CS 415 – A.I.

1. CS 415 – A.I. Slide Set 2

2. Outline • Last Time • AI, an attempted definition • Historical foundations • Today • Overview of application areas • An introduction to representation and search

3. First Material Review • Accurate Relative Localization • See website for link to pdf • Read by class on Tuesday • 1 page summary report • Full run-down of what the paper is about • Any thoughts you had about the paper

4. Turing Test • Attempts to give an objective notion of intelligence • Humanity is the best and only known standard • Avoids debate about “true” nature of intelligence • Abstracts out unanswerable questions • What is consciousness? Is the computer conscious? • Eliminates “bias” against machines • Interrogator can’t see any evidence except text • Abstracts out specific intelligent processes • No spinning disks, no artificial sounding voices, etc.

5. Lady Lovelace’s Objection • Ada Lovelace • After whom the programming language Ada is named • Computers can only do as they are instructed, thus they are incapable of original thought • What is originality, but a rehashing of the old • Certainly, computers can do this • Genetic Algorithms, etc. • What is the fundamental difference between humans and computers in this regard • What is the seat of human originality • Humans only know what they’ve been taught – or – “ghost in the machine” • Programs do not have to be strictly sequential • Instead, AI programs can be built as rules that can be applied when it is deemed by the computer to be necessary

6. Argument from Informality of Behavior • “No set of instructions can prepare a computer for behaving rationally in any possible previously unknown situation.” • Wrong • Again, computers do not have to act sequentially to perform tasks • Nor, in fact, do computers have to be strictly discrete. • Also, how flexible are humans to new environments and so how flexible do computers need to be?

7. Other Theories and Approaches • So far, only talked about rationalist theories • Define intelligence according to mathematics and logical consistency • But, post-modernism is diametrically opposed to this viewpoint • Connectionist Theory • Intelligence doesn’t have to be rational • Humans don’t always act rationally • Instead, lets model the biology of the brain • Neural networks • Genetic algorithms • Knowledge and solutions morph unpredictably and are tested against previous solutions

8. Emergent Knowledge • Bakeries in New York • Provide almost exactly the correct amount of bread to New York • no rationalizing on good information • Stock Market • Prices are set, but investors have little knowledge • Universities • Answers to hard problems are solved because people work and interact, even seperately • Web 2.0 • Wikipedia • Data Mining

9. Agents • How does the knowledge emerge? • Agents that interact with other agents in simple ways • Each agent is autonomous, and each has a task that isn’t based upon the system as a whole • Each agent is sensitive to its own environment • Agents form a “society” • The society is structured in some way • Each agent, working in its own sphere in the society allows the society to produce emergent knowledge

10. Overview of AI Application Areas • 2 Major Parts to AI • Knowledge Representation • Search • Game Playing • Not just for fun • Well-structured rules, “easily” represented states • Show need for heuristics • Automated Theorem Proving • Given any set of known facts and a possibly true statement, is the statement in question implied? • PROLOG (Chapter 15)

11. Expert Systems • A system that focuses on “domain-specific knowledge” • 2 parts • Theoretical understanding of the problem • Set of heuristics that can be applied in certain situations • Expert systems are “loaded” with these two parts • Problems • Difficult to capture “deep” knowledge in the field • Sometimes the expert just knows what he/she knows • Not necessarily robust of flexible • Can’t cross-apply knowledge from different fields like humans can • Can’t present deep explanations • Difficulty in verification • Aren’t flexible in the sense of being able to learn anything new • Specialized systems only

12. Understanding Natural Language • Make my computer do what I mean, not strictly what I say • Programmer’s dream • But, there isn’t a consistent way to parse language • Meaning is often, if not always, a matter of context and domain

13. Modeling Human Performance • Make computers act more and more like humans • Planning and Robotics • Start with a robot that can perform certain atomic tasks • Now, build it so that it can plan and act in its environment without previous knowledge of the environment • Hierarchical problem decomposition • How does a robot enter a room and find an object • Possible Step? • Each step can be rationally decomposed

14. Languages and Environments for AI • Object-Oriented Programming • C++ and Java • Represent Knowledge Representation from the start • LISP and PROLOG • Support modular development and better ability for handling complexity of heuristic search • Machine Learning • Computers asked a question today, will, when asked tomorrow, take the same steps to determine the same answer to the same question • Humans don’t have to do this • Need to have computers that remember results in meaningful ways • Text-completion • Here are some RNA strands that are known to exist in nature, can this one exist in nature?

