Intelligence 2E

foundations of computational agents

The third edition of Artificial Intelligence: foundations of computational agents, Cambridge University Press, 2023 is now available (including full text).

Throughout human history, people have used technology to model themselves. There is evidence of this from ancient China, Egypt, and Greece bearing witness to the universality of this activity. Each new technology has, in its turn, been exploited to build intelligent agents or models of mind. Clockwork, hydraulics, telephone switching systems, holograms, analog computers, and digital computers have all been proposed both as technological metaphors for intelligence and as mechanisms for modeling mind.

About 400 years ago people started to write about the nature of thought and reason. Hobbes (1588–1679), who has been described by Haugeland [1985, p. 85] as the “Grandfather of AI,” espoused the position that thinking was symbolic reasoning like talking out loud or working out an answer with pen and paper. The idea of symbolic reasoning was further developed by Descartes (1596–1650), Pascal (1623–1662), Spinoza (1632–1677), Leibniz (1646–1716), and others who were pioneers in the philosophy of mind.

The idea of symbolic operations became more concrete with the development of computers. Babbage (1792–1871) designed the first general-purpose computer, the Analytical Engine, but it was not built until 1991 at the Science Museum of London. In the early part of the twentieth century, there was much work done on understanding computation. Several models of computation were proposed, including the Turing machine by Alan Turing (1912–1954), a theoretical machine that writes symbols on an infinitely long tape, and the lambda calculus of Church (1903–1995), which is a mathematical formalism for rewriting formulas. It can be shown that these very different formalisms are equivalent in that any function computable by one is computable by the others. This leads to the Church–Turing thesis:

Any effectively computable function can be carried out on a Turing machine (and so also in the lambda calculus or any of the other equivalent formalisms).

Here effectively computable means following well-defined operations; “computers” in Turing’s day were people who followed well-defined steps and computers as we know them today did not exist. This thesis says that all computation can be carried out on a Turing machine or one of the other equivalent computational machines. The Church–Turing thesis cannot be proved but it is a hypothesis that has stood the test of time. No one has built a machine that has carried out computation that cannot be computed by a Turing machine. There is no evidence that people can compute functions that are not Turing computable. An agent’s actions are a function of its abilities, its history, and its goals or preferences. This provides an argument that computation is more than just a metaphor for intelligence; reasoning is computation and computation can be carried out by a computer.

Once real computers were built, some of the first applications of computers were AI programs. For example, Samuel [1959] built a checkers program in 1952 and implemented a program that learns to play checkers in the late 1950s. His program beat the Connecticut state checkers champion in 1961. Wang [1960] implemented a program that proved every logic theorem (nearly 400) in Principia Mathematica [Whitehead and Russell, 1910]. Newell and Simon [1956] built a program, Logic Theorist, that discovers proofs in propositional logic.

In addition to work on high-level symbolic reasoning, there was also much work on low-level learning inspired by how neurons work. McCulloch and Pitts [1943] showed how a simple thresholding “formal neuron” could be the basis for a Turing-complete machine. The first learning for these neural networks was described by Minsky [1952]. One of the early significant works was the perceptron of Rosenblatt [1958]. The work on neural networks went into decline for a number of years after the 1968 book by Minsky and Papert [1988], which argued that the representations learned were inadequate for intelligent action.

The early programs concentrated on learning and search as the foundations of the field. It became apparent early that one of the main tasks was how to represent the knowledge required for intelligent action. Before learning, an agent must have an appropriate target language for the learned knowledge. There have been many proposals for representations from simple features to neural networks to the complex logical representations of McCarthy and Hayes [1969] and many in between, such as the frames of Minsky [1975].

During the 1960s and 1970s, natural language understanding systems were developed for limited domains. For example, the STUDENT program of Daniel Bobrow [1967] could solve high school algebra tasks expressed in natural language. Winograd’s [1972] SHRDLU system could, using restricted natural language, discuss and carry out tasks in a simulated blocks world. CHAT-80 [Warren and Pereira, 1982] could answer geographical questions placed to it in natural language. Figure 1.2 shows some questions that CHAT-80 answered based on a database of facts about countries, rivers, and so on. All of these systems could only reason in very limited domains using restricted vocabulary and sentence structure. Interestingly, IBM’s Watson, which beat the world champion in the TV game show Jeopardy!, used a similar technique to CHAT-80 [Lally et al., 2012]; see Section 13.6.

During the 1970s and 1980s, there was a large body of work on expert systems, where the aim was to capture the knowledge of an expert in some domain so that a computer could carry out expert tasks. For example, DENDRAL [Buchanan and Feigenbaum, 1978], developed from 1965 to 1983 in the field of organic chemistry, proposed plausible structures for new organic compounds. MYCIN [Buchanan and Shortliffe, 1984], developed from 1972 to 1980, diagnosed infectious diseases of the blood, prescribed antimicrobial therapy, and explained its reasoning. The 1970s and 1980s were also a period when AI reasoning became widespread in languages such as Prolog [Colmerauer and Roussel, 1996; Kowalski, 1988].

During the 1990s and the 2000s there was great growth in the subdisciplines of AI such as perception, probabilistic and decision-theoretic reasoning, planning, embodied systems, machine learning, and many other fields. There has also been much progress on the foundations of the field; these form the framework of this book.