Human-like machine intelligence / edited by Stephen Muggleton and Nicholas Chater.

Format:

Book

Contributor:

Muggleton, Stephen, editor.

Chater, Nick, editor.

Language:

English

Subjects (All):

Artificial intelligence.

Cognitive science.

Genre:

Electronic books.

Physical Description:

1 online resource (533 pages)

Place of Publication:

Oxford : Oxford University Press, [2021]

System Details:

text file

Summary:

This book, authored by an array of internationally recognised researchers, is of direct relevance to all those involved in Academia and Industry wanting to obtain insights into the topics at the forefront of the revolution in Artificial Intelligence and Cognitive Science.

Contents:

Cover

Human-Like Machine Intelligence

Copyright

Preface

Acknowledgements

Contents

Part 1: Human-like Machine Intelligence

1: Human-Compatible Artificial Intelligence

1.1 Introduction

1.2 Artificial Intelligence

1.3 1001 Reasons to Pay No Attention

1.4 Solutions

1.4.1 Assistance games

1.4.2 The off-switch game

1.4.3 Acting with unknown preferences

1.5 Reasons for Optimism

1.6 Obstacles

1.7 Looking Further Ahead

1.8 Conclusion

References

2: Alan Turing and Human-Like Intelligence

2.1 The Background to Turing's 1936 Paper

2.2 Introducing Turing Machines

2.3 The Fundamental Ideas of Turing's 1936 Paper

2.4 Justifying the Turing Machine

2.5 Was the Turing Machine Inspired by Human Computation?

2.6 From 1936 to 1950

2.7 Introducing the Imitation Game

2.8 Understanding the Turing Test

2.9 Does Turing's "Intelligence" have to be Human-Like?

2.10 Reconsidering Standard Objections to the Turing Test

3: Spontaneous Communicative Conventions through Virtual Bargaining

3.1 The Spontaneous Creation of Conventions

3.2 Communication through Virtual Bargaining

3.3 The Richness and Flexibility of Signal-Meaning Mappings

3.4 The Role of Cooperation in Communication

3.5 The Nature of the Communicative Act

3.6 Conclusions and Future Directions

4: Modelling Virtual Bargaining using Logical Representation Change

4.1 Introduction-Virtual Bargaining

4.2 What's in the Box?

4.3 Datalog Theories

4.3.1 Clausal form

4.3.2 Datalog properties

4.3.3 Application 1: Game rules as a logic theory

4.3.4 Application 2: Signalling convention as a logic theory

4.4 SL Resolution

4.4.1 SL refutation

4.4.2 Executing the strategy

4.5 Repairing Datalog Theories

4.5.1 Fault diagnosis and repair

4.5.2 Example: The black swan

4.6 Adapting the Signalling Convention

4.6.1 'Avoid' condition

4.6.2 Extended vocabulary

4.6.3 Private knowledge

4.7 Conclusion

Part 2: Human-like Social Cooperation

5: Mining Property-driven Graphical Explanations for Data-centric AI from Argumentation Frameworks

5.1 Introduction

5.2 Preliminaries

5.2.1 Background: argumentation frameworks

5.2.2 Application domain

5.3 Explanations

5.4 Reasoning and Explaining with BFs Mined from Text

5.4.1 Mining BFs from text

5.4.2 Reasoning

5.4.3 Explaining

5.5 Reasoning and Explaining with AFs Mined from Labelled Examples

5.5.1 Mining AFs from examples

5.5.2 Reasoning

5.5.3 Explaining

5.6 Reasoning and Explaining with QBFs Mined from Recommender Systems

5.6.1 Mining QBFs from recommender systems

5.6.2 Explaining

5.7 Conclusions

6: Explanation in AI systems

6.1 Machine-generated Explanation

6.1.1 Bayesian belief networks: a brief introduction

Notes:

6.1.2 Bayesian belief networks: explaining evidence.

Access to content denied by publisher. Description cannot be verified. Title from digital cover (viewed on October 14, 2021).

Other Format:

Print version: Muggleton, Stephen Human-Like Machine Intelligence

ISBN:

9780192607461

0192607464

9780191895333

0191895334

OCLC:

1259588332

Access Restriction:

Restricted for use by site license.