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«Mark H. Bickhard Lehigh University Loren Terveen AT&T Bell Laboratories forthcoming, 1995 Elsevier Science Publishers Contents Preface xi ...»

-- [ Page 1 ] --

FOUNDATIONAL ISSUES IN

ARTIFICIAL INTELLIGENCE AND

COGNITIVE SCIENCE

IMPASSE AND SOLUTION

Mark H. Bickhard

Lehigh University

Loren Terveen

AT&T Bell Laboratories

forthcoming, 1995

Elsevier Science Publishers

Contents

Preface xi

Introduction

A PREVIEW

I GENERAL CRITIQUE 5

1 Programmatic Arguments

CRITIQUES AND QUALIFICATIONS

DIAGNOSES AND SOLUTIONS

IN-PRINCIPLE ARGUMENTS

2 The Problem of Representation

ENCODINGISM

Circularity Incoherence — The Fundamental Flaw A First Rejoinder The Necessity of an Interpreter 3 Consequences of Encodingism

LOGICAL CONSEQUENCES

Skepticism Idealism Circular Microgenesis Incoherence Again Emergence 4 Responses to the Problems of Encodings

FALSE SOLUTIONS

Innatism Methodological Solipsism Direct Reference External Observer Semantics Internal Observer Semantics Observer Idealism Simulation Observer Idealism vi

SEDUCTIONS

Transduction

Correspondence as Encoding:

Confusing Factual and Epistemic Correspondence 5 Current Criticisms of AI and Cognitive Science

AN APORIA

Empty Symbols

ENCOUNTERS WITH THE ISSUES

Searle Gibson Piaget Maturana and Varela Dreyfus Hermeneutics 6 General Consequences of the Encodingism Impasse

REPRESENTATION

LEARNING

THE MENTAL

WHY ENCODINGISM?

II INTERACTIVISM:

AN ALTERNATIVE TO ENCODINGISM 53

7 The Interactive Model

BASIC EPISTEMOLOGY

Representation as Function Epistemic Contact: Interactive Differentiation and Implicit Definition Representational Content

EVOLUTIONARY FOUNDATIONS

SOME COGNITIVE PHENOMENA

Perception Learning Language 8 Implications for Foundational Mathematics

TARSKI

Encodings for Variables and Quantifiers Tarski’s Theorems and the Encodingism Incoherence Representational Systems Adequate to Their Own Semantics Observer Semantics Truth as a Counterexample to Encodingism vii

TURING

Semantics for the Turing Machine Tape Sequence, But Not Timing Is Timing Relevant to Cognition?

Transcending Turing Machines

III ENCODINGISM:

ASSUMPTIONS AND CONSEQUENCES 87

9 Representation: Issues within Encodingism

EXPLICIT ENCODINGISM IN THEORY AND PRACTICE

Physical Symbol Systems The Problem Space Hypothesis SOAR

PROLIFERATION OF BASIC ENCODINGS

CYC — Lenat’s Encyclopedia Project

TRUTH-VALUED VERSUS NON-TRUTH-VALUED

Procedural vs Declarative Representation

PROCEDURAL SEMANTICS

Still Just Input Correspondences

SITUATED AUTOMATA THEORY

NON-COGNITIVE FUNCTIONAL ANALYSIS

The Observer Perspective Again

BRIAN SMITH

Correspondence Participation No Interaction Correspondence is the Wrong Category

ADRIAN CUSSINS

INTERNAL TROUBLES

Too Many Correspondences Disjunctions Wide and Narrow Red Herrings 10 Representation: Issues about Encodingism

SOME EXPLORATIONS OF THE LITERATURE

Stevan Harnad Radu Bogdan Bill Clancey A General Note on Situated Cognition Rodney Brooks: Anti-Representationalist Robotics Agre and Chapman Benny Shanon viii Pragmatism Kuipers’ Critters Dynamic Systems Approaches

A DIAGNOSIS OF THE FRAME PROBLEMS

Some Interactivism-Encodingism Differences Implicit versus Explicit Classes of Input Strings Practical Implicitness: History and Context Practical Implicitness: Differentiation and Apperception Practical Implicitness: Apperceptive Context Sensitivities A Counterargument: The Power of Logic Incoherence: Still another corollary Counterfactual Frame Problems The Intra-object Frame Problem 11 Language

INTERACTIVIST VIEW OF COMMUNICATION

THEMES EMERGING FROM AI RESEARCH IN LANGUAGE

Awareness of the Context-dependency of Language Awareness of the Relational Distributivity of Meaning Awareness of Process in Meaning Toward a Goal-directed, Social Conception of Language Awareness of Goal-directedness of Language Awareness of Social, Interactive Nature of Language

Conclusions

12 Learning

RESTRICTION TO A COMBINATORIC SPACE OF ENCODINGS

LEARNING FORCES INTERACTIVISM





Passive Systems Skepticism, Disjunction, and the Necessity of Error for Learning Interactive Internal Error Conditions What Could be in Error?