15. Neural Nets and Genetic Algorithms • Neural Nets • Based on biological nueron networks • See pg 29 • Genetic Algorithms • Solutions “evolve” • AI and Philosophy • Most of the theoretical questions we asked earlier are also asked by philosophers

16. What it all means • We cannot solve the unanswered questions that have been asked for centuries • Not in this class anyway • However, we can look at the foundational approaches that have gone in to the basics of AI and Robotics • Predicate Calculus • Search Methods • Knowledge Representation • Machine Learning • Reasoning • PROLOG • LISP

17. Representation and Search • Representational System – function is to capture the essential features of a problem domain and make the information accessible • Abstraction – being able to efficiently store the features of the problem domain • Note: the features will undoubtedly change • Balance trade-offs between efficiency and expressiveness

18. Representation in Mobile Robotics • Kinematics – basic study of how mechanisms move • Basic goal: given all the angles and movement, what is you point in space at this time • 2 Frames of Reference • Global Frame of Reference • Robot gets through layers of representations (maps, etc)‏ • Local Frame of Reference • Don't know what the world looks like • Remember how far I've traveled

19. Given global frame of reference • If the robot moves how do we keep up with where we are in space • Kinematic Equations • A system of equations that determines our x,y position and our rotation (angle Θ) after k control steps • See second page of ARL paper • Synchro Drive Robots • Also exist for Differential Drive Robots • Store as a matrix system • Perform matrix operations to transform and solve

20. Regardless, everyone should work in the same frame of reference • Homogenous Transform • Do matrix operations to transform from one frame of reference to another

21. How far has the robot moved? • Apply power, robot moves, right? • Power does not relate well to speed • So, other options: • Check the particular motor velocity (left/right)‏ • Some visual cue for how fast you're going • Encode inside motor • How many 'ticks' has the motor made as it rotated • Use PID, proportional integral derivative • Integral and derivative → smooths error/gives result

22. Mapping • How do we abstract a map? • Efficiency vs Expressiveness • What are the tradeoffs • Dealing with Errors • Examples: • Synchro Drive • Differential Drive

23. Accurate Relative Localization Using Odometry • Drawing Maps • Depends on relative localization • Can't escape the use of odometry • Have error built in • Overcoming Error • Odometry error modeling • Error Parameters estimation • Covariance matrix estimation • 1 and 2 – systematic errors • 3 – non-systematic errors

24. Systematic Errors • Define these for the robot based on the appropriate error model for the drive type • Differential Drive → given in the literature • Borenstein paper • Synchro Drive → given in this paper • Major source: wheel misalignment • Major source of distortion for theta (angular velocity): drag AND rotate the robot • Provable by geometric analysis of kinematic equation

25. Non-systematic Errors • PC (POSTECH CMU)-method • 1st get the error model • 2nd use PC-method to generate error parameters and covariance matrix • Based on sensor-based navigation through the Generalized Voronoi Graph (GVG)‏ • Voronoi extensively covered in literature • Creates a well-understood path based on obstacles • Robot drives it forward (FOP) and backward (BOP)‏ • 2 diff odom paths, same real-world path

26. Give an initial CFOP and CBOP based on error model and initial error parameters guess • Then, find the error parameters that minimize error between CFOP and CBOP • Steepest descent method • Now, build an error covariance matrix based on 3 assumptions and worst-case analysis

27. A little error, uncorrected, tends to flourish • See Fig 8 • Note: one possible approach, reset the odometry before the error gets too bad • See Fig 9 • See Fig 12 • See Fig 13

28. Representation/Search - Considerations • Real-time Systems • Is it schedulable • Has a lot to do with efficiency/expressiveness • How are we storing things (fast to slow)‏ • I/O • Memory • Registers • Cache • What Language are we using (fast to slow)‏ • Assembly • C • C++ • Java • Python

29. Other Forms of Representation • Example: A robot might be stacking elements from a table on top of one another • Might give the following predicates (state facts about our domain): clear(c)‏ clear(a)‏ ontable(a)‏ ontable(b)‏ on(c,b)‏ cube(b)‏ cube(a)‏ pyramid(c)‏ • Might also define a set of rules which relate to these predicates For all X if there does not exist any Y where on(Y,X) than this implies clear(X)‏

30. Using Predicate Calculus • Predicates can also be more advanced hassize(bluebird,small)‏ hascovering(bird,feathers)‏ hascolor(bluebird,blue)‏ hasproperty(bird,flies)‏ isa(bluebird,bird)‏ isa(bird,vertebrae)‏ • Predicates are not functions in the sense of higher-level languages, nor should you think of them in terms of programming • There is no set of predicate functions • Any predicate can be defined • They are strictly useful for representing knowledge in conjunction with rules

31. Search • What are the possible moves? • The computer knows because of the knowledge representation • All moves are either stored or can be inferred from the stored knowledge and set of rules. • What is the best move? • This is the domain of search • Example: Tic Tac Toe

34. Limitations of State-Space Search • Not sufficient to automate intelligent behavior • How big is the state-space for chess? • 10120 different board configurations • Larger than # molecules in the universe • Larger than the number of seconds since the “big bang” • How big is the state-space for human language? • Untold possibilities • State-Space Representation and Search is an important tool only

35. Exhaustive Search vs. Heuristic Search • Exhaustive Search • Brute force attempting all possible combinations till an optimized solution is found • Heuristic Search • Humans don’t use exhaustive search • Instead, we use rules of thumb based on what seems most “promising” • Heuristic – a strategy for selectively searching a state space ---- Examples?