Most of the time, we think of search agents as programs that figure out an entire solution before taking action. They plan everything in advance and then execute their plan step by step. But in real-world scenarios, this approach isn’t always practical. That’s where online search agents come in. Instead of mapping out everything ahead of time, they make decisions as they go—taking an action, observing the results, and then deciding what to do next.
This method is especially useful in dynamic environments where circumstances can change quickly, or in situations where spending too much time planning could be costly. It also works well in unpredictable settings because it lets the agent focus on what’s actually happening rather than worrying about every possible scenario that might never occur. Of course, there's a trade-off: the more an agent plans ahead, the less likely it is to run into unexpected problems, but sometimes, quick decision-making is necessary.
Navigating the Unknown: The Power of Online Search
Online search is crucial in environments where an agent has little to no prior knowledge. Imagine dropping a robot into a brand-new building with no map. It has to explore its surroundings, learn about obstacles, and gradually build a mental model of how to get from one place to another.
This kind of problem isn’t limited to just robots. Think about how a newborn baby interacts with the world. It doesn’t know what each movement will do, but through trial and error, it learns how to control its body and understand cause and effect. This gradual discovery process is, in a way, a form of online search.
The Challenges of Online Search
Unlike traditional problem-solving methods that rely on pure computation, online search requires the agent to act first and learn from experience. To function, the agent typically has access to:
ACTIONS(s): A list of possible moves from a given state.
Step-cost function c(s, a, s’): The cost of an action, though the agent only learns this after seeing the outcome.
GOAL-TEST(s): A way to check whether the agent has reached its objective.
The agent can’t predict the results of its actions beforehand—it has to experience them firsthand. Take, for example, an agent navigating a maze. If it starts at (1,1), it doesn’t automatically know that moving up will take it to (1,2). It has to try the action to find out. Once there, it still doesn’t know if moving down will take it back to (1,1) or to some other location.
In some cases, the agent may have partial knowledge. A robot, for instance, might understand how movement works but still be unaware of where walls or obstacles are located. The key takeaway? Online search allows an agent to gradually figure out the unknown, adapting as it learns and improving its decision-making over time.
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