Artificial Intelligence Planning Systems

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In the late 1980s early expert systems started to disappear, but a symbolic approach remained. Today, you see it in what’s called artificial intelligence planning — a branch of AI that employs strategies and action sequences to enhance the computer’s ability to match symbols and patterns.

AI planning attempts to solve the problem of combinatorial explosion 🤯 by using something called heuristic reasoning — an approach that attempts to give artificial intelligence a form of common sense, thus limiting the number of patterns the program has to match at any one time. This approach is sometimes referred to as “limiting the search space.”

Imagine heuristic reasoning applied to the Chinese room experiment. You could use heuristic reasoning in an AI program to limit the possibilities of the first note. You can set it up so that the program expects a message like “Hello” or “How are you?” or “Do you speak Chinese?” to limit how far the program has to search to match the pattern.

The drawback is that if the program doesn’t receive the anticipated input, it then has to search the entire database for a match, as well, which requires additional processing. For example, suppose the first note asks, “Do you know how to say purple in English?” The program must first rule out the anticipated messages and then search the entire database, or what AI planners refer to as the “entire search space.”

AI planning is common with navigation systems such as Google Maps. You enter your location and your destination, and the system finds the shortest, fastest route. It still uses a symbolic approach that relies on lists, and Google must gather the data to create those lists. It does so by pulling data from numerous sources, including satellite and aerial imagery; state, city and county maps; the US Geological Survey; its own Street View cars; and from users who contribute their own map information. All of this data is carefully vetted and then stitched together to create highly detailed maps. Google Maps also extracts current traffic data from local highway authorities to help route drivers around accidents and backups.

Unlike early symbolic systems, Google Maps uses heuristic reasoning to limit its search to a certain geographical area based on the location and destination you enter, and it can provide detailed directions, such as whether to turn left or right at a given intersection without having to search through its entire database of symbols and patterns.

Even though it’s considered old-fashioned AI, symbolic systems and AI planning are still used in many new projects. It performs very well in systems that have predefined symbols and patterns. You can see this with driving directions, but it also works with contracts, logistics and even video games. If you’re considering a new AI project, don’t be quick to dismiss the benefits of good old-fashioned AI. Newer approaches may not be the right fit.

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