Artificial Intelligence (AI) Planning Systems
AI Planning Systems is a branch of Artificial Intelligence whose purpose is to identify strategies and action sequences that will, with a reasonable degree of confidence, enable the AI program to deliver the correct answer, solution, or outcome.
Early AI used the physical symbol system hypothesis (PSSH). This approach attempted to "program" intelligence. If you see a bird, say "this is a bird." As you can imagine, this type of programmed intelligence lead to "combinatorial explosion." This is when the number of possible combinations increases beyond the computer's capability to explore all of them in a reasonable amount of time.
AI planning system
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. Heuristic reasoning enables an AI program to rule out a large number of possible combinations by identifying them as impossible or highly unlikely. This approach is sometimes referred to as "limiting the search space."
A heuristic is a mental shortcut or rule-of-thumb that enables people to solve problems and make decisions quickly. For example, the Rule of 72 is a heuristic for estimating the number of years it would take an investment to double your money. You divide 72 by the rate of return, so an investment with a 6% rate of return would double your money in about 72/6 = 12 years.
Heuristic reasoning is common in innovation. Inventors rarely consider all the possibilities for solving a particular problem. Instead, they start with an idea, a hypothesis, or a hunch based on their knowledge and prior experience, then they start experimenting and exploring from that point forward. If they were to consider all the possibilities, they would waste considerable time, effort, energy, and expertise on futile experiments and research.
Heuristic Reasoning Combined with a Physical Symbol System
With AI planning, you might combine heuristic reasoning with a physical symbol system to improve performance. Imagine this applied to John Searle's Chinese room scenario.
In the Chinese room scenario, you, an English-only speaker, are locked in a room with a narrow slot on the door through which notes can pass. You have a book filled with long lists of statements in Chinese, and the floor is covered in Chinese characters. You are instructed that upon receiving a certain sequence of Chinese characters, you are to look up a corresponding response in the book and, using the characters strewn about the floor, formulate your response.
What you do in the Chinese room is very similar to how AI programs work. They simply identify patterns, look up entries in a database that correspond to those patterns, and output the entries in response.
With the addition of heuristic reasoning, AI could limit the possibilities of the first note. For example, you could program the software to expect a message such as "Hello" or "How are you?” In effect, this would limit the search space, so that the AI program had to search only a limited number of records in its database to find an appropriate response. It wouldn't get bogged down searching the entire database to consider all possible messages and responses.
The only drawback is that if the first message was not one of those that was anticipated, the AI program would need to search its entire database.
A Real-World Example
Heuristic reasoning is commonly employed in modern "AI" applications. For example, if you enter your location and destination in a GPS app, the app doesn't search its vast database of source data, which consists of satellite and aerial imagery; state, city, and county maps; the US Geological Survey; traffic data; and so on. Instead, it limits the search space to the area that encompasses the location and destination you entered. In addition, it limits the output to the fastest or shortest route (not both) depending on which setting is in force, and it likely omits a great deal of detail from its maps to further expedite the process.
The goal is to deliver an accurate map and directions, in a reasonable amount of time, that lead you from your current location to your desired destination as quickly as possible. Without the shortcuts to the process provided by heuristic reasoning, the resulting combinatorial explosion would leave you waiting for directions... for a very long time.
Good Old-Fashioned Applications of AI
Even though many of the modern AI applications are built on what are now considered old-fashioned methods, AI planning allows for the intelligent combination of these methods, along with newer methods, to build AI applications that deliver the desired output. The resulting applications can certainly make computers appear to be intelligent beings — providing real-time guidance from point A to point B, analyzing contracts, automating logistics, and even building better video games.
If you're considering a new AI project, don't be quick to dismiss the benefits of good old-fashioned AI (GOFAI). Newer approaches may not be the right fit.
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Frequently Asked Questions
What are planning domain modelling languages (PDDL)?
A Planning Domain Definition Language helps you set up a planning problem. It shows the states, actions, rules, and goals. It makes it clear what the planning area is so that a planner can understand and work with it.
What is the difference between planning and scheduling in AI?
Planning involves deciding on a sequence of actions to achieve a goal, while scheduling focuses on allocating resources and timing for those actions. A planning system in AI often integrates both to handle complex scenarios effectively.
What are some applications of AI planning systems?
AI planning systems are used in various applications such as robotics, supply chain management, autonomous vehicles, cybersecurity, and space exploration.
You can use planning software to make tasks easier. It helps you optimize resources and improve decision-making processes.
What is domain-independent planning?
Domain-independent planning refers to approaches not tailored to a specific problem domain. Instead, they use general algorithms for planning that can be applied to various types of problems across different domains.
What is classical planning?
Classical planning is a part of AI planning. Classical planning is about environments where you can predict what will happen when you do a certain action.
It involves creating actions that transform a starting state into the desired outcome.
Can machine learning be combined with AI planning?
Yes, machine learning can be combined with AI planning to improve the capabilities of planning systems. For instance, reinforcement learning can be used to develop better planning algorithms by learning from interactions with the environment.
What are hierarchical planning and temporal planning in AI?
Hierarchical planning involves breaking down a complex planning problem into smaller, more manageable subproblems.
Temporal planning focuses on generating plans considering the timing and durations of actions.
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Absolutely. While generative AI is powerful, GOFAI systems offer reliability and transparency—crucial for tasks where consistency matters. A balanced approach often brings the best results.
📚 Author | Learning and Dev. Professional | Guest Faculty & Speaker | The purpose of my content is to leave you healthier and better🧘♂️
3wThanks for sharing, Doug, This is insightful and exciting information!!
Chief Digital Transformation Consultant at SumatoSoft | Your trusted software developement partner.
3wAlso love the GPS example - perfect way to illustrate how AI planning works in real life.
Economiste
3wVery interesting. Thank you so much for sharing Doug Rose
Environmental & Linen Services Manager| System & Auditing
3wVery insightful. This good article touches on how to apply Heuristic reasoning to AI Planning for real life solutions.