Artificial Intelligence Planning

Artificial intelligence planning is a branch of AI 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.

As I explained in a previous post "The General Problem Solver," one of the limitations of early AI, which was based on the physical symbol system hypothesis (PSSH), is combinatorial explosion — a mathematical phenomenon in which the number of possible combinations increases beyond the computer's capability to explore all of them in a reasonable amount of time.

Heuristic Reasoning

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. For example, imagine heuristic reasoning applied to the Chinese room experiment I introduced in my previous post on the general problem solver.

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 . . . possibly for the rest of your life.

Good Old-Fashioned 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.

Defining Intelligence in Artificial Intelligence

To understand the concept of artificial intelligence, we must first grasp the concept of intelligence. According to the dictionary definition, intelligence is a "capacity for learning, reasoning, understanding, and similar forms of mental activity; aptitude for grasping truths, relationships, facts, meanings, etc." This definition is broad enough to cover both human and computer (artificial) intelligence. Both people and computers can learn, reason, understand relationships, distinguish facts from falsehoods and so forth.

However, some definitions of intelligence raise the bar to include consciousness or self-awareness, wisdom, emotion, sympathy, intuition and creativity. In some definitions, intelligence also involves spirituality — a connection to a greater force or being. These definitions separate natural, human intelligence from artificial intelligence, at least in the current, real world. In science fiction, in futuristic worlds, artificially intelligent computers and robots often make the leap to self-consciousness and self-determination, which leads to conflict with their human creators. In The Terminator, artificial intelligence leads to all-out war between humans and the intelligent machines they created.

Other Challenges in Defining Intelligence

A further challenge to our ability to define “intelligence” is the fact that human intelligence comes in many forms and often includes the element of creativity. While computers can be proficient at math, repetitive tasks, playing certain games (such as chess), and anything else a human being can program them to do (or to learn to do), people excel in a variety of fields, including math, science, art, music, politics, business, medicine, law, linguistics and so on.

Another challenge to defining intelligence is that we have no definitive standard for measuring it. We do have intelligent quotient (IQ) tests, but a typical IQ test evaluates only short-term memory, analytical thinking, mathematical ability and spatial recognition. In high school, we take ACTs and SATs to gauge our mastery of what we should have learned in school, but the results from those tests don't always reflect a person's true intelligence. In addition, while some people excel in academics, others are skilled in trades or have a higher level of emotional competence or spirituality. There are also people who fail in school but still manage to excel in business, politics, or their chosen careers.

Without a reliable standard for measuring human intelligence, it’s very difficult to point to a computer and say that it's behaving intelligently. Computers are certainly very good at performing certain tasks and may do so much better and faster than humans, but does that make them intelligent? For example, computers have been able to beat humans in chess for decades. IBM Watson beat some of the best champions in the game show Jeopardy. Google's DeepMind has beaten the best players in the 2,500-year-old Chinese game called “Go” — a game so complex that there are thought to be more possible configurations of the board than there are atoms in the universe. Yet none of these computers understands the purpose of a game or has a desire to play.

Expertise in Pattern-Matching

As impressive as these accomplishments are, they are still just a product of a computer’s special talent for pattern-matching. Pattern-matching is what happens when a computer extracts information from its database and uses that information to answer a question or perform a task. This seems to be intelligent behavior only because a computer is excellent at that particular task. However, excellence at performing a specific task is not necessarily a reflection of intelligence in a human sense. Just because a computer can beat a chess master does not mean that the computer is more intelligent. We generally don't measure a machine's capability in human terms—for example, we don't describe a boat as swimming faster than a human or a hydraulic jack as being stronger than a weightlifter—so it makes little sense to describe a computer as being smarter or more intelligent just because it is better at performing a specific task.

Pattern Matching

A computer's proficiency at pattern-matching can make it appear to be intelligent in a human sense. For example, computers often beat humans at games traditionally associated with intelligence. But games are the perfect environments for computers to mimic human intelligence through pattern-matching. Every game has specific rules with a certain number of possibilities that can be stored in a database. When IBM's Watson played Jeopardy, all it needed to do was use natural language processing (NLP) to understand the question, buzz in faster than the other contestants, and apply pattern-matching to find the correct answer in its database.

Good Old-Fashioned Artificial Intelligence (GOFAI)

Early AI developers knew that computers had the potential to excel in a world of fixed rules and possibilities. Only a few years after the first AI conference, developers had their first version of a chess program. The program could match an opponent’s move with thousands of possible counter moves and play out thousands of games to determine the potential ramifications of making a move before deciding which piece to move and where to move it, and it could do so in a matter of seconds.

Artificial intelligence is always more impressive when computers are on their home turf — when the rules are clear and the possibilities limited. Organizations that benefit most from AI are those that work within a well-defined space with set rules, so it’s no surprise that organizations like Google fully embrace AI. Google’s entire business involves pattern-matching — matching users’ questions with a massive database of answers. AI experts often refer to this as good old-fashioned artificial intelligence (GOFAI).

If you're thinking about incorporating AI in your business, consider what computers are really good at — pattern-matching. Do you have a lot of pattern-matching in your organization? Does a lot of your work have set rules and possibilities? It will be this work that is first to benefit from AI.