Transcript intro
1946: ENIAC heralds the dawn of Computing 1950: Turing asks the question…. I propose to consider the question: “Can machines think?” --Alan Turing, 1950 1995: RALPH takes a trip from coast to coast CMU’s RALPH program drove a van for all but 52 miles of a trip from D.C. to San Diego 1996: EQP proves that Robbin’s Algebras are all boolean ----- EQP 0.9, June 1996 ----The job began on eyas09.mcs.anl.gov, Wed Oct 2 12:25:37 1996 UNIT CONFLICT from 17666 and 2 at 678232.20 seconds. ---------------- PROOF ---------------2 (wt=7) [] -(n(x + y) = n(x)). 3 (wt=13) [] n(n(n(x) + y) + n(x + y)) = y. 5 (wt=18) [para(3,3)] n(n(n(x + y) + n(x) + y) + y) = n(x + y). 6 (wt=19) [para(3,3)] n(n(n(n(x) + y) + x + y) + y) = n(n(x) + y). ……. 17666 (wt=33) [para(24,16426),demod([17547])] n(n(n(x) + x) …. [An Argonne lab program] has come up with a major mathematical proof that would have been called creative if a human had thought of it. -New York Times, December, 1996 Jan 12, 1997: HAL 9000 becomes operational in fictional Urbana, Illinois …by now, every intelligent person knew that H-A-L is derived from Heuristic ALgorithmic -Dr. Chandra, 2010: Odyssey Two May, 1997: Deep Blue beats the World Chess Champion vs. I could feel human-level intelligence across the room -Gary Kasparov, World Chess Champion (human) May, 1999: Remote Agent takes Deep Space 1 on a galactic ride Goals Scripts Scripted Executive ESL Mission-level actions & resources Generative Planner & Scheduler Generative Mode Identification & Recovery component models Monitors Real-time Execution Adaptive Control Hardware For two days in May, 1999, an AI Program called Remote Agent autonomously ran Deep Space 1 (some 60,000,000 miles from earth) May 2000: SCIFINANCE synthesizes programs for financial modeling Develop pricing models for complex derivative structures Involves the solution of a set of PDEs (partial differential equations) Integration of objectoriented design, symbolic algebra, and plan-based scheduling Sept. 2002: Cindy Smart will be marketed Vision: can read, tell the time Speech recognition: can recognize 700 words and 77 phrases Voice synthesis: speaks with a soft voice What else? Real-time response robustness autonomous intelligent interaction with the environment planning communication with natural language commonsense reasoning creativity learning ??? Administrivia Textbook: Luger’s Artificial Intelligence, 2002, Addison Wesley Grading: – Assignments 40% – Midterm Exam 1 20% – Midterm Exam 2 20% – Final Exam 20% Academic honesty Contents PART I: Artificial Intelligence: Its Roots and Scope – Chapter 1: AI: History and Applications PART II: Artificial Intelligence as Representation and Search – Chapter 2: The Predicate Calculus – Chapter 3: Structures and Strategies for State Space Search – Chapter 4: Heuristic Search – Chapter 5: Control and Implementation of State-Space Search Contents (cont’d) Part III: Representation and Intelligence: The AI Challenge – Chapter 6: Knowledge Representation – Chapter 7: Strong Method Problem Solving – Chapter 8: Reasoning in Uncertain Situations Contents (cont’d) Part IV: Machine Learning – Chapter 9: Machine Learning: Symbolbased – Chapter 10: Machine Learning: Connectionist – Chapter 11: Machine Learning: Social and Emergent Contents (cont’d) Part V: Advanced Topics for AI Problem Solving – Chapter 12: Automated Reasoning – Chapter 13: Understanding Natural Language Contents (cont’d) Part VI: Languages and Programming Techniques for AI – Chapter 14: An Introduction to Prolog – Chapter 15: An Introduction to Lisp Part VII: Epilolgue – Chapter 16: Artificial Intelligence as Empirical Enquiry What is AI? Figure 1.1: The Turing test. Definitions of AI Systems that think like humans Systems that act like humans Systems that think rationally Systems that act rationally Question: What would impress you as an “intelligent system?” Important Research and Application Areas Game playing Automated Reasoning and Theorem Proving Expert Systems Natural Language Understanding and Semantic Modeling Modeling Human Performance Planning and Robotics Languages and Environments for AI Machine Learning Alternative Representations: Neural Nets and Genetic Algorithms AI and Philosophy Important Features of AI The use of computers to do reasoning, pattern recognition, learning, or some other form of inference. A focus on problems that do not respond to algorithmic solutions. This underlies the reliance on heuristic search as an AI problem-solving technique. A concern with problem solving using inexact, missing, or poorly defined information and the use of representational formalisms that enable the programmer to compensate for these problems. Important Features of AI (cont’d) Reasoning about the significant qualitative features of a situation. An attempt to deal with issues of semantic meaning as well as syntactic form. Answers that are neither exact nor optimal, but are in some sense “sufficient.” This is a result of the essential reliance on heuristic problem-solving methods in situations where optimal or exact results are either too expensive or not possible. Important Features of AI (cont’d) The use of large amounts of domainspecific knowledge in solving problems. This is the basis of expert systems. The use of meta-level knowledge to effect more sophisticated control of problem solving strategies. Although this is a very difficult problem, addressed in relatively few current systems, it is emerging as an essential area of research.