1. Computing & Information Sciences Kansas State University Friday, 03 Oct 2008CIS 530 / 730: Artificial Intelligence Lecture 16 of 42 Friday, 03 October 2008 William H. Hsu Department of Computing and Information Sciences, KSU KSOL course page: http://snipurl.com/v9v3http://snipurl.com/v9v3 Course web site: http://www.kddresearch.org/Courses/Fall-2008/CIS730http://www.kddresearch.org/Courses/Fall-2008/CIS730 Instructor home page: http://www.cis.ksu.edu/~bhsuhttp://www.cis.ksu.edu/~bhsu Reading for Next Class: Review Chapter 9, Russell & Norvig 2 nd edition Situation Calculus Discussion: Machine Problem 4

2. Computing & Information Sciences Kansas State University Friday, 03 Oct 2008CIS 530 / 730: Artificial Intelligence Lecture Outline Today’s Reading: Sections 10.1 – 10.2, R&N 2e Friday’s Reading: Section 10.3, Russell and Norvig Previously: Logical Representations and Theorem Proving  Propositional, predicate, and first-order logical languages  Proof procedures: forward and backward chaining, resolution refutation Today: Knowledge Rep, Ontologies, Situational Calculus Friday  Temporal logic  Semantic networks  Description Logics Midterm Exam: 16 Oct 2006  Remote students: have exam agreement faxed to DCE  Exam will be faxed to proctors Wednesday or Friday

3. Computing & Information Sciences Kansas State University Friday, 03 Oct 2008CIS 530 / 730: Artificial Intelligence Midterm Review – IAs, Search: Unclear Points? Artificial Intelligence (AI)  Operational definition: study / development of systems capable of “thought processes” (reasoning, learning, problem solving)  Constructive definition: expressed in artifacts (design and implementation) Intelligent Agent Framework  Reactivity vs. state  From goals to preferences (utilities) Methodologies and Applications  Search: game-playing systems, problem solvers  Planning, design, scheduling systems  Control and optimization systems  Machine learning: hypothesis space search (for pattern recognition, data mining) Search  Problem formulation: state space (initial / operator / goal test / cost), graph  State space search approaches  Blind (uninformed) – DFS, BFS, B&B  Heuristic (informed) – Greedy, Beam, A/A*; Hill-Climbing, SA

4. Computing & Information Sciences Kansas State University Friday, 03 Oct 2008CIS 530 / 730: Artificial Intelligence Adapted from slides by S. Russell, UC Berkeley Search versus Planning

5. Computing & Information Sciences Kansas State University Friday, 03 Oct 2008CIS 530 / 730: Artificial Intelligence Adapted from slides by S. Russell, UC Berkeley Planning in Situation Calculus

6. Computing & Information Sciences Kansas State University Friday, 03 Oct 2008CIS 530 / 730: Artificial Intelligence Adapted from slides by S. Russell, UC Berkeley STRIPS Operators

7. Computing & Information Sciences Kansas State University Friday, 03 Oct 2008CIS 530 / 730: Artificial Intelligence Adapted from slides by S. Russell, UC Berkeley State Space versus Plan Space

8. Computing & Information Sciences Kansas State University Friday, 03 Oct 2008CIS 530 / 730: Artificial Intelligence Describing Actions [1]: Frame, Qualification, and Ramification Problems Adapted from slides by S. Russell, UC Berkeley

9. Computing & Information Sciences Kansas State University Friday, 03 Oct 2008CIS 530 / 730: Artificial Intelligence Adapted from slides by S. Russell, UC Berkeley Describing Actions [2]: Successor State Axioms

10. Computing & Information Sciences Kansas State University Friday, 03 Oct 2008CIS 530 / 730: Artificial Intelligence Making Plans Adapted from slides by S. Russell, UC Berkeley

11. Computing & Information Sciences Kansas State University Friday, 03 Oct 2008CIS 530 / 730: Artificial Intelligence Making Plans: A Better Way Adapted from slides by S. Russell, UC Berkeley

12. Computing & Information Sciences Kansas State University Friday, 03 Oct 2008CIS 530 / 730: Artificial Intelligence First-Order Logic: Summary Adapted from slides by S. Russell, UC Berkeley

13. Computing & Information Sciences Kansas State University Friday, 03 Oct 2008CIS 530 / 730: Artificial Intelligence Prolog – Tricks of The Trade [1]: Dealing with Equality Problem  How to find appropriate inference rules for sentences with =?  Unification OK without it, but…  A = B doesn’t force P(A) and P(B) to unify Solutions  Demodulation  Generate substitution from equality term  Additional sequent rule: p. 284 R&N  Paramodulation  More powerful  Generate substitution from WFF containing equality constraint  e.g., (x = y)  P(x)  Sequent rule sketch: p. 284 R&N

