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Agent. The other answer defines an agent as a policy (as it's defined in reinforcement learning). However, although this definition is fine for most current purposes, given that currently agents are mainly used to solve video games, in the real world, an intelligent agent will also need to have a body, which Russell and Norvig call an architecture (section 2.4 of the 3rd edition of Artificial ...
A model-based reflex agent needs memory for storing the percept history; it uses the percept history to help to reveal the current unobservable aspects of the environment. An example of this IA class is the self-steering mobile vision , where it's necessary to check the percept history to fully understand how the world is evolving.
1. A learning agent can be defined as an agent that, over time, improves its performance (which can be defined in different ways depending on the context) based on the interaction with the environment (or experience). The human is an example of a learning agent. For example, a human can learn to ride a bicycle, even though, at birth, no human ...
6. When we use the term rationality in AI, it tends to conform to the game theory / decision theory definition of rational agent. In a solved or tractable game, an agent can have perfect rationality. If the game is intractable, rationality is necessarily bounded. (Here, "game" can be taken to mean any problem.)
For more details regarding the definition of an agent in AI, see my answer to the question What is an agent in Artificial Intelligence?. A multi-agent system is a system composed of multiple agents that interact with an environment. See Multi-Agent Systems: A survey (2018) fore a more exhaustive overview of the field.
What is an agent in Artificial Intelligence? While studying artificial intelligence, I have often encountered the term "agent" (often autonomous, intelligent). For instance, in fields such as Reinforcement Learning, Multi-Agent Systems, Game ...
Utility is a fundamental to Artificial Intelligence because it is the means by which we evaluate an agent's performance in relation to a problem. To distinguish between the concept of economic utility and utility-based computing functions, the term "performance measure" is utilized.
The key difference between a learning agent and non-learning agents is that the learning agent can improve it's performance on it's own, allowing it to get "smarter". Russel & Norvig cover the different types of intelligent agents in detail in their textbook Artificial Intelligence: A Modern Approach , and the wikipedia entry for intelligent ...
$\begingroup$ Hutter and Legg's definition of intelligence is based on Hutter's AIXI framework, and it's an optimization-based definition: basically, intelligence is a measure of the capability of an agent to optimize with respect to a "wide range of" environments.
The motivation for this description of "agent" arose from a desire to have a quantitative model - it's not clear that such a model is a good fit for human cognition. However, there are alternative definitions of agents, for example the BDI model , which are rather more open-ended and hence more obviously applicable to humans.