* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Download 人工智能 - Lu Jiaheng's homepage
Catastrophic interference wikipedia , lookup
Ecological interface design wikipedia , lookup
Technological singularity wikipedia , lookup
Artificial intelligence in video games wikipedia , lookup
Convolutional neural network wikipedia , lookup
Computer vision wikipedia , lookup
Personal knowledge base wikipedia , lookup
Computer Go wikipedia , lookup
Adaptive collaborative control wikipedia , lookup
Wizard of Oz experiment wikipedia , lookup
Human–computer interaction wikipedia , lookup
Embodied cognitive science wikipedia , lookup
Intelligence explosion wikipedia , lookup
Existential risk from artificial general intelligence wikipedia , lookup
Knowledge representation and reasoning wikipedia , lookup
Ethics of artificial intelligence wikipedia , lookup
计算机科学概述 Introduction to Computer Science 陆嘉恒 中国人民大学 信息学院 www.jiahenglu.net Artificial Intelligence (人工智能) Objectives In this class, you will learn about • What is artificial intelligence • Knowledge representation • Recognition tasks • Reasoning tasks • Robotics Introduction to Artificial Intelligence • What is intelligence? – The capacity to acquire and apply knowledge. – The faculty of thought and reason. – The ability to learn or understand or to deal with new or trying situations. Major Subdivisions of AI • Understanding • Thinking • Acting AI: Understanding • Computer Vision – understanding what you see AI: Thinking • Capturing Structure and Reaching Goals – Machine Learning – Planning – Clustering AI: Acting • Robotics Consider AI use in one company Search P a g eR a n kâ H “ € u g eG ra p hS e a rc hP ro b le m Sponsered Links S p o n s e re dA d s :R e le v a n c ea n dM o n e y Google News C lu s te rC o m m o nA rtic le s Google maps S e a rc hP ro b le m Introduction • Turing test – A test for intelligent behavior of machines – Allows a human being to interrogate two entities, both hidden from the interrogator • A human being • A machine (a computer) The Turing Test Introduction (continued) • Turing test (continued) – If the interrogator is unable to determine which entity is the human being and which is the computer, the computer has passed the test • Artificial intelligence can be thought of as constructing computer models of human intelligence A Division of Labor • Categories of tasks – Computational tasks – Recognition tasks – Reasoning tasks • Computational tasks – Tasks for which algorithmic solutions exist – Computers are better (faster and more accurate) than human beings A Division of Labor (continued) • Recognition tasks – Sensory/recognition/motor-skills tasks – Human beings are better than computers • Reasoning tasks – Require a large amount of knowledge – Human beings are far better than computers Figure 14.2 Human and Computer Capabilities Knowledge Representation • Knowledge: A body of facts or truths • For a computer to make use of knowledge, it must be stored within the computer in some form Knowledge Representation (continued) • Knowledge representation schemes – Natural language – Formal language – Pictorial – Graphical Knowledge Representation (continued) • Required characteristics of a knowledge representation scheme – Adequacy – Efficiency – Extendability – Appropriateness Recognition Tasks • A neuron is a cell in the brain capable of – Receiving stimuli from other neurons through its dendrites – Sending stimuli to other neurons through its axon Figure 14.4 A Neuron Recognition Tasks (continued) • If the sum of activating and inhibiting stimuli received by a neuron equals or exceeds its threshold value, the neuron sends out its own signal • Each neuron can be thought of as an extremely simple computational device with a single on/off output Recognition Tasks (continued) • Human brain: A connectionist architecture – A large number of simple “processors” with multiple interconnections • Von Neumann architecture – A small number (maybe only one) of very powerful processors with a limited number of interconnections between them Recognition Tasks (continued) • Artificial neural networks (neural networks) – Simulate individual neurons in hardware – Connect them in a massively parallel network of simple devices that act somewhat like biological neurons • The effect of a neural network may be simulated in software on a sequentialprocessing computer Recognition Tasks (continued) • Neural network – Each neuron has a threshold value – Incoming lines carry weights that represent stimuli – The neuron fires when the sum of the incoming weights equals or exceeds its threshold value • A neural network can be built to represent the exclusive OR, or XOR, operation Figure 14.5 One Neuron with Three Inputs Figure 14.8 The Truth Table for XOR Recognition Tasks (continued) • Neural network – Both the knowledge representation and “programming” are stored as weights of the connections and thresholds of the neurons – The network can learn from experience by modifying the weights on its connections Reasoning Tasks • Human reasoning requires the ability to draw on a large body of facts and past experience to come to a conclusion • Artificial intelligence specialists try to get computers to emulate this characteristic Intelligent Searching • State-space graph – After any one node has been searched, there are a huge number of next choices to try – There is no algorithm to dictate the next choice • State-space search – Finds a solution path through a state-space graph Figure 14.12 A State-Space Graph with Exponential Growth Intelligent Searching (continued) • Each node represents a problem state • Goal state: The state we are trying to reach • Intelligent searching applies some heuristic (or an educated guess) to – Evaluate the differences between the present state and the goal state – Move to a new state that minimizes those differences Swarm Intelligence • Swarm intelligence – Models the behavior of a colony of ants • Swarm intelligence model – Uses simple agents that • Operate independently • Can sense certain aspects of their environment • Can change their environment • May “evolve” and acquire additional capabilities over time Intelligent Agents • An intelligent agent: Software that interacts collaboratively with a user • Initially an intelligent agent simply follows user commands Intelligent Agents (continued) • Over time – Agent initiates communication, takes action, and performs tasks on its own using its knowledge of the user’s needs and preferences Expert Systems • Rule-based systems – Also called expert systems or knowledgebased systems – Attempt to mimic the human ability to engage pertinent facts and combine them in a logical way to reach some conclusion Expert Systems (continued) • A rule-based system must contain – A knowledge base: Set of facts about subject matter – An inference engine: Mechanism for selecting relevant facts and for reasoning from them in a logical way • Many rule-based systems also contain – An explanation facility: Allows user to see assertions and rules used in arriving at a conclusion Expert Systems (continued) • A fact can be – A simple assertion – A rule: A statement of the form if . . . then . . . • Modus ponens (method of assertion) – The reasoning process used by the inference engine Expert Systems (continued) • Inference engines can proceed through – Forward chaining – Backward chaining • Forward chaining – Begins with assertions and tries to match those assertions to “if” clauses of rules, thereby generating new assertions Expert Systems (continued) • Backward chaining – Begins with a proposed conclusion • Tries to match it with the “then” clauses of rules – Then looks at the corresponding “if” clauses • Tries to match those with assertions or with the “then” clauses of other rules Expert Systems (continued) • A rule-based system is built through a process called knowledge engineering – Builder of system acquires information for knowledge base from experts in the domain Robotics • Robot: Device that can gather sensory information autonomously • Many uses for robots (auto manufacturing, bomb disposal, exploration, microsurgery) • Deliberative strategy: Robot has an internal representation of its environment • Reactive strategy: Uses heuristic algorithms to allow robot to respond directly to environment Summary • Artificial intelligence explores techniques for incorporating aspects of intelligence into computer systems • Categories of tasks: Computational tasks, recognition tasks, reasoning tasks • Neural networks simulate individual neurons in hardware and connect them in a massively parallel network Summary (continued) • Swarm intelligence models the behavior of a colony of ants • Intelligent agent interacts with a user • Rule-based systems attempt to mimic the human ability to engage pertinent facts and combine them in a logical way to reach some conclusion • Robots can perform many useful tasks Conclusions • • • • AI is big business Still can't do most things What it can do it does extremely well Major Subdivision of AI – vision and language – robotics – machine learning