14. Computing & Information Sciences Kansas State University Friday, 03 Oct 2008CIS 530 / 730: Artificial Intelligence Prolog – Tricks of The Trade [2]: Resolution Strategies Unit Preference  Idea: Prefer inferences that produce shorter sentences (compare: Occam’s Razor)  How? Prefer unit clause (single-literal) resolvents  Reason: trying to produce a short sentence (   True  False) Set of Support  Idea: try to eliminate some potential resolutions (prevention as opposed to cure)  How? Maintain set SoS of resolution results and always take one resolvent from it  Caveat: need right choice for SoS to ensure completeness Input Resolution and Linear Resolution  Idea: “diagonal” proof (proof “list” instead of proof tree)  How? Every resolution combines some input sentence with some other sentence  Input sentence: in original KB or query  Generalize to linear resolution: include any ancestor in proof tree to be used Subsumption  Idea: eliminate sentences that sentences that are more specific than others  E.g., P(x) subsumes P(A)

15. Computing & Information Sciences Kansas State University Friday, 03 Oct 2008CIS 530 / 730: Artificial Intelligence Prolog – Tricks of The Trade [3]: Indexing Strategies Store and Fetch  Idea: store knowledge base in list of conjuncts  STORE: constant, i.e., O(1) worst-case running time  FETCH: linear, i.e., O(n) time Table Based  Idea: store KB in hash table (key: ground literals)  STORE: O(1)  FETCH: O(1) expected case  Problems  Complex WFFs (other than negated atoms)  Variables  Solution: implicative normal form matching (Figure 10.1, p. 301 R&N) Tree-Based  What if there are many clauses for a predicate? (e.g., Brother (012-34-5678, x))  Type of combined indexing: joint primary key – predicate and argument symbols  May need background knowledge for semantic query optimization (SQO)

16. Computing & Information Sciences Kansas State University Friday, 03 Oct 2008CIS 530 / 730: Artificial Intelligence Adapted from slides by S. Russell, UC Berkeley Prolog - Tricks of The Trade [4]: Compilation Intermediate Languages  Abstract machines  Warren Abstract Machine (WAM)  Java Virtual Machine (JVM)  Imperative intermediate representations (IRs)  C/C++  LISP / Scheme / SML – functional languages with imperative features Use in Genetic Programming (GLP): Later Beyond Scope of CIS 730: Compiling with Continuations (Appel)

17. Computing & Information Sciences Kansas State University Friday, 03 Oct 2008CIS 530 / 730: Artificial Intelligence Summary Points Previously: FOL, Forward/Backward Chaining, Resolution, Unification Today: Prolog and Decidability  Review: resolution inference rule  Single-resolvent form  General form  Application to logic programming  Review: decidability properties  FOL-SAT  FOL-NOT-SAT (language of unsatisfiable sentences; complement of FOL-SAT)  FOL-VALID  FOL-NOT-VALID  Unification Next Week  Intro to knowledge representation  Ontologies

18. Computing & Information Sciences Kansas State University Friday, 03 Oct 2008CIS 530 / 730: Artificial Intelligence Terminology Properties of Knowledge Bases (KBs)  Satisfiability and validity  Entailment and provability Properties of Proof Systems  Soundness and completeness  Decidability, semi-decidability, undecidability Resolution Refutation Satisfiability, Validity Prolog: Tricks of The Trade  Demodulation, paramodulation  Unit resolution, set of support, input / linear resolution, subsumption  Indexing (table-based, tree-based)

19. Computing & Information Sciences Kansas State University Friday, 03 Oct 2008CIS 530 / 730: Artificial Intelligence Summary Points Previously: Logical Representations and Theorem Proving  Propositional, predicate, and first-order logical languages  Proof procedures: forward and backward chaining, resolution refutation Today: Introduction to Classical Planning  Search vs. planning  STRIPS axioms  Operator representation  Components: preconditions, postconditions (ADD, DELETE lists) Next Monday: More Classical Planning  Partial-order planning (NOAH, etc.)  Limitations

20. Computing & Information Sciences Kansas State University Friday, 03 Oct 2008CIS 530 / 730: Artificial Intelligence Adapted from slides by S. Russell, UC Berkeley Terminology Classical Planning  Planning versus search  Problematic approaches to planning  Forward chaining  Situation calculus  Representation  Initial state  Goal state / test  Operators Efficient Representations  STRIPS axioms  Components: preconditions, postconditions (ADD, DELETE lists)  Clobbering / threatening  Reactive plans and policies  Markov decision processes