1. Building Control Algorithms for State Space Search. Luger: Artificial Intelligence, 5 th edition. © Pearson Education Limited, 2005

2. “If we carefully factor out the influences of the task environment from the influences of the underlying hardware components and organization, we reveal the true simplicity of the adaptive system. For, as we have seen, we need postulate only a very simple information processing system in order to account for human problem solving in such tasks as chess, logic, and cryptarithmetic. The apparently complex behavior of the information processing system in a given environment is produced by the interaction of the demands of the environment with a few basic parameters of the system, particularly characteristics of its memories.” Newell and Simon, Human Problem Solving (1972)

3. Depth-First Search via Recursion Luger: Artificial Intelligence, 5 th edition. © Pearson Education Limited, 2005

5. A production system. Control loops until working memory pattern no longer matches the conditions of any productions. Luger: Artificial Intelligence, 5 th edition. © Pearson Education Limited, 2005

6. Trace of a simple production system. Luger: Artificial Intelligence, 5 th edition. © Pearson Education Limited, 2005

7. The 8-puzzle as a production system. Luger: Artificial Intelligence, 5 th edition. © Pearson Education Limited, 2005

8. The 8-puzzle searched by a production system with loop detection and depth-bound, from Nilsson (1971). Luger: Artificial Intelligence, 5 th edition. © Pearson Education Limited, 2005

9. Legal moves of a chess knight. Luger: Artificial Intelligence, 5 th edition. © Pearson Education Limited, 2005

10. A 3 x 3 chessboard with move rules for the simplified knight tour problem. Luger: Artificial Intelligence, 5 th edition. © Pearson Education Limited, 2005

11. Production rules for the 3 x 3 knight problem. (  X)path(X,X)terminating condition (  X,Y)[path(X,Y)  (  Z)[move(X,Z)  path(Z,Y)]] Luger: Artificial Intelligence, 5 th edition. © Pearson Education Limited, 2005 Production system...

12. A production system solution to the 3 x 3 knight’s tour problem. Luger: Artificial Intelligence, 5 th edition. © Pearson Education Limited, 2005 Control algorithm: (  X)path(X,X) (terminating condition) (  X,Y)[path(X,Y)  (  Z)[move(X,Z)  path(Z,Y)]]

13. (  X)path(X,X) (  X,Y)[path(X,Y)  (  Z)[move(X,Z)  path(Z,Y)]] Or, to prevent looping: (  X)path(X,X) (  X,Y)[path(X,Y)  (  Z) [move(X,Z)   ((been(Z)  assert(been(Z))  path(Z,Y)]] The recursive path algorithm as a production system. Luger: Artificial Intelligence, 5 th edition. © Pearson Education Limited, 2005

14. Data-driven search in a production system. Luger: Artificial Intelligence, 5 th edition. © Pearson Education Limited, 2005 (match conditions…)

15. Goal-driven search in a production system. Luger: Artificial Intelligence, 5 th edition. © Pearson Education Limited, 2005 (match actions…)

16. Bidirectional search missing in both directions, resulting in excessive search. Luger: Artificial Intelligence, 5 th edition. © Pearson Education Limited, 2005

17. Bidirectional search meeting in the middle, eliminating much of the space examined by unidirectional search. Luger: Artificial Intelligence, 5 th edition. © Pearson Education Limited, 2005

18. Control of Search Through Rule Structure Consider the following: a  b  c  a  b  c Now let a=the engine turns over b=the lights come on c=check the battery and consider the difference (e.g., the first suggests a particular sequence of action). Consider alternative forms of the same expression… Luger: Artificial Intelligence, 5 th edition. © Pearson Education Limited, 2005

19. Control of Search Through Conflict Resolution Refraction –After a rule has fired, do not let it fire again until the working memory elements matching its conditions have been modified. Helps avoid looping. Recency –Select rules with conditions matching patterns most recently added to working memory. Focuses search on a single line of reasoning. Specificity –Use a more specific problem-solving strategy than a more general one. A rule is more specific if it has more conditions. Luger: Artificial Intelligence, 5 th edition. © Pearson Education Limited, 2005

20. Major advantages of production systems for artificial intelligence Separation of Knowledge and Control A Natural Mapping onto State Space Search Modularity of Production Rules Pattern-Directed Control Opportunities for Heuristic Control of Search Tracing and Explanation Language Independence A Plausible Model of Human Problem-Solving Luger: Artificial Intelligence, 5 th edition. © Pearson Education Limited, 2005

21. Problems with Traditional Production Systems Combinatorial explosion Deductive (only) reasoning Domain limited/context constrained Inflexible rule processing Inability to use full knowledge base Grounding in perceptual reality Brittleness No meta-knowledge Knowledge acquisition Validation

22. Blackboard architecture Luger: Artificial Intelligence, 5 th edition. © Pearson Education Limited, 2005

24. Note: a “full-blown” version of this algorithm must: 1.Deal with retrieval order of alternative matches. 2.Handle binary connectives (including making the proper substitutions during unification). 3.Solve goals involving negation. 4.Return the bindings involved in the solution. Recursive algorithm to search a space of logical inferences for some (predicate logic) fact. Luger: Artificial Intelligence, 5 th edition. © Pearson Education Limited, 2005

25. Facts and rules: l(a) l(j) m(j) n(s) q(t) q(s) q(j) s(c) v(b) n(X) → r(X) k(X) → u(X) q(X)  v(X) → u(X) s(Y)  t(X) → u(X) l(Z)  m(Z) → p(Z) p(X)  q(X)  r(Y) → t(Y) Prove u(s) Luger: Artificial Intelligence, 5 th edition. © Pearson Education Limited, 2005

26. Facts and rules: l(a) l(j) m(j) n(s) q(t) q(s) q(j) s(c) v(b) n(X) → r(X) k(X) → u(X) q(X)  v(X) → u(X) s(Y)  t(X) → u(X) l(Z)  m(Z) → p(Z) p(X)  q(X)  r(Y) → t(Y)