Artificial Intelligence
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This document summarizes chapter 14 from George F Luger's book "Artificial Intelligence 6th edition". The chapter introduces weak methods for automated reasoning and theorem proving, including the General Problem Solver which uses means-ends analysis and difference tables. It also describes resolution theorem proving which involves breaking statements into clauses and resolving them. Prolog logic programming is discussed as well as further issues in automated reasoning such as handling variables and extracting answers. Various examples and figures are provided to illustrate logical proofs and reasoning approaches.
Artificial Intelligence
- 1. George F Luger ARTIFICIAL INTELLIGENCE 6th edition Structures and Strategies for Complex Problem Solving Automated Reasoning Luger: Artificial Intelligence, 6th edition. © Pearson Education Limited, 2009 14.0 Introduction to Weak Methods in Theorem Proving 14.1 The General Problem Solver and Difference Tables 14.2 Resolution Theorem Proving 14.3 PROLOG and Automated Reasoning 14.4 Further Issues in Automated Reasoning 14.5 Epilogue and References 14.6 Exercises 1
- 2. Fig 14.1a Transformation rules for logic problems, from Newell and Simon (1961). Luger: Artificial Intelligence, 6th edition. © Pearson Education Limited, 2009 2
- 3. Fig 14.1b A proof of a theorem in propositional calculus, from Newell and Simon (1961). Luger: Artificial Intelligence, 6th edition. © Pearson Education Limited, 2009 3
- 4. Fig 14.2 Flow chart and difference reduction table for the General Problem Solver, from Newell and Simon (1963b). Luger: Artificial Intelligence, 6th edition. © Pearson Education Limited, 2009 4
- 5. Resolution refutation proofs involve the following steps: Luger: Artificial Intelligence, 6th edition. © Pearson Education Limited, 2009 5
- 6. Fig 14.3 Resolution proof for the “dead dog” problem. Luger: Artificial Intelligence, 6th edition. © Pearson Education Limited, 2009 6
- 7. Fig 14.4 One resolution proof for an example from the propositional calculus. Luger: Artificial Intelligence, 6th edition. © Pearson Education Limited, 2009 7
- 8. Luger: Artificial Intelligence, 6th edition. © Pearson Education Limited, 2009 8
- 9. Luger: Artificial Intelligence, 6th edition. © Pearson Education Limited, 2009 9
- 10. Fig 14.5 One refutation for the “happy student” problem. Luger: Artificial Intelligence, 6th edition. © Pearson Education Limited, 2009 10
- 11. Fig 14.6 Resolution proof for the “exciting life” problem. Luger: Artificial Intelligence, 6th edition. © Pearson Education Limited, 2009 11
- 12. Fig 14.7 another resolution refutation for the example of Fig 14.6. Luger: Artificial Intelligence, 6th edition. © Pearson Education Limited, 2009 12
- 13. Fig 14.8 Complete state space for the “exciting life” problem generated by breadth-first search (to two levels). Luger: Artificial Intelligence, 6th edition. © Pearson Education Limited, 2009 14
- 14. Fig 14.9 Using the unit preference strategy on the “exciting life” problem. Luger: Artificial Intelligence, 6th edition. © Pearson Education Limited, 2009 14
- 15. Fig 14.10 Unification substitutions of Fig 14.6 applied to the original query. Luger: Artificial Intelligence, 6th edition. © Pearson Education Limited, 2009 15
- 16. Fig 14.11 Answer extraction process on the “finding fido” problem. Luger: Artificial Intelligence, 6th edition. © Pearson Education Limited, 2009 16
- 17. Fig 14.12 Skolemization as part of the answer extraction process. Luger: Artificial Intelligence, 6th edition. © Pearson Education Limited, 2009 17
- 18. Fig 14.13 Data-driven reasoning with n and/or graph in the propositional calculus Luger: Artificial Intelligence, 6th edition. © Pearson Education Limited, 2009 18
- 19. Fig 14.14 Goal-driven reasoning with an and/or graph in the propositional calculus. Luger: Artificial Intelligence, 6th edition. © Pearson Education Limited, 2009 19
- 20. Luger: Artificial Intelligence, 6th edition. © Pearson Education Limited, 2009 20