Artificial Intelligence An Introduction 1
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1 Artificial Intelligence An Introduction 1 Instructor: Dr. B. John Oommen Chancellor s Professor Fellow: IEEE; Fellow: IAPR School of Computer Science, Carleton University, Canada. 1 The primary source of these notes are the slides of Professor Hwee Tou Ng from Singapore. I sincerely thank him for this. 1/29
2 Leibniz, Babbage, Boole, Frege, Russell, Tarski... Turing (1930 s) Turing Machine (TM) Turing Test Operationalizing Intelligence Machine s ability to demonstrate intelligence Human Judge converses with human and machine BOTH try to appear human All participants are placed in isolated locations If Judge cannot reliably tell the machine from the human, the machine Passes the test The Test itself Turing-Church Thesis: If a problem is not solvable by a TM, it is not solvable by people either 2/29
3 Leibniz, Babbage, Boole, Frege, Russell, Tarski... Turing (1930 s) Turing Machine (TM) Turing Test Operationalizing Intelligence Machine s ability to demonstrate intelligence Human Judge converses with human and machine BOTH try to appear human All participants are placed in isolated locations If Judge cannot reliably tell the machine from the human, the machine Passes the test The Test itself Turing-Church Thesis: If a problem is not solvable by a TM, it is not solvable by people either 2/29
4 Leibniz, Babbage, Boole, Frege, Russell, Tarski... Turing (1930 s) Turing Machine (TM) Turing Test Operationalizing Intelligence Machine s ability to demonstrate intelligence Human Judge converses with human and machine BOTH try to appear human All participants are placed in isolated locations If Judge cannot reliably tell the machine from the human, the machine Passes the test The Test itself Turing-Church Thesis: If a problem is not solvable by a TM, it is not solvable by people either 2/29
5 Leibniz, Babbage, Boole, Frege, Russell, Tarski... Turing (1930 s) Turing Machine (TM) Turing Test Operationalizing Intelligence Machine s ability to demonstrate intelligence Human Judge converses with human and machine BOTH try to appear human All participants are placed in isolated locations If Judge cannot reliably tell the machine from the human, the machine Passes the test The Test itself Turing-Church Thesis: If a problem is not solvable by a TM, it is not solvable by people either 2/29
6 3/29 : 1940 s 1940s: McCulloch-Pitts, Wiener, Ashby Neuron models Cybernetics - Feedback Teleological behavior Study of design and purpose All things to be designed for or directed toward a final result There is an inherent purpose or final cause for all that exists Homeostat Device built by Ashby in 1948 Adaptive ultrastable system from four bomb control units Had inputs and feedback Used magnetically-driven water-filled potentiometers Stabilizes effects of disturbances introduced into the system Time: Closest thing to a synthetic brain... designed by man
7 3/29 : 1940 s 1940s: McCulloch-Pitts, Wiener, Ashby Neuron models Cybernetics - Feedback Teleological behavior Study of design and purpose All things to be designed for or directed toward a final result There is an inherent purpose or final cause for all that exists Homeostat Device built by Ashby in 1948 Adaptive ultrastable system from four bomb control units Had inputs and feedback Used magnetically-driven water-filled potentiometers Stabilizes effects of disturbances introduced into the system Time: Closest thing to a synthetic brain... designed by man
8 3/29 : 1940 s 1940s: McCulloch-Pitts, Wiener, Ashby Neuron models Cybernetics - Feedback Teleological behavior Study of design and purpose All things to be designed for or directed toward a final result There is an inherent purpose or final cause for all that exists Homeostat Device built by Ashby in 1948 Adaptive ultrastable system from four bomb control units Had inputs and feedback Used magnetically-driven water-filled potentiometers Stabilizes effects of disturbances introduced into the system Time: Closest thing to a synthetic brain... designed by man
