Artificial intelligence systems, known as agents, require knowledge to make inferences and respond to requests. Programmers can encode knowledge and the ability to acquire more over time. The level of knowledge is above symbols and involves a library of logical information and goals. Knowledge level coding can take time and involve debugging. The more sophisticated the AI, the higher the level of knowledge. Users can interact with the system to test its programming. Systems that can make complex inferences are more powerful and flexible.
The level of knowledge is the rationale for the behavior of a system using artificial intelligence. Known as agents, such systems need knowledge to make inferences about the world and act in response to specific requests. In developing such systems, programmers can encode knowledge as well as the ability to acquire more of it over time through observation and study of their surroundings.
AI researchers proposed the one-level-of-knowledge model in the 1980s, when they began dealing with more sophisticated agents in their studies. The topic has been the subject of further study and discussion among people interested in defining the components of artificial intelligence systems. Understanding how such systems work can help people code better ones over time.
This is above the level of symbols, the mechanical basis used to support system operations. At the knowledge level, an agent has a library of logical information that they can use along with goals for using that information. If the system appears to behave rationally, even if an answer is incorrect or doesn’t make sense, it is showing use of its level of knowledge. For example, an agent might have false information that two plus two equals five. When asked what two plus two is, she answered five, showing that he has a goal to answer the question and that he uses existing knowledge to achieve it.
Knowledge level coding can take time and may involve debugging to remove incorrect, contradictory, or confusing information. The more sophisticated an AI, the higher the level of knowledge and the more ways it has to apply the information it stores. This is often encoded in a series of sentences that the system can use in logic tests in response to a prompt. For example, an agent controlling a chemical process might have a statement that says if temperatures rise above a certain level, she must take action to cool the process equipment to prevent an accident.
Artificial intelligence research examines both how such systems are built and how they respond to their environment. At the knowledge level, users can interact with the system to see how well it has been programmed. Information gaps and an inability to learn are signs that an agent is not flexible enough to adjust over time. Systems that can make complex inferences, especially if they can involve logical leaps, are more powerful and can be usable in more settings.
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