Human-like machine intelligence

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 5058 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

References

3: Spontaneous Communicative Conventions through Virtual Bargaining

3.1 The Spontaneous Creation of Conventions

3.2 Communication through Virtual Bargaining 5058 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

Acknowledgements

References

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 5058 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

Acknowledgements

References

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 5058 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

Acknowledgements

References

6: Explanation in AI systems

6.1 Machine-generated Explanation

6.1.1 Bayesian belief networks: a brief introduction