9 4/29 : 1940 s 1940s: Walter, von Neumann Machina Speculatrix (Elmer 1948, and Elsie 1949) First electronic autonomous robots Rich connections between a small number of brain cells - Very complex behaviors Described as tortoises due to their shape and slow motion Taught us about the secrets of organization and life Three-wheeled tortoise robots Could find their way to a recharging station Self-reproducing automata Self-replication: Process by which a thing copies of itself Self-reproductive systems:produce copies of themselves Primitives: From metal bar and wire Self-assembling systems Assemble copies of themselves from finished parts Self-reproducing computer programs
10 4/29 : 1940 s 1940s: Walter, von Neumann Machina Speculatrix (Elmer 1948, and Elsie 1949) First electronic autonomous robots Rich connections between a small number of brain cells - Very complex behaviors Described as tortoises due to their shape and slow motion Taught us about the secrets of organization and life Three-wheeled tortoise robots Could find their way to a recharging station Self-reproducing automata Self-replication: Process by which a thing copies of itself Self-reproductive systems:produce copies of themselves Primitives: From metal bar and wire Self-assembling systems Assemble copies of themselves from finished parts Self-reproducing computer programs
11 5/29 : 1950 s 1950s: Simon, Newell, McCarthy, Minsky: AI (1956) Fundamentals of Classification Neural networks Perceptron
12 6/29 : Since 1960 s 1960s: Lisp, Adaline, Fuzzy sets (Zadeh 65) 1960s: General Problem Solver (GPS), Logic Theory 1970s: Backpropogation, Fuzzy Controllers 1970s: Knowledge Engineering, Genetic Algorithms (GA) 1970s: Production systems, Expert systems 1970s: Natural Language Processing (NLP) SHRDLU SHRDLU was an early NLP developed by Winograd at MIT Micro Planner and Lisp programming language on a PDP-6 SHRDLU was derived from ETAOIN SHRDLU Arrangement of the alpha keys on a Linotype machine in descending frequency order
13 6/29 : Since 1960 s 1960s: Lisp, Adaline, Fuzzy sets (Zadeh 65) 1960s: General Problem Solver (GPS), Logic Theory 1970s: Backpropogation, Fuzzy Controllers 1970s: Knowledge Engineering, Genetic Algorithms (GA) 1970s: Production systems, Expert systems 1970s: Natural Language Processing (NLP) SHRDLU SHRDLU was an early NLP developed by Winograd at MIT Micro Planner and Lisp programming language on a PDP-6 SHRDLU was derived from ETAOIN SHRDLU Arrangement of the alpha keys on a Linotype machine in descending frequency order
14 7/29 : 1970 s & 1980 s 1970s: Theorem proving, Planning 1980s: NN / Connectionist boom, Boltzmann Machine 1980s: Knowledge Representation (KR) 1980s: More semantics in NLP (Conceptual Dependency) 1980s: Symbolic Machine Learning (ML)
15 8/29 : Since 1990 s More NN Subsumption Architecture (Brooks) Decompose complicated intelligent behaviour Many simple behaviour modules organized into layers Each layer implements a particular goal Higher layers are increasingly abstract A robot s layers: Lowest layer could be avoid an object On top of it would be the layer wander around Which in turn lies under explore the world Uses a bottom-up design
16 8/29 : Since 1990 s More NN Subsumption Architecture (Brooks) Decompose complicated intelligent behaviour Many simple behaviour modules organized into layers Each layer implements a particular goal Higher layers are increasingly abstract A robot s layers: Lowest layer could be avoid an object On top of it would be the layer wander around Which in turn lies under explore the world Uses a bottom-up design
17 9/29 : Since 1990 s Reinforcement Learning Bayesian Belief Nets Data Mining More NN, More GA, GP, Artificial-Life More GAs, Genetic Programming (GP), Artificial-Life Bottom-up or behavior-based AI vs Top-down AI Emergent Computing, Swarm Intelligence Self-Organization...
18 9/29 : Since 1990 s Reinforcement Learning Bayesian Belief Nets Data Mining More NN, More GA, GP, Artificial-Life More GAs, Genetic Programming (GP), Artificial-Life Bottom-up or behavior-based AI vs Top-down AI Emergent Computing, Swarm Intelligence Self-Organization...
19 10/29 What is Intelligence Intelligence is: Intellectual (?) behavior that we admire But don t understand Intelligence is manifested in behavior Closely related to surviving in a complex world Or...
