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Such planners are called "domain independent" to emphasize the fact that they can solve planning problems from a wide range of domains. Typical examples of domains are block-stacking, logistics, workflow management, and robot task planning. Hence a single domain-independent planner can be used to solve planning problems in all these various ...
Toy problems were invented with the aim to program an AI which can solve it. The blocks world domain is an example for a toy problem. Its major advantage over more realistic AI applications is, that many algorithms and software programs are available which can handle the situation. [2] This allows to compare different theories against each other.
"Autonomous agents are computational systems that inhabit some complex dynamic environment, sense and act autonomously in this environment, and by doing so realize a set of goals or tasks for which they are designed." [3] Franklin and Graesser (1997) review different definitions and propose their definition:
The CFO Survey, a collaboration of Duke and the Atlanta and Richmond Fed banks, found that nearly one in three (32%) firms — large or small — plan to use AI in the next year to complete tasks ...
Moreover, many tasks may be carried out inadequately by artificial intelligence even if its algorithms were transparent, understood, bias-free, apparently effective, and goal-aligned and its trained data sufficiently large and cleansed – such as in cases were the underlying or available metrics, values or data are inappropriate.
In the context of generative artificial intelligence, AI agents [31] (also known as compound AI systems, [31] agentic AI, [32] [33] [34] large action models, [32] or large agent models [32]) are agents defined by a spectrum of attributes: complexity of their environment, complexity of their goals, a user interface based on natural language ...
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