CNS 4470 Artificial Intelligence

What is AI? No really what is it? No really what is it?

Acting Humanly: The Turing Test Allen Turing (1950) predicted: Allen Turing (1950) predicted: –By 2000, a machine might have a 30% chance of fooling a lay person for 5 minutes –He was nearly right. True if you restrict the domain to something like Law. True if you restrict the domain to something like Law. –Suggested major components of AI: knowledge, reasoning, Language understanding and learning. knowledge, reasoning, Language understanding and learning.

Thinking Humanly: Cognitive Science Requires scientific theories of internal activities of the brain Requires scientific theories of internal activities of the brain –What level of abstraction? Knowledge vs. Circuits Knowledge vs. Circuits –How to validate: requires Predicting and testing behavior of human subjects Predicting and testing behavior of human subjects Direct identification from neurological data Direct identification from neurological data Now separate sciences from AI Now separate sciences from AI

Thinking rationally: Laws of Thought Greek philosophy to present day math and philosophy have prescribed “right-thinking” Greek philosophy to present day math and philosophy have prescribed “right-thinking” But… But… –Not all intelligent behavior requires logical deliberation –How do I really know what I should be thinking Right Now!

Acting Rationally Rational behavior: Do the right thing Rational behavior: Do the right thing The right thing: The right thing: –Maximize goal achievement –Given available information Not necessarily involve “thinking” Not necessarily involve “thinking” –Reflex –Detailed Planning

History –McCulloch and Pitts –Donald Hebb –Hebbian Learning –Refuted by Marvin Minsky

History Birth – 1956 Birth – 1956 –John McCarthy workshop at Dartmouth –Allen Newell –Herb Simon Logic Theorist Logic Theorist Solved Mind – body problem Solved Mind – body problem –Showed that AI was only field to attempt to build machines that would function autonomously in complex, changing environments

History 1952 – – 1969 –“Look mom no hands” –General Problem Solver –Lisp –Analogy geometry solving program geometry solving program –Perceptrons

History Dose of reality Dose of reality –1966 – 1973 –No world Chess champion –No translator between Russian and English Spirit is willing but the flesh is weak Spirit is willing but the flesh is weak the vodka is good but the meat is rotten the vodka is good but the meat is rotten –Summarized as Failure to come to grips with the “combinatorial explosion”

History Expert Systems Expert Systems –Dendral Reasoning about chemistry Reasoning about chemistry –Mycin Diagnose blood infections Diagnose blood infections

History Present Present AI becomes Science AI becomes Science –AI no longer separated from the rest of computer science –Ex. Machine learning not separated from information theory Examples Examples –Backprop Neural Networks –Hidden Markov Models –Bayesian Networks

Rational Agents Agent Agent –Perceives –Acts Function Function –Precepts --> Actions Seek agent with best performance Seek agent with best performance Or given computational constraints Or given computational constraints –Best program, given machine resources

Rational Agents Not always Not always –Omniscient –Clairvoyant –Successful Just Act Reasonably given the information at hand Just Act Reasonably given the information at hand

Computer-Human Comparison 1CPU 10^10 bits RAM 10^12 bits disk 10^9 cycles/sec 10^10 bits/sec 10^9 updates/sec 10^11 neurons 10^14 synapses 10^ 3 cycles/sec 10^14 bits/sec 10^14 updates/sec

State of the Art Autonomous Planning and Scheduling Autonomous Planning and Scheduling –Remote Agent: Actual Scheduling of operations of a NASA Spacecraft Game playing Game playing –IBM’s Deep Blue defeated the world champion Autonomous control Autonomous control –ALVINN: Controlled a minivan across the US 98% of the time. Diagnosis Diagnosis –Expert physician diagnosis: Medical expert scoffs then later agrees with the diagnosis

State of the Art Logistics Planning Logistics Planning –DART: Dynamic Analysis and Re-planning Tool: 50,000 vehicles, cargo and people Robotics Robotics –Robotic micro-surgery assistants: three-dimensional model of internal anatomy & guided insertion of artificial hip Language Understanding and Problem Solving Language Understanding and Problem Solving –Proverb: Solves crossword puzzles better than most humans

Setting for Intelligent Design P.A.G.E. P.A.G.E. Percepts Percepts Actions Actions Goals Goals Environment Environment

Types of Agents Reflex Agents Reflex Agents –Sensors –Rules -> Actions Reflex Agents with State Reflex Agents with State –Add the ability to know how actions effect the current state Goal-Based Agents Goal-Based Agents –Add the Ability to know how actions effect future states Utility-Based Agents Utility-Based Agents –Add the Ability to know how happy it will be under various goal conditions

Performance Measure Objective measure of Agent’s performance Objective measure of Agent’s performance –Specified by the Designer –Connected to the Agent’s Goal Intelligent Agent Intelligent Agent –Maximizes the Performance Measure Given available resources

Environments Fully observable vs. partially observable Fully observable vs. partially observable Deterministic vs. Stochastic Deterministic vs. Stochastic Episodic vs. sequential Episodic vs. sequential Static vs. Dynamic Static vs. Dynamic Discrete vs. Continuous Discrete vs. Continuous Single agent vs. multi-agent. Single agent vs. multi-agent.