection, Experimental Validation
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[18]
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Arnold S. Monto.
Global burden of influenza: what we know and what we need to know.
International Congress Series, 1263:3-11, 2004.
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Background on the spread of influenza and a summary of compiled data
Keywords: Influenza illness, Pneumonia and influenza deaths, Influenza vaccine,
Tropical disease
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[19]
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J.D. Mooney, E. Holmes, and P. Christie.
Real-time modelling of influenza outbreaks.
Eurosurveillance, 7(12):184-187, 2002.
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Modelling of influenza outbreaks using consultation data and
linear regression. As new data comes in, one updates the
projected severity of the outbreak.
Keywords: None
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[20]
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S. Nolfi.
Power and limits of reactive agents.
Neurocomputing, 42:119-145, 2002.
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Experiments in several domains using mobile reactive agents
(sometimes with simple internal states). The paper discusses
the power of sensory-motor coordination and the emergent
behavior even purely reactive agents can exhibit.
Keywords: Sensory-motor Coordination, Embodied Intelligence, Reactive
Systems, Internal Representations, Evolutionary Robotics
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[21]
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Anand S. Rao and Michael P. Georgeff.
Bdi agents: From theory to practice.
In Proceedings of the First Intl. Conference on Multiagent
Systems, San Francisco, 1995.
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abstract
Keywords: None
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[22]
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F. Sadri and F. Toni.
Computational logic and multi-agent systems: a roadmap.
Technical report, December 1999.
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Overview of many current projects working on agent-based
computing. Discussion includes comparison of theory and
implementation between the projects, what is required or at
least important for practical agent-based computing.
Keywords: None
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[23]
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R. Sawyer.
Artificial societies: Multiagent systems and micro-macro link in
sociological theory.
Sociological Methods & Research, 31(3):325-363, February 2003.
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In-depth paper on the ideas and use of multi-agent systems in
Sociology
Keywords: Computer Simulation, Multi-agent Systems, Sociological Theory,
Emergence
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[24]
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M. Schillo, K. Fischer, and C. Klein.
The micro-macro link in dai and sociology.
Lecture Notes in Artificial Intelligence, 1979, July 2000.
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Discussion on the differences between usage of the terms
micro, meso, macro, etc. in sociology and distributed
artificial intelligence. The paper includes suggestions on how
sociology can aid in DAI's progress and vice versa.
Keywords: None
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[25]
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N. Stilianakis, A. Perelson, and F. Hayden.
Emergence of druge resistance during an influenza epidemic: Insights
from a mathematical model.
The Journal of Infectious Diseases, 177:863-873, 1998.
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Uses a mathematical model to analyze when and how
drugs/treatments could best be used to combat epidemics
Keywords: None
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[26]
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S. Woods, L. Hall, K. Dautenhahn, and D. Wolke.
Implications of gender differences for the development of animated
characters for the studying of bullying behavior.
Computers in Human Behavior, 2004.
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An experiment with animated agents in bullying scenarios shows
that boys empathize more with male characters than with
female characters. However, girls empathize with characters
of both genders well.
Keywords: Children, Technology, Education, Victimization, Virtual
Reality, Aggressive Behavior
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[27]
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Michael Wooldridge and Nicholas R. Jennings.
Intelligent agents: Theory and practice.
Knowledge Engineering Review, 10(2):115-152, 1995.
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Keywords:
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