Computing & Information Sciences Kansas State University Monday, 01 Dec 2008CIS 530 / 730: Artificial Intelligence Lecture 37 of 42 Monday, 01 December 2008 William H. Hsu Department of Computing and Information Sciences, KSU KSOL course page: Course web site: Instructor home page: Reading for Next Class: Sections 22.1, , Russell & Norvig 2 nd edition Vision, Part 1 of 2 Discussion: GEC Concluded

Computing & Information Sciences Kansas State University Monday, 01 Dec 2008CIS 530 / 730: Artificial Intelligence Lecture Outline This Week: Chapter 26, Russell and Norvig 2e Today: Chapter 23, R&N 2e Wednesday (Last Lecture!): Chapter 24, R&N 2e References  Robot Vision, B. K. P. Horn  Courses:  UCB CS 280: The Vision Problem  Early vs. late vision  Marr’s 2 ½ - D sketch  Waltz diagrams Shape from Shading  Ikeuchi-Horn method  Subproblems: edge detection, segmentation Optical Flow

Computing & Information Sciences Kansas State University Monday, 01 Dec 2008CIS 530 / 730: Artificial Intelligence GP Flow Graph Adapted from The Genetic Programming Notebook © 2002 Jaime J. Fernandez

Computing & Information Sciences Kansas State University Monday, 01 Dec 2008CIS 530 / 730: Artificial Intelligence Structural Crossover Adapted from The Genetic Programming Notebook © 2002 Jaime J. Fernandez

Computing & Information Sciences Kansas State University Monday, 01 Dec 2008CIS 530 / 730: Artificial Intelligence Structural Mutation Adapted from The Genetic Programming Notebook © 2002 Jaime J. Fernandez

Computing & Information Sciences Kansas State University Monday, 01 Dec 2008CIS 530 / 730: Artificial Intelligence Genetic Programming: The Next Generation (Synopsis and Discussion) Automatically-Defined Functions (ADFs)  aka macros, anonymous inline functions, subroutines  Basic method of software reuse Questions for Discussion  What are advantages, disadvantages of learning anonymous functions?  How are GP ADFs similar to and different from human-produced functions? Exploiting Advantages  Reuse  Innovation Mitigating Disadvantages  Potential lack of meaning – semantic clarity issue (and topic of debate)  Redundancy  Accelerated bloat – scalability issue

Computing & Information Sciences Kansas State University Monday, 01 Dec 2008CIS 530 / 730: Artificial Intelligence Code Bloat [1]: Problem Definition Definition  Increase in program size not commensurate with increase in functionality (possibly as function of problem size)  Compare: structural criteria for overfitting, overtraining Scalability Issue  Large GPs will have this problem  Discussion: When do we expect large GPs?  Machine learning: large, complex data sets  Optimization, control, decision making / DSS: complex problem What Does It Look Like? What Can We Do About It?  ADFs  Advanced reuse techniques from software engineering: e.g., design patterns  Functional, object-oriented design; theory of types  Controlling size: parsimony (MDL-like), optimization (cf. compiler)

Computing & Information Sciences Kansas State University Monday, 01 Dec 2008CIS 530 / 730: Artificial Intelligence Code Bloat [2]: Mitigants Automatically Defined Functions Types  Ensure  Compatibility of functions created  Soundness of functions themselves  Define: abstract data types (ADTs) – object-oriented programming  Behavioral subtyping – still “future work” in GP  Generics (cf. C++ templates)  Polymorphism Advanced Reuse Techniques  Design patterns  Workflow models  Inheritance, reusable classes

Computing & Information Sciences Kansas State University Monday, 01 Dec 2008CIS 530 / 730: Artificial Intelligence Code Bloat [3]: More Mitigants Parsimony (cf. Minimum Description Length)  Penalize code bloat  Inverse fitness = loss + cost of code (evaluation)  May include terminals Target Language Optimization  Rewriting of constants  Memoization  Loop unrolling  Loop-invariant code motion

Computing & Information Sciences Kansas State University Monday, 01 Dec 2008CIS 530 / 730: Artificial Intelligence Genetic Programming 3 (Synopsis and Discussion [1]) Automatic Program Synthesis by Computational Intelligence: Criteria  1. Specification: starts with what needs to be done  2. Procedural representation: tells us how to do it  3. Algorithm implementation: produces a computer program  4. Automatic determination of program size  5. Code reuse  6. Parametric reuse  7. Internal storage  8. Iteration (while / for), recursion  9. Self-organization of hierarchies  10. Automatic determination of architecture  11. Wide range of programming constructs  12. Well-defined  13. Problem independent

Computing & Information Sciences Kansas State University Monday, 01 Dec 2008CIS 530 / 730: Artificial Intelligence Genetic Programming 3 (Synopsis and Discussion [2]) 16 Criteria for Automatic Program Synthesis …  14. Generalization: wide applicability  15. Scalability  16. Human-competitiveness Current Bugbears: Generalization, Scalability Discussion: Human Competitiveness?

