Difference between revisions of "Exploration"
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* [[Agents#AI Agent Optimization|AI Agent Optimization]] ... [[Optimization Methods]] ... [[Optimizer]] ... [[Objective vs. Cost vs. Loss vs. Error Function]] ... [[Exploration]] | * [[Agents#AI Agent Optimization|AI Agent Optimization]] ... [[Optimization Methods]] ... [[Optimizer]] ... [[Objective vs. Cost vs. Loss vs. Error Function]] ... [[Exploration]] | ||
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| + | AI efforts, frameworks, and architectures can be broadly categorized into two main approaches: exploration and optimization for objectives. | ||
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| + | Exploration in AI refers to the process of discovering new information or knowledge through exploration. This involves AI systems learning and adapting based on their environment, aiming to understand and navigate complex problems or data spaces more effectively. Exploratory AI systems are designed to explore the unknown, identify patterns, and make decisions that maximize learning or discovery. This approach is particularly useful in fields like robotics, where systems need to adapt to new environments or tasks without prior knowledge. | ||
Revision as of 20:35, 5 March 2024
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- AI Agent Optimization ... Optimization Methods ... Optimizer ... Objective vs. Cost vs. Loss vs. Error Function ... Exploration
AI efforts, frameworks, and architectures can be broadly categorized into two main approaches: exploration and optimization for objectives.
Exploration in AI refers to the process of discovering new information or knowledge through exploration. This involves AI systems learning and adapting based on their environment, aiming to understand and navigate complex problems or data spaces more effectively. Exploratory AI systems are designed to explore the unknown, identify patterns, and make decisions that maximize learning or discovery. This approach is particularly useful in fields like robotics, where systems need to adapt to new environments or tasks without prior knowledge.
