Intelligent Agents

 Intelligent agents try to achieve goals by using sensors to calculate their reaction to the environment. Nikola Kasabov defines intelligent agents with certain characteristics; 

• accommodate new problem solving rules incrementally
• adapt online and in real time
• be able to analyze itself in terms of behavior, error and success.
• learn and improve through interaction with the environment
• learn quickly from large amounts of data
• have memory-based exemplar storage and retrieval capacities
• have parameters to represent short and long term memory, age, forgetting, etc.

Russel and Norvig groups intelligent agents into five classes that represent their intelligence and capability. These range from simple reflex agents, where they act on if condition then action, to learning agents, which can expand their basic knowledge due to feedback and from that they can make informed decisions. There are other agents apart from the five classes defined by Russel and Norvig.

An common example of intelligent agents in games is Wumpus World(Hunt the Wumpus). It is a simple block world that can be used to represent reason and knowledge. The goal is to get the gold without dying and the wumpus has a set of rules to follow.