20 11/29 2 kinds of AI (or 3 or 4) Engineering vs Cognitive Science Making usefully smart machines, somehow: Expert systems; Deep Blue; some Data Mining Understanding how minds work AI to express and test psychological/linguistic etc. theories
21 12/29 Classical/Top-down / Symbolic vs Behavior-based / Bottom-up / Subsymbolic Mind vs Brain Physical symbol system hypothesis Hi-level approach is brittle Bottom-up approach often unimpressive Scruffies vs Neats
22 12/29 Classical/Top-down / Symbolic vs Behavior-based / Bottom-up / Subsymbolic Mind vs Brain Physical symbol system hypothesis Hi-level approach is brittle Bottom-up approach often unimpressive Scruffies vs Neats
23 13/29 Weak AI vs Strong AI Chinese Room argument (John Searle) No such things as AI... An experiment: Someone who knows only English Sits alone in a room following English instructions for manipulating strings of Chinese characters To those outside the room it appears as if someone in the room understands Chinese. Shows that while computers may appear to converse in natural language, they cannot even in principle. Searle argues that computers merely use syntactic rules to manipulate symbol strings Have no understanding of meaning or semantics.
24 14/29 AI: All about Tradeoffs Theoretical insights in AI: Concern tradeoffs Tradeoffs: Efficiency and Generality Tradeoffs: Robustness and Power Tradeoffs: Design complexity - Ability to degrade gracefully Tradeoffs: Prior cooking and Achievement Tradeoffs: Memory and Inference Above All Tradeoffs: Memory and Time
25 14/29 AI: All about Tradeoffs Theoretical insights in AI: Concern tradeoffs Tradeoffs: Efficiency and Generality Tradeoffs: Robustness and Power Tradeoffs: Design complexity - Ability to degrade gracefully Tradeoffs: Prior cooking and Achievement Tradeoffs: Memory and Inference Above All Tradeoffs: Memory and Time
26 14/29 AI: All about Tradeoffs Theoretical insights in AI: Concern tradeoffs Tradeoffs: Efficiency and Generality Tradeoffs: Robustness and Power Tradeoffs: Design complexity - Ability to degrade gracefully Tradeoffs: Prior cooking and Achievement Tradeoffs: Memory and Inference Above All Tradeoffs: Memory and Time
27 14/29 AI: All about Tradeoffs Theoretical insights in AI: Concern tradeoffs Tradeoffs: Efficiency and Generality Tradeoffs: Robustness and Power Tradeoffs: Design complexity - Ability to degrade gracefully Tradeoffs: Prior cooking and Achievement Tradeoffs: Memory and Inference Above All Tradeoffs: Memory and Time
28 14/29 AI: All about Tradeoffs Theoretical insights in AI: Concern tradeoffs Tradeoffs: Efficiency and Generality Tradeoffs: Robustness and Power Tradeoffs: Design complexity - Ability to degrade gracefully Tradeoffs: Prior cooking and Achievement Tradeoffs: Memory and Inference Above All Tradeoffs: Memory and Time
29 14/29 AI: All about Tradeoffs Theoretical insights in AI: Concern tradeoffs Tradeoffs: Efficiency and Generality Tradeoffs: Robustness and Power Tradeoffs: Design complexity - Ability to degrade gracefully Tradeoffs: Prior cooking and Achievement Tradeoffs: Memory and Inference Above All Tradeoffs: Memory and Time
30 15/29 AI must bescruffy... Neatness is impossible in complex domains Complex domains: Structure that requires solutions Found by exploring branching paths in a search space No. of branches is exponential function of path depth Any intelligent agent needs to find tricks and shortcuts Even in formally specified domains! Unless: Infinitely large and fast computers Good shortcuts cannot be worked out in advance They are not perfect - even in mathematics Shortcuts & laziness: Go hand in hand... Key to intelligence (Gauss )
31 15/29 AI must bescruffy... Neatness is impossible in complex domains Complex domains: Structure that requires solutions Found by exploring branching paths in a search space No. of branches is exponential function of path depth Any intelligent agent needs to find tricks and shortcuts Even in formally specified domains! Unless: Infinitely large and fast computers Good shortcuts cannot be worked out in advance They are not perfect - even in mathematics Shortcuts & laziness: Go hand in hand... Key to intelligence (Gauss )
32 15/29 AI must bescruffy... Neatness is impossible in complex domains Complex domains: Structure that requires solutions Found by exploring branching paths in a search space No. of branches is exponential function of path depth Any intelligent agent needs to find tricks and shortcuts Even in formally specified domains! Unless: Infinitely large and fast computers Good shortcuts cannot be worked out in advance They are not perfect - even in mathematics Shortcuts & laziness: Go hand in hand... Key to intelligence (Gauss )
33 16/29 The Real World is even Harder Lack of complete initial information Range of things to do is large (branching factor!) Search spaces are huge Things happen fast There are deadlines Rapidly accessible and executable heuristics Must be learned by trial and error (for example) Such heuristic rules are bound to be fallible Overgeneralization Poor observations, weak sensors Errors in measurement Inadequate concepts Noise, environmental variance etc...
