Bikramjit Banerjee's Publications
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Using Bayesian Networks to Model Agent Relationships
Bikramjit Banerjee, Anish Biswas, Manisha Mundhe, Sandip Debnath, and Sandip Sen. Using Bayesian Networks to Model Agent Relationships. Journal of Applied Artificial Intelligence: Special issue on "Deception, Fraud and Trust in Agent Societies, 14(9):867–880, 2000.
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Abstract
An agent-society of the future is envisioned to be as complex as a human society. Just like human societies, such multiagent systems (MAS) deserve an in-depth study of the dynamics, relationships, and interactions of the constituent agents. An agent in a MAS may have only approximate a priori estimates of the trustworthiness of another agent. But it can learn from interactions with other agents, resulting in more accurate models of these agents and their dependencies together with the influences of other environmental factors. Such models are proposed to be represented as Bayesian or belief networks. An objective mechanism is presented to enable an agent elicit crucial information from the environment regarding the true nature of the other agents. This mechanism allows the modeling agent to choose actions that will produce guaranteed minimal improvement of the model accuracy. The working of the proposed maxim in entropy procedure is demonstrated in a multiagent scenario.
BibTeX
@Article{Banerjee00:Using,
author = {Bikramjit Banerjee and Anish Biswas and Manisha Mundhe
and Sandip Debnath and Sandip Sen},
title = {Using Bayesian Networks to Model Agent Relationships},
journal = {Journal of Applied Artificial Intelligence: Special
issue on "Deception, Fraud and Trust in Agent Societies},
year = {2000},
volume = {14},
number = {9},
pages = {867--880},
abstract = {An agent-society of the future is envisioned to be as
complex as a human society. Just like human societies, such multiagent
systems (MAS) deserve an in-depth study of the dynamics, relationships,
and interactions of the constituent agents. An agent in a MAS may have
only approximate a priori estimates of the trustworthiness of another
agent. But it can learn from interactions with other agents, resulting
in more accurate models of these agents and their dependencies together
with the influences of other environmental factors. Such models are
proposed to be represented as Bayesian or belief networks. An objective
mechanism is presented to enable an agent elicit crucial information
from the environment regarding the true nature of the other agents.
This mechanism allows the modeling agent to choose actions that will
produce guaranteed minimal improvement of the model accuracy. The
working of the proposed maxim in entropy procedure is demonstrated in a
multiagent scenario.},
}
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