Foundations of Foundations of Artificial Intelligence

Foundations of Artificial Intelligence (edited by David Kirsh, MIT Press, 1992) presents a number of chapters from major players in artificial intelligence (AI), including Kirsh, Nilsson, Birnbaum, Hewitt, Gasser, Brooks, Lenat & Feigenbaum, Smith, Rosenbloom and the Soar team, and Norman. These chapters discuss fundamental assumptions underlying the dominant approaches to AI today. Perhaps the best parts of the book are the critiques: each chapter is followed by an in-depth critique that evaluates the utility of those assumptions in pursuing the goal of AI.

But what is the goal of AI? Although several chapters propose definitions of the AI enterprise, there seems to be little agreement even at this fundamental level. Kirsh discusses the following definition in his introduction:

  • A theory in AI is a specification of the knowledge underpinning a cognitive skill. (p. 5)

While there appears to be a broad consensus (with some dissension from Brooks) that knowledge specification is an important part of the practice of AI, there seems to be little agreement that knowledge specification by itself constitutes a theory in AI. Indeed, while Lenat and Feigenbaum take this position seriously, Nilsson focusses on the language for the specification of such knowledge (rather than the knowledge itself); Hewitt on communication between agents; Rosenbloom, Laird, Newell, and McCarl on architectural issues in lieu of knowledge; and Brooks eschews explicit representations of knowledge altogether.

This lack of consensus is both the principal strength and weakness of the book. […] In our view, a theory of intelligent behavior should have a descriptive part and an explanatory part. The descriptive part specifies the computational mechanisms of the theory, and makes clear how the program instantiates those mechanisms. Computational mechanisms can be described under the following headings:

  • Knowledge: both the content of the relevant knowledge and the representation language used to express that knowledge.
  • Processes: the algorithms or mechanisms that produce the intelligent behavior.
  • Architecture: the “cognitive architecture” on which the algorithms execute.
  • Machine architecture: the physical hardware, if this happens to be theoretically relevant.

A theory of intelligent behavior also has an explanatory part, which justifies the computational mechanisms of the theory by explaining the way in which they are a good account of the behavior. The explanation provides a functional or teleological basis for the design decisions underlying the computational model, such as the choice of representational primitives and formalisms, and architectural and algorithmic commitments. The explanation should also make clear how the computer implementation exemplifies this account.

Read the full review:

Foundations of Foundations of Artificial Intelligence

by Ashwin Ram, Eric Jones

Philosophical Psychology, 8(2):193-199, 1995

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