34 16/29 The Real World is even Harder Lack of complete initial information Range of things to do is large (branching factor!) Search spaces are huge Things happen fast There are deadlines Rapidly accessible and executable heuristics Must be learned by trial and error (for example) Such heuristic rules are bound to be fallible Overgeneralization Poor observations, weak sensors Errors in measurement Inadequate concepts Noise, environmental variance etc...
35 16/29 The Real World is even Harder Lack of complete initial information Range of things to do is large (branching factor!) Search spaces are huge Things happen fast There are deadlines Rapidly accessible and executable heuristics Must be learned by trial and error (for example) Such heuristic rules are bound to be fallible Overgeneralization Poor observations, weak sensors Errors in measurement Inadequate concepts Noise, environmental variance etc...
36 17/29 Problems with Heuristics Rules and facts should be consistent Consistency is undecidable (Approximate) Consistency checking is explosive Maintaining consistency also explosive To revise a belief, you need Fallible heuristics Allow for finding related beliefs Identifying and retracting underlying assumptions etc. A huge reason maintenance system won t do
37 17/29 Problems with Heuristics Rules and facts should be consistent Consistency is undecidable (Approximate) Consistency checking is explosive Maintaining consistency also explosive To revise a belief, you need Fallible heuristics Allow for finding related beliefs Identifying and retracting underlying assumptions etc. A huge reason maintenance system won t do
38 17/29 Problems with Heuristics Rules and facts should be consistent Consistency is undecidable (Approximate) Consistency checking is explosive Maintaining consistency also explosive To revise a belief, you need Fallible heuristics Allow for finding related beliefs Identifying and retracting underlying assumptions etc. A huge reason maintenance system won t do
39 18/29 Semantics is Scruffy too Conceptual schemes: Open-ended Unlike formal languages There is no formal, recursive semantics for NL: We don t know the extension-assigning functions! Concepts: May be indeterminate, vague, or ambiguous Prompt conceptual innovations Empirical concepts: No crisp necess./suff. conditions Many concepts are theoretical
40 18/29 Semantics is Scruffy too Conceptual schemes: Open-ended Unlike formal languages There is no formal, recursive semantics for NL: We don t know the extension-assigning functions! Concepts: May be indeterminate, vague, or ambiguous Prompt conceptual innovations Empirical concepts: No crisp necess./suff. conditions Many concepts are theoretical
41 19/29 is Inevitable Inevitable for any resource-limited being! No practical strategy to reduce scruffiness works always AI must be scruffy, for neat reasons Thus: Study what the history has come up with Of course: Theories about such inevitably scruffy systems As neat as possible (maximally falsifiable etc.!!)
42 19/29 is Inevitable Inevitable for any resource-limited being! No practical strategy to reduce scruffiness works always AI must be scruffy, for neat reasons Thus: Study what the history has come up with Of course: Theories about such inevitably scruffy systems As neat as possible (maximally falsifiable etc.!!)
