Artificial Intelligence with Prolog


What is intelligence? Various explanations and definitions have been put forward by different researchers. Intelligence has been defined, among many other proposals, as the ability to acquire and apply knowledge, and as the capacity for logical reasoning and self-awareness. It has been related to understanding, solving, planning, and several other concepts and tasks. It has been categorized into different kinds of intelligence, such as emotional intelligence, social intelligence and so on.

The word stems from the Latin verb intellego "I understand, realize, perceive, see, notice" (among other meanings), from lego "I gather, collect, take, read" (among other meanings).

Since no generally accepted definition of intelligence is available, it is even harder to define what artificial intelligence is. Broadly speaking, we can distinguish two different categories of artificial intelligence (AI): So far, we have seen a lot of progress in weak AI. For example, machines have been built that play chess, Go, Jeopardy and many other games even better than the most able humans. Many of us are routinely using partially self-driving trains and subways, and we currently witness the first attempts at self-driving cars.

In contrast, strong AI has remained out of reach. In fact, it has been argued, and further argued, that strong AI is inherently impossible to implement.


Broadly speaking, we distinguish two possible approaches towards implementing AI:
  1. symbolic approaches:
  2. statistical approaches:
Both approaches have been widely known among researchers, and have also been used in practice, for many decades. Prolog is frequently associated with symbolic approaches. Well-known applications of this kind are planning tasks such as Wumpus World, Escape from Zurg, Connect 4, scheduling tasks such as timetabling and sports tournaments, and also other combinatorial tasks such as N queens and Sudoku. Prolog can of course also implement statistical approaches. For example, check out ProbLog, cplint and AILog 2 for probabilistic logic programming and reasoning with uncertainties.

The approaches have different advantages and drawbacks: A symbolic approach is typically amenable to formal verification. The rules are explicitly available, and you can check whether they are complete, and whether they express what you intend. Using inductive logic programming (ILP), you can even let a program learn rules based on positive and negative examples. See for example the Metagol system. However, not all tasks are amenable to symbolic approaches, because the rules may be too hard to express. For example, it is hard to express rules that determine whether a musical piece is borderline atonal, or whether a photo contains a painting of a flower.

On the other hand, statistical approaches have the ability to acquire rules implicitly, based on so-called training-, test- and validation sets. This is convenient, because you do not have to think about the rules yourself, and the machine may derive rules that are too hard for you to encode. Unfortunately, this also comes with significant drawbacks: You can no longer reason about the rules explicitly, and the obtained mechanism may produce colossal outliers that are hard to remedy. For example, it may misclassify the picture of a penguin as a car if you change a single pixel.

Prolog and AI

Prolog has very strong historic ties with AI. In 1982, Japan started a very ambitious government project called the Fifth Generation Computer System (FGCS) with the goal to create a massively parallel computer, using concurrent logic programming as the software foundation of the project.

When I visited Japan, I asked a retired Japanese scientist who had worked on the project why the project had failed. He said: "What do you mean, 'failed'? All my colleagues who have worked on the project went on to become professors!" Another researcher told me that the project achieved what was actually intended: a way to input Japanese characters into computers.

In my view, a major achievement of the scientists who had worked on this project was that they managed to convince the government to fund an interesting and very ambitious project with large sums.

By the end of the FGCS project, commodity hardware had become so powerful that custom-built machines as those constructed by the project were hardly needed anymore.

Now, several decades later, is it not time to try again? Is it not time to convince our governments and funding agencies that we want to benefit from the power of Prolog to build applications, and do interesting research?

Here are a few ideas:

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