Human-like machine intelligence / edited by Stephen Muggleton, Imperial College London, Nick Chater, University of Warwick.
| Other author | Muggleton, Stephen. |
| Other author | Chater, Nick. |
| Other author | Oxford University Press. |
| Format | Electronic |
| Edition | First edition. |
| Publication Info | Oxford : Oxford University Press, 2021. |
| Description | xviii, 516 pages : illustrations (some color) ; 26 cm |
| Supplemental Content | Full text available from Oxford Scholarship Online |
| Subjects |
| 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 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 |
| Bibliography note | Includes bibliographical references and index. |
| Access restriction | Available only to authorized users. |
| Technical details | Mode of access: World Wide Web |
| Genre/form | Electronic books. |
| LCCN | 2021932529 |
| ISBN | 9780198862536 (hardback) |
| ISBN | 0198862539 (hardback) |