Archive for January 1st, 1995

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

Understanding the Creative Mind

Margaret Boden, a master at bring ideas from artificial intelligence and cognitive science to the masses, has done it again. In The Creative Mind: Myths and Mechanisms (published by Routledge, 2003), she has produced a well-written, well-argued review and synthesis of current computational theories relevant to creativity. This book seems appropriately pitched for students in survey courses and for the intelligent lay public. And if ever there were a topic suitable for bridging the gap between researchers adh the layperson, this is surely it: What is creativity, and how is it possible? Or, in computational terms (the terms that Boden argoes ought to be applied), what are the processes of creativity?

We believe that in order to analyze creative reasoning, one needs a theoretical framework in which to model thinking. To this end, we propose using a computational approach rooted in case-based reasoning. This paradigm is fundamentally concerned with memory issues, such as remindings from partial matches at varying levels of representation and the formation of analogical maps between seemingly disparate situations—exactly the kinds of phenomena that researchers up to, and including, Boden have highlighted as central to creativity.

Our research suggests that creativity is not a process in itself that can be turned on or off; rather, it arises from the confluence and complex interaction of inferences using multiple kinds of knowledge in the context of a task or problem and in the context of a specific situation. Much of what we think of as “creativity” arises from interesting strategic control of these inferences and their integration in the context of a task and situation.

These five aspects—inferences, knowledge, task, situation, and control—are not special or unique to creativity but are part of normal everyday thinking. They determine the thinkable, the thoughts the reasoner might normally have when addressing a problem or performing a task. In a specific individual, more creative thoughts will likely result when these pieces come together in a novel way to yield unexplored and unexpected paths that go “beyond the thinkable”.

Read the full review:

Understanding the Creative Mind

by Ashwin Ram, Linda Wills, Eric Domeshek, Nancy Nersessian, Janet Kolodner

Artificial Intelligence journal, 79(1):111-128, 1995

Cognitive Media Types for Multimedia Information Access

Multimedia repositories, libraries, and databases offer the potential for providing students with access to a wide variety of interconnected information resources. However, in order to realize this potential, multimedia systems should provide access to information and activities that support effective knowledge construction and learning by students. This article proposes a theoretical framework for organizing information and activities in educational hypermedia systems. We show that such systems should not be characterized primarily in terms of the kinds of physical media types that can be accessed; instead, the important aspect is the content that can be represented within a physical media, rather than the physical media itself.

We propose a theory of “cognitive media types”based on the inferential and learning processes of human users. The theory highlights specific media characteristics that facilitate specific problem solving actions, which in turn are enabled by specific kinds of physical media. We present an implemented computer system, called AlgoNet, that supports hypermedia information access and constructive learning activities for self-paced learning in computer and engineering disciplines. Extensive empirical evaluations with undergraduate students suggest that self-paced interactive learning environments, coupled with multimedia information access and constructive activities organized into cognitive media types, can support and help students develop deep intuitions about important concepts in a given domain.

Read the paper:

Cognitive Media Types for Multimedia Information Access

by Mimi Recker, Ashwin Ram, Terry Shikano, George Li, John Stasko

Journal of Educational Multimedia and Hypermedia, 4(2/3):185-210, 1995. Earlier version presented at the.Annual Meeting of the American Educational Research Association (AERA), San Franciso, 1995.