Error as Failure of Interactive Functional Indications — of Interactive Implicit Predications Learning Forces Interactivism Learning and Interactivism

COMPUTATIONAL LEARNING THEORY

INDUCTION

GENETIC AI

Overview Convergences Differences Constructivism ix 13 Connectionism

OVERVIEW

STRENGTHS

WEAKNESSES

ENCODINGISM

CRITIQUING CONNECTIONISM AND

AI LANGUAGE APPROACHES

IV SOME NOVEL ARCHITECTURES 299

14 Interactivism and Connectionism

INTERACTIVISM AS AN INTEGRATING PERSPECTIVE

Hybrid Insufficiency

SOME INTERACTIVIST EXTENSIONS OF ARCHITECTURE

Distributivity Metanets 15 Foundations of an Interactivist Architecture

THE CENTRAL NERVOUS SYSTEM

Oscillations and Modulations Chemical Processing and Communication Modulatory “Computations” The Irrelevance of Standard Architectures A Summary of the Argument

PROPERTIES AND POTENTIALITIES

Oscillatory Dynamic Spaces Binding Dynamic Trajectories “Formal” Processes Recovered Differentiators In An Oscillatory Dynamics An Alternative Mathematics The Interactive Alternative

–  –  –

Artificial Intelligence and Cognitive Science are at a foundational impasse which is at best only partially recognized. This impasse has to do with assumptions concerning the nature of representation: standard approaches to representation are at root circular and incoherent. In particular, Artificial Intelligence research and Cognitive Science are conceptualized within a framework that assumes that cognitive processes can be modeled in terms of manipulations of encoded symbols.

Furthermore, the more recent developments of connectionism and Parallel Distributed Processing, even though the issue of manipulation is contentious, share the basic assumption concerning the encoding nature of representation. In all varieties of these approaches, representation is construed as some form of encoding correspondence. The presupposition that representation is constituted as encodings, while innocuous for some applied Artificial Intelligence research, is fatal for the further reaching programmatic aspirations of both Artificial Intelligence and Cognitive Science.

First, this encodingist assumption constitutes a presupposition about a basic aspect of mental phenomena — representation — rather than constituting a model of that phenomenon. Aspirations of Artificial Intelligence and Cognitive Science to provide any foundational account of representation are thus doomed to circularity: the encodingist approach presupposes what it purports to be (programmatically) able to explain.

Second, the encoding assumption is not only itself in need of explication and modeling, but, even more critically, the standard presupposition that representation is essentially constituted as encodings is logically fatally flawed. This flaw yields numerous subsidiary consequences, both conceptual and applied.

This book began as an article attempting to lay out this basic critique at the programmatic level. Terveen suggested that it would be more powerful to supplement the general critique with explorations of actual projects and positions in the fields, showing how the foundational flaws visit themselves upon the efforts of researchers. We began that task, and, among other things, discovered that there is no natural closure

xii Preface

to it — there are always more positions that could be considered, and they increase in number exponentially with time. There is no intent and no need, however, for our survey to be exhaustive. It is primarily illustrative and demonstrative of the problems that emerge from the underlying programmatic flaw. Our selections of what to include in the survey have had roughly three criteria. We favored: 1) major and well known work,

2) positions that illustrate interesting deleterious consequences of the encodingism framework, and 3) positions that illustrate the existence and power of moves in the direction of the alternative framework that we propose. We have ended up, en passant, with a representative survey of much of the field. Nevertheless, there remain many more positions and research projects that we would like to have been able to address.

The book has gestated and grown over several years. Thanks are due to many people who have contributed to its development, with multitudinous comments, criticisms, discussions, and suggestions on both the manuscript and the ideas behind it. These include, Gordon Bearn, Lesley Bickhard, Don Campbell, Robert Campbell, Bill Clancey, Bob Cooper, Eric Dietrich, Carol Feldman, Ken Ford, Charles Guignon, Cliff Hooker, Norm Melchert, Benny Shanon, Peter Slezak, and Tim Smithers.

Deepest thanks are also due to the Henry R. Luce Foundation for support to Mark Bickhard during the final years of this project.

Mark H. Bickhard Henry R. Luce Professor of Cognitive Robotics & the Philosophy of Knowledge Department of Psychology 17 Memorial Drive East Lehigh University Bethlehem, PA 18015 mhb0@lehigh.edu Loren Terveen Human Computer Interface Research AT&T Bell Laboratories 600 Mountain Avenue Murray Hill, NJ 07974 terveen@research.att.com Introduction How can we understand representation? How can we understand the mental? How can we build systems with genuine representation, with genuine mentality? These questions frame the ultimate programmatic aims of Artificial Intelligence and Cognitive Science. We argue that Artificial Intelligence and Cognitive Science are in the midst of a programmatic impasse — an impasse that makes these aims impossible — and we outline an alternative approach that transcends that impasse.

Most contemporary research in Artificial Intelligence and Cognitive Science proceeds within a common conceptual framework that is grounded on two fundamental assumptions: 1) the unproblematic nature of formal systems, and 2) the unproblematic nature of encoded, semantic symbols upon which those systems operate. The paradigmatic conceptual case, as well as the paradigmatic outcome of research, is a computer program that manipulates and operates on structures of encoded data — or, at least, a potentially programmable model of some phenomena of interest. The formal mathematical underpinnings of this approach stem from the introduction of Tarskian model theory and Turing machine theory in the 1930s. Current research focuses on the advances to be made, both conceptually and practically, through improvements in the programs and models and in the organization of the data structures.



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