Artificial Agents
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agent interaction and can be viewed from particular perspectives,
“lies in the eye of the beholder ”.
• Hybrid architecture: Combines two features stated above. Agents
have representation using deliberative architecture but also a reactive part, such that they react to the environment using symbolic
AI.
Additonally, Luck [51] presented more general agent attributes.
• Agent beliefs: Knowledge about itself and environment.
• Agent desires: The states the agent wants to achieve in response to
certain actions.
• Agent intentions: Plans adopted by agent.
• Plan library: Agents maintain a repository of available plans.
• Events: Agent actions using beliefs and goals.
Agents may sometimes be required to adopt goals of other agents. This
is argues in Social power theory where there is a dependence among
agents in a network for achieving their own goals. Such a system allows
agents to possess resources, creating the divide between some agents
being better off than others.
Agent language. Shoham [176] proposed agent-oriented programming as
• Logical system for defining a mental state of an agent.
• Interrelated programming language for programming agents.
• Low-level programs to convert agents in programming language.
Agent languages encompass the implementation aspects and techniques
as language representation of agents.
A g e n t a c t i o n s
A u t o n o m y
S o c i a l a b i l i t y
R e a c t i v i t y
P r o - a c t i v e n e s s
M o b i l i t y
V e r a c i t y
B e n e v o l e n c e
R a t i o n a l i t y
W e a k n o t i o n
S t r o n g n o t i o n
FIGURE 2.5: Weak and strong notions of agent actions. Cf. [205].
Learning in system. Most agent-based systems have mechanisms to learn
and adapt their behavior. These agents could be
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agent interaction and can be viewed from particular perspectives,
“lies in the eye of the beholder ”.
• Hybrid architecture: Combines two features stated above. Agents
have representation using deliberative architecture but also a reactive part, such that they react to the environment using symbolic
AI.
Additonally, Luck [51] presented more general agent attributes.
• Agent beliefs: Knowledge about itself and environment.
• Agent desires: The states the agent wants to achieve in response to
certain actions.
• Agent intentions: Plans adopted by agent.
• Plan library: Agents maintain a repository of available plans.
• Events: Agent actions using beliefs and goals.
Agents may sometimes be required to adopt goals of other agents. This
is argues in Social power theory where there is a dependence among
agents in a network for achieving their own goals. Such a system allows
agents to possess resources, creating the divide between some agents
being better off than others.
Agent language. Shoham [176] proposed agent-oriented programming as
• Logical system for defining a mental state of an agent.
• Interrelated programming language for programming agents.
• Low-level programs to convert agents in programming language.
Agent languages encompass the implementation aspects and techniques
as language representation of agents.
A g e n t a c t i o n s
A u t o n o m y
S o c i a l a b i l i t y
R e a c t i v i t y
P r o - a c t i v e n e s s
M o b i l i t y
V e r a c i t y
B e n e v o l e n c e
R a t i o n a l i t y
W e a k n o t i o n
S t r o n g n o t i o n
FIGURE 2.5: Weak and strong notions of agent actions. Cf. [205].
Learning in system. Most agent-based systems have mechanisms to learn
and adapt their behavior. These agents could be