Automated reasoning has been receiving much interest from a number of fields, including philosophy, cognitive science, and computer science. In this chapter, we consider the particular interest of computer science in automated reasoning over the last few decades, and then focus our attention on probabilistic reasoning using Bayesian networks, which is the main subject of this book.

Automated reasoning

The interest in automated reasoning within computer science dates back to the very early days of artificial intelligence (AI), when much work had been initiated for developing computer programs for solving problems that require a high degree of intelligence. Indeed, an influential proposal for building automated reasoning systems was extended by John McCarthy shortly after the term “artificial intelligence” was coined [McCarthy, 1959]. This proposal, sketched in Figure 1.1, calls for a system with two components: a knowledge base, which encodes what we know about the world, and a reasoner (inference engine), which acts on the knowledge base to answer queries of interest. For example, the knowledge base may encode what we know about the theory of sets in mathematics, and the reasoner may be used to prove various theorems about this domain.

McCarthy's proposal was actually more specific than what is suggested by Figure 1.1, as he called for expressing the knowledge base using statements in a suitable logic, and for using logical deduction in realizing the reasoning engine; see Figure 1.2. McCarthy's proposal can then be viewed as having two distinct and orthogonal elements.