43 20/29 By the Way Nearly anything you want to compute you can t!! Because there are countably many Turing machines But Uncountably many functions The interesting things you can compute Too expensive to compute So, you can t compute them Exponential worst case run-time functions T(n) = kc n e.g. 1 input item takes 10 7 sec, n=50, complexity is 2 n : years Biological systems must use approximate solutions Learning: On-line regularity detection for prediction Experimentation and mental simulation To be adaptable, an organism must be suboptimal (Gould)
44 20/29 By the Way Nearly anything you want to compute you can t!! Because there are countably many Turing machines But Uncountably many functions The interesting things you can compute Too expensive to compute So, you can t compute them Exponential worst case run-time functions T(n) = kc n e.g. 1 input item takes 10 7 sec, n=50, complexity is 2 n : years Biological systems must use approximate solutions Learning: On-line regularity detection for prediction Experimentation and mental simulation To be adaptable, an organism must be suboptimal (Gould)
45 20/29 By the Way Nearly anything you want to compute you can t!! Because there are countably many Turing machines But Uncountably many functions The interesting things you can compute Too expensive to compute So, you can t compute them Exponential worst case run-time functions T(n) = kc n e.g. 1 input item takes 10 7 sec, n=50, complexity is 2 n : years Biological systems must use approximate solutions Learning: On-line regularity detection for prediction Experimentation and mental simulation To be adaptable, an organism must be suboptimal (Gould)
46 20/29 By the Way Nearly anything you want to compute you can t!! Because there are countably many Turing machines But Uncountably many functions The interesting things you can compute Too expensive to compute So, you can t compute them Exponential worst case run-time functions T(n) = kc n e.g. 1 input item takes 10 7 sec, n=50, complexity is 2 n : years Biological systems must use approximate solutions Learning: On-line regularity detection for prediction Experimentation and mental simulation To be adaptable, an organism must be suboptimal (Gould)
47 21/29 Many fields have contributed to AI In the form of ideas, viewpoints and techniques Philosophy: Logic, reasoning, mind as a physical system Mathematics: Formal representation and proofs Mathematics: Computation, (un)decidability, (in)tractability Mathematics: Probability, fuzzy theory Psychology: Learning, perception, motor control Economics: Theory of rational decisions, game theory
48 22/29 Other fields that have contributed to AI: Linguistics: Knowledge representation, grammar Neuroscience: Physical substrate for mental activities Biology: Adaptation, evolution of complex systems Controls: Homeostatic systems, stability, optimal agents Complex Systems Theory etc. etc. etc...
49 23/29 AI Systems Think like humans Cognitive modelling (AI + Psychology) Act like humans Turing test approach: needs NLP, KR, ML,... Think rationally First-Order-Logic based problem solving and planning Closely related to automated theorem proving Act rationally A rational agent acts so as to achieve its goals Given its beliefs & limited rationality Autonomous agents, robots, evolutionary computation
50 24/29 Reasoning and Knowledge Goal of AI... Heuristic search Problem solving, planning, game playing Theorem proving Knowledge-based (KB) systems Knowledge Engineering (KE); Knowledge Representation (KR); Expert systems Natural Language Processing (NLP) Story understanding Speech recognition Question answering
51 24/29 Reasoning and Knowledge Goal of AI... Heuristic search Problem solving, planning, game playing Theorem proving Knowledge-based (KB) systems Knowledge Engineering (KE); Knowledge Representation (KR); Expert systems Natural Language Processing (NLP) Story understanding Speech recognition Question answering
52 24/29 Reasoning and Knowledge Goal of AI... Heuristic search Problem solving, planning, game playing Theorem proving Knowledge-based (KB) systems Knowledge Engineering (KE); Knowledge Representation (KR); Expert systems Natural Language Processing (NLP) Story understanding Speech recognition Question answering
53 25/29 Reasoning and Knowledge Goal of AI... Perception Vision Robotics Machine Learning Pattern Recognition cog sci AI psych linguistics
54 26/29 Reasoning and Knowledge Goal of AI Intelligence is (?) Reasoning + Knowledge Reasoning Universal inference methods Weak methods, e.g. hill climbing Domain-independent search through symbolic state spaces Problem-solving/planning theorem proving - first principles Knowledge Universal methods combinatorial explosion Strong methods: Heuristics Domain-dependent knowledge Shallow deductions... Expert systems
55 26/29 Reasoning and Knowledge Goal of AI Intelligence is (?) Reasoning + Knowledge Reasoning Universal inference methods Weak methods, e.g. hill climbing Domain-independent search through symbolic state spaces Problem-solving/planning theorem proving - first principles Knowledge Universal methods combinatorial explosion Strong methods: Heuristics Domain-dependent knowledge Shallow deductions... Expert systems
56 Reasoning and Knowledge Goal of AI Goal of AI Build a person / animal Vision... NL Search Deduction Learning... Robotics... Speech Internal Representation Internal Representation Internal representation: Not NL All representations inter-translatable Unambiguous, explicit referents, only gist remembered Support inferences 27/29
57 28/29 Reasoning and Knowledge Goal of AI Why is AI not just Learning? Experience State Performance Learning Plan Libraries Grammar Rules etc. Planning Parsing etc. Characterize what it is that is to be learned!
58 29/29 Reasoning and Knowledge Goal of AI To learn anything you should already know a lot Without strong clues of domain, nothing is learned There are many kinds of learning...
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