Computing & Information Sciences Kansas State University Monday, 01 Dec 2008CIS 530 / 730: Artificial Intelligence Summary of Videos GP1: Basics of SGP GP2: ADFs and Problem of Code Bloat GP3: Advanced Topics  A. M. Turing’s 16 criteria  How GP does and does not (yet) meet them

Computing & Information Sciences Kansas State University Monday, 01 Dec 2008CIS 530 / 730: Artificial Intelligence More Food for Thought and Research Resources Discussion: Future of GP Current Applications Conferences  GECCO: ICGA + ICEC + GP  GEC  EuroGP Journals  Evolutionary Computation Journal (ECJ)  Genetic Programming and Evolvable Machines (GPEM)

Computing & Information Sciences Kansas State University Monday, 01 Dec 2008CIS 530 / 730: Artificial Intelligence More Food for Thought and Research Resources Discussion: Future of GP Current Applications Conferences  GECCO: ICGA + ICEC + GP  GEC  EuroGP Journals  Evolutionary Computation Journal (ECJ)  Genetic Programming and Evolvable Machines (GPEM)

Computing & Information Sciences Kansas State University Monday, 01 Dec 2008CIS 530 / 730: Artificial Intelligence Adapted from slides © 1999 J. Malik, UC Berkeley (CS 280 Computer Vision) Line Drawing Interpretation

Computing & Information Sciences Kansas State University Monday, 01 Dec 2008CIS 530 / 730: Artificial Intelligence Adapted from slides © 1999 J. Malik, UC Berkeley (CS 280 Computer Vision) Line Labeling [1]: Solid Polyhedra and Other Shapes Waltz, others

Computing & Information Sciences Kansas State University Monday, 01 Dec 2008CIS 530 / 730: Artificial Intelligence Adapted from slides © 1999 J. Malik, UC Berkeley (CS 280 Computer Vision) Line Labeling [2]: Junctions Junctions occur at tangent discontinuities False T-junctions

Computing & Information Sciences Kansas State University Monday, 01 Dec 2008CIS 530 / 730: Artificial Intelligence Adapted from slides © 1999 T. Leung, UC Berkeley (CS 280 Computer Vision) Orientation and Texture Discrimination (Textons) [1]

Computing & Information Sciences Kansas State University Monday, 01 Dec 2008CIS 530 / 730: Artificial Intelligence Adapted from slides © 1999 J. Malik, UC Berkeley (CS 280 Computer Vision) Orientation and Texture Discrimination (Textons) [2]

Computing & Information Sciences Kansas State University Monday, 01 Dec 2008CIS 530 / 730: Artificial Intelligence Adapted from slides © 1999 J. Malik, UC Berkeley (CS 280 Computer Vision) Segmentation (Grouping) [1]: Definition

Computing & Information Sciences Kansas State University Monday, 01 Dec 2008CIS 530 / 730: Artificial Intelligence Adapted from slides © 1999 J. Malik, UC Berkeley (CS 280 Computer Vision) Segmentation (Grouping) [2]: Physical Factors

Computing & Information Sciences Kansas State University Monday, 01 Dec 2008CIS 530 / 730: Artificial Intelligence Adapted from slides © 1999 J. Malik, UC Berkeley (CS 280 Computer Vision) Edge Detection [1]: Convolutional Filters and Gaussian Smoothing

Computing & Information Sciences Kansas State University Monday, 01 Dec 2008CIS 530 / 730: Artificial Intelligence Adapted from slides © 1999 J. Malik, UC Berkeley (CS 280 Computer Vision) Edge Detection [2]: Difference of Gaussian

Computing & Information Sciences Kansas State University Monday, 01 Dec 2008CIS 530 / 730: Artificial Intelligence Adapted from slides © 1999 J. Malik, UC Berkeley (CS 280 Computer Vision) Binocular Stereo [1]: Stereo Correspondence – Properties

Computing & Information Sciences Kansas State University Monday, 01 Dec 2008CIS 530 / 730: Artificial Intelligence Adapted from slides © 1999 J. Malik, UC Berkeley (CS 280 Computer Vision) Binocular Stereo [2]: Stereo Correspondence – Open Problems

Computing & Information Sciences Kansas State University Monday, 01 Dec 2008CIS 530 / 730: Artificial Intelligence Adapted from slides © 1999 J. Malik, UC Berkeley (CS 280 Computer Vision) Optical Flow

Computing & Information Sciences Kansas State University Monday, 01 Dec 2008CIS 530 / 730: Artificial Intelligence Terminology Vision Problem  Early vs. late vision  Marr’s 2 ½ - D sketch  Waltz diagrams Shape from Shading  Ikeuchi-Horn method  Subproblems: edge detection, segmentation Optical Flow

Computing & Information Sciences Kansas State University Monday, 01 Dec 2008CIS 530 / 730: Artificial Intelligence Summary Points References  Robot Vision, B. K. P. Horn  The Vision Problem  Early vs. late vision  Marr’s 2 ½ - D sketch  Waltz diagrams Shape from Shading  Ikeuchi-Horn method  Subproblems: edge detection, segmentation Optical Flow Next Week  Natural Language Processing (NLP